The Art of Streetplay

Poking Holes in Bogle's Pro-Cap Weighting Rationale

2006-06-27T19:39:00.000-07:00

Surprise surprise... John Bogle is putting down fundamental indexing in favor of, you guessed it, what has made him rich-- cap weighted passive indexing. And he got Burton Malkiel to back him up and give him a sense of credibility-- not too different from WisdomTree getting Siegel on board. Must be right if they've got an academic on board!

I thought I'd hone in on a few aspects of his argument and then share some personal thoughts.

Aspect #1:
"First let us put to rest the canard that the remarkable success of traditional market-weighted indexing rests on the notion that markets must be efficient. Even if our stock markets were inefficient, capitalization-weighted indexing would still be -- must be -- an optimal investment strategy. All the stocks in the market must be held by someone. Thus, investors as a whole must earn the market return when that return is measured by a capitalization-weighted total stock market index. We can not live in Garrison Keillor's Lake Wobegon, where all the children are above average. For every investor who outperforms the market, there must be another investor who underperforms. Beating the market, in principle, must be a zero-sum game."

This does make some sense-- it is true that (1) the market return from one instant to the next is, technically, the return generated by a capitalization-weighted total stock market index. It is also true that (2) beating the "market" is a zero-sum game. If one makes the claim (as they did) that the market is inefficient, though, the flaw in the logic above is that claims (1) and (2) imply the market must be the an optimal investment strategy. If, for whatever reason, there are at times deviations from intrinsic value, and one is able to, probabilistically or otherwise, construct a strategy which takes advantage of mean reversion of stocks to their intrinsic value over time, then one could theoretically outperform the market (with a few more conditions). Because beating the market return is zero-sum, yes, you would be earning a profit at the expense of another market participant, and yes, it is notoriously difficult to beat the market after fees over time. The only thing I am saying here is it is a logical fallacy to say that given (1) and (2) are true, then even if the market is inefficient, cap weighting must be an optimal investment strategy-- it is NOT a logical fallacy, however, to make the deduction that cap weighting MAY be an optimal investment strategy.

Aspect #2: Expenses-- Management Fees, Turnover, Taxes (Capacity)
"Purveyors of fundamentally weighted indexes also tend to charge management fees well above the typical index fund. While index funds also incur expenses, they are available at costs below 10 basis points. The expense ratios of publicly available fundamental index funds range from an average of 0.49% (plus brokerage commissions) to 1.14% (plus a 3.75% sales load), plus an undisclosed amount of portfolio turnover costs."

"Fundamental weighting also fails to provide the tax efficiency of market weighting."

Later in the article they delve into some of the conditions which would allow one to make the claim that investing in cap weighted indexes is the optimal investment strategy. First off I would say that again, they are definitely right. Cap weighting has a bunch of natural advantages. They are easy to construct, they require no turnover and hence no transaction cost and no manager who takes a fee for himself, and they are tax efficient.

The question I ask again is, does this lead to the natural deduction that cap weighting MUST be the optimal strategy? Perhaps I'm wrong, but I don't believe so. What it implies to me is the crux of the argument for active management-- as one deviates from investing in the market to increase your portfolio's allocation to a security you believe to be mispriced, you run up against a number of frictions: (1) all the time you are spending to figure out whether that security is, in fact, mispriced-- the cost of time (which you may or may not outsource to a money manager for a fee); (2) transaction costs to invest in that security; (3) tax inefficiencies; (4) other (ie. market impact, etc). These are real costs.

So the fundamental question is: in the face of probabilistic inefficiency (which is all Arnott and Siegel claim), is "market noise" of a large enough magnitude and does it mean revert quickly enough for it to be worthwhile to incur the incremental costs necessary to generate those returns?

I am not saying anything which hasn't been said a million times in all likelihood. Arnott's paper in the FAJ allocated large chunks of space to the time series of the returns relative to the market returns, how "market-like" those returns were, what capacity was available to the trading strategy, the effect of the trading strategy on volatility, and the incremental costs involved assuming turnover at a certain rate.

In other words, he was making an apples to apples after-transaction-cost comparison between his strategy and the market return, and he found that his trading strategy outperformed over time robustly enough that the probability of overfitting was minimal. That is pretty valid-- this article applied no such rigor. I don't blame it (this is the WSJ we're talking about), but nevertheless, it does not conclusively disprove the assertions made in Arnott's paper.

Aspect #3: Increase in Efficiency
"We concede that there is some evidence, based on numbers compiled by Ibbotson Associates, that long-run excess returns have been earned from dividend-paying, "value" and small-cap stocks -- albeit returns that are overstated by not taking into account management fees, operating expenses, turnover costs and taxes. But to the extent that investors are persuaded by these data, the premiums offered by such stocks may well now have been "arbitraged away" in the stock market, as price-earnings multiples have become extremely compressed."

This is a valid point, not out of line with the logic in the fundamental question: in the face of probabilistic inefficiency (which is all Arnott and Siegel claim), is "market noise" of a large enough magnitude and does it mean revert quickly enough for it to be worthwhile to incur the incremental costs necessary to generate those returns?

It isn't enough to make the claim that the market is noisy-- the noise must be large enough and mean reverting enough. If, through the influx of hedge fund investment and everything else, the market is more efficient than it was, then perhaps even though a risk-reward favorable trading strategy existed in the past, we wouldn't see returns nearly as large going forward-- especially after fees.

I have commented on the outperformance of value of late in Commentary on The Trouble With Value, based on GMO's piece a while back. It is true- the run value has had of late is now getting long in the tooth. The outperformance of value relative to growth has averaged out to a certain level over time, and we are now well above that average. Reversion to the mean would imply value may have a more difficult time going forward.

Claim #4: New paradigms don't tend to last

"We never know when reversion to the mean will come to the various sectors of the stock market, but we do know that such changes in style invariably occur. Before we too easily accept that fundamental indexing -- relying on style tilts toward dividends, "value" and smallness -- is the "new paradigm," we need a longer sense of history, as well as an appreciation that capitalization-weighted indexing does not depend on efficient markets for its usefulness.

While we have witnessed many "new paradigms" over the years, none have persisted. The "concept" stocks of the Go-Go years in the 1960s came, and went. So did the "Nifty Fifty" era that soon followed. The "January Effect" of small-cap superiority came, and went. Option-income funds and "Government Plus" funds came, and went. High-tech stocks and "new economy" funds came as well, and the survivors remain far below their peaks. Intelligent investors should approach with extreme caution any claim that a "new paradigm" is here to stay. That's not the way financial markets work."

This is theoretically a slightly different, broader slant from Aspect #3. This isn't necessarily making the claim that markets have secularly gotten more efficient in general. It is more making the claim that over time, two things tend to happen at different points in time for a variety of reasons-- (1) fads develop and (2) systematic statistical patterns form. Neither persist over time, and it is so unlikely that you will be able to know when they pop or decrease to statistical insignificance that it isn't worth the costs necessary to act on the information. They give a bunch of examples of (1). I would posit an example of (2) to be the incredibly large serial autocorrelation detected in the market indices by Andy Lo. It really did exist. They pointed it out, people got excited and probably traded on it a bunch, probably made some good money, and then over time, it decreased in absolute value to the point that it is no longer profitable to trade on relative to sticking all that money in an index fund.

Fair enough. What is the "truth"? Will the magnitude of this aberration decrease over time, as more people catch on, to the point that it isn't worth the incremental costs, or not? You know, they very well could be right.

Personal Thoughts
My belief is that this is less a statistical anomaly than the Andy Lo autocorrelation phenomenon was. It is lower resolution, it takes longer to realize the abnormally positive returns, it requires patience and a smidge of contrarianism. These are all qualities which would allow it to persist longer than other phenomena would. The big irony of it all, however, is this-- if Siegel is able to convince enough investors that he is, in fact, on the right side of this debate, the influx of capital may itself cause the anomaly to disappear! This stems from one unquestionable truth-- beating the market return is a zero sum game, and the market return is the return on the cap weighted total stock market index. If the majority of investors believe they will beat the market return by investing in fundamental indexing, they will have to earn their above market return at the expense of other market participants-- but those market participants aren't anywhere to be had. Those abnormal returns exist because the "market" has allocated funds in a particular way over the history of the stock market. If the "market" were to no longer allocate funds that way, perhaps we would have the indirect benefit of an overall better functioning economic system, but directly, the market, as a whole, cannot escape the market return. If everyone believes something to be true, you cannot earn abnormal returns off of it.

The other aspect which I personally grapple with is Aspect #3. As trite as it may sound, we have seen a hell of a rally in value relative to growth. The outperformance of value is now above the mean. Has the influx of capital to professional money managers made the pricing of stocks relative to each other more efficient? If so, the returns of an investment strategy which worked when the investing landscape was not riddled with value managers may not be applicable to the world we will see over the next 20 years.

I no doubt believe that the market is noisy, as Siegel puts it. But that alone is not enough to make fundamental indexation "work". For it to "work", there needs to be sufficient noise and mean reversion to compensate for the costs incurred. From the point of view of someone today investing over the next 10 years, that is a difficult tradeoff for anyone to say definitively will go one way or the other, in my opinion.

Money Managers Have a Place in This World
A final thought is in order on the topic of market efficiency, and professional money managers. They do have a place in this world. Just think about it: if all money was invested in index funds, who would set the value of the individual stocks which comprise the S&P? We need stock pickers! More than that, they deserve compensation for providing efficiency to the price of stocks! Without individuals estimating the intrinsic value of stocks, the market system breaks down, because its whole purpose in the paradigm of the financial markets is to allow companies to raise capital efficiently. If they did not do that, there would be no need for the stock market at all.

The question is not whether they should exist or not-- the question is what is the just compensation they deserve relative to the amount of efficiency they can provide to the market.
With all this talk of index investing, I get a good feeling inside knowing I might have a place in this world-- as an allocator of efficiency capital. Great.

WisdomTree Update, June 20th 2006

2006-06-20T10:41:00.000-07:00

Needless to say, a lot has happened since my last post, and since I first started writing about WisdomTree in April 2005. The 20 ETF's have officially launched. All trade on the NYSE under a variety of tickers- DTN, DLN, ..., all of which are listed on the up and running website they now have. One of the commenters on this blog completely nailed the launch date. They have brought on board yet another BGI veteran, Bruce Lavine.

Rather than spell out everything that is easily available to the public, it might be of value to analyze what is going on one level deeper.

(1) WSDT is leveraging its star power and university environment very effectively. It has done this in 3 ways- 1) it has obtained ETF's licenses much more quickly than I thought would be possible, 2) it has gotten discount or free advertising all over the WSJ, CNBC, on the floor of the NYSE, and elsewhere, and 3) it has gotten heavily discounted research and development aid from students at the University of Pennsylvania and Wharton, through a class offered called the Wharton Field Challenge.

This cost structure really doesn't need much capex at all to fuel itself. The management team is also most likely compensating itself in a call option-type fashion than anything else. We will look at the economics later.

(2) The Expense Ratios seem low to me. The expense ratios range from 28 to 58 bps, but the "bread and butter" fund, in my opinion, seems to be the Total Dividend Fund which charges 28, and DIEFA, which charges 48. The rest are probably better looking on the backtests (as weighting to small caps increases, and as they squeeze for more yield), but I am unsure about their merits relative to what is currently in the marketplace. As Luciano, their head of research said himself, what is lacking in the marketplace today are indices which are broader, more representative indices which fill larger asset allocation needs. What is not lacking are "one-off" products that may be seen as tricky, clever attempts to game the system... but a Small Cap Dividend Fund may fall into that category itself. We will factor this into the economics later.

(3) They are 100% playing the "fundamental indexation" theme, which has been beaten to death on this blog. Siegel mentioned it in his piece in the WSJ, and it has been mentioned many other times since. As such, they are essentially piggy-backing off of a wave which really truly originated with Bob Arnott, off of which 2 companies have already put out ETF's. My hypothesis is that they started with a Dividend index, and not one of the other perhaps more "expected" fundamental metrics, because Siegel, their Director of Research, has already done quite a lot of work on dividends, which means there may be cost factors involved. In a prior post on this blog, I mentioned a study that he had done a while back, but concluded that the dividend space was too crowded for this to be a likely ETF candidate (oops). My bet is they either won't have to pay a licensing fee, or the licensing fee is greatly reduced, because Siegel can claim that this is all simply an extension of prior work that he has done, which gives him a claim on said work. If this is true, then he gets all the advertising and education benefits of the "fundamental indexation" wave-- which I am sure that he, Arnott, Steinhardt, and others in the pseudo-active ETF space now intend to drive into the heads of common investors around the globe-- without having to pay for it. If it works, maybe he can release other indices based on other fundamental metrics later.

The Economics of an Investment in WSDT

I talked about the probable cost structure for WSDT in prior posts. Most specific talk of it is in the oldest post-- basically their revenue model is the expense ratio. Barclays charges something like 70 bps on a whole bunch of its indices, while the Spiders goes down to like 12 basis points. WSDT seems to be in the middle.

So you'd make an assumption on what WSDT's weighted average expense ratio is (if the split is 50/50 domestic international flagship ETF's, that would imply 38 bps). They typically get paid Monday through Friday if PowerShares was any indication, so the cash flow is a very slow and steady function of the assets under management. They may or may not have to pay a license fee (variable cost), then they pay for listing on the NYSE (fixed and variable cost), and they pay for the traders who construct the indices which most likely track computer-generated output of what it is the portfolio should look like with say a 5% leeway. They pay transaction costs (variable cost). They will also pay for a sales force (pseudo-variable cost), through which they intend to open themselves up to new investment channels. Other than that the biggest costs are for the management team. If you look at the pedigree of their management team (many guys who were heavily involved with the launch of the BGI suite), there is no way they are getting much in cash- they are probably accepting a call option-type compensation package-- variable cost. Research is probably not expensive at all because of student help, and marketing is probably much cheaper because of their star management. Main other costs I would imagine are consumer education, maintaining a website and logistical and administrative expenses.

They are "competing" against a handful of other ETF's which already have fundamental indexation products on the market-- I've talked about many of them on my blog, but they include 2 that were put out and are paying licensing fees to Arnott as well as the products put out by Powershares, now a sub of Amvescap. I know that WSDT intends to release a bunch of other non-dividend products (isn't focusing on being a dividend ETF co.), but I would be surprised if they were to sway too far from the fundamental indexation theme (aka piggybacking Arnott).

The key swing factors from my point of view are as follows:

How much of mutual fund and hedge fund assets will end up in the hands of ETF products, synthetically or directly, when ETFs represent ~$420bn in assets, mutual funds ~$8T, and hedge funds ~$1.25T?
Will Wisdom Tree win out over the host of other ETF products attacking the same market?

The driving force behind (1) is the sad fact that 80% of mutual funds underperform the market. And by market I mean the S&P.

More broadly speaking, the driving force behind (1) is the sad fact that it is very difficult to beat the "market", period. It takes a lot of work. And when you throw hedge funds into the equation, most of the hedge funds that do consistently outperform are either lucky or are not open to new investment. Of the hedge funds that are open to new investment, a good proportion of them are probably receiving compensation that is not in line with their ability to generate risk adjusted returns. Niederhoffer's Matador fell 30+% in the month of May alone. I am sure they are suffering from redemption issues. There were a slew of other funds which have closed after the recent market weakness. And I am sure there are many other investors who are looking at these funds closing, looking at their own investments and scratching their heads at why they are paying so much themselves (200 basis points and 20% of profits) when their hedge fund investments, which were supposed to be resilient on the downside, have fallen far more than the market has.

There is nothing new in the fact that mutual funds underperform. Academic studies have been done, etc etc. It boils down to one real question-- if 80% of mutual funds underperform the market and mutual funds charge 150 basis points, and there are ETF's which have shown an ability to outperform the market over time which also have deep capacity for investment and charge a 80% less than mutual funds, the current aggregate allocation of funds may consider changing!

So there are reasonable arguments for individuals in both camps to perhaps consider ETF's in some shape or form.

I will sound crazy for proposing the numbers below, but remember that I am looking at this from a 5 year perspective. In 5 years, either the paradigm shifts, or this company is probabilistically dead. I factor in probabilistic death into the upside through a setting, at the end, of the probability that paradigm shift does not occur. Adjust the market size, costs, margins as you wish... but I would hope that the underlying model is more or less representative.

The basic calculus-- if 25% of assets in funds right now are paying excessive, tax inefficient fees with inefficient portfolio construction, and come to the realization that they are doing so over the next 5 years, and if, in that period of time, (1) companies can release the education necessary to educate the market and (2) companies can create the platforms which can provide investors easy, tax efficient access to these products, and (3) WSDT is able to get 20% of those assets, it will have around $500bn in assets under management. At 38 bps, its top line is $1.83bn. With the SPY as a guide, transaction costs are probably around 12 bps for WSDT. Licensing fees is a wildcard. Sales commission and management expenses may be another 5 bps (or $242mm, split between ~10 hotshot (greedy) managers and a salesforce of maybe 30 highly successful guys), just to throw out a number. The other costs will probably become more variable-- research maybe $1mm, listing probably cost them $200k per ETF initially plus maybe 1bp of ongoing costs, website + non-exec admin + consumer education maybe another $80mm. Because IXDP emerged from a dead company, there may be some tax benefits, so perhaps slap on a 20% tax rate. This implies recurring net profit in the upside case of around $700mm. That profit will be a bit cyclical but in general pretty high quality so lets say slap a 15 multiple on it-- market cap of $10.5bn. Its market cap right now is $314mm, implying an annualized return of 100% for 5 years.

So what is the likelihood of this happening? Assume, for a moment, that the outcome of this company is binary (probably not too far from the truth). If the probability that they meet this admittedly extremely lofty scenario is 10%. That implies the expected value of the future market cap is $1.1bn, imply an expected annualized return of 27% from here... with some serious volatility.

Any thoughts would be appreciated.

WisdomTree Update

2006-04-30T13:40:00.000-07:00

I haven't posted in a while but I believe all that has been happening at WisdomTree merits a post.

The bottom line
WisdomTree is focusing on dividend ETF's, and has filed to release 20, 6 of which are domestic and the other 14 international, based on the premise that stocks which pay dividends regularly tend to outperform the market on a risk-adjusted basis.

How this plays into things I've said in the past on this blog
I mentioned in this post that that Siegel did a dividend study, but that PowerShares and others had released more than enough different divident products. My conclusion was that it was unlikely that they would pursue dividend ETF's. Ironic then that the lion's share of their ETF's are indeed targeting dividend's-- quite a crowded space.

The driving point of this post was that they now have a nice, full bench of experienced professionals to smoothly bring them from idea to implementation. This post as well as the aformentioned one also contrasted WSDT to PowerShares in this regard. PS simply didn't have the management pedigree, even though their product offering were solid enough. Interesting to see, then, that PS was acquired by Amvescap. To me this makes some sense, although Amvescap is an interesting acquirer-- Amvescap could leverage its size to plug some of the holes that PS was unable to fill, while PS's core asset was the theoretical strength of its products. Through acquisition, Amvescap could use its marketing and distribution experience to add value to PS, adding a respectable amount of incremental value to PS at less incremental cost to Amvescap, not even mentioning any possible cash flow issues PS may or may not have been subject to which could have cheapened their bid.

WSDT's rationale makes sense
1) One can manipulate earnings, but one cannot manipulate cash.
2) Cash dividends represent a real, direct and immediate return to shareholders. All else equal, if free cash flow is retained, one must make an underlying assumption on the company's ability to reinvest at a reasonable risk-adjusted rate. Companies that simply pay out that cash flow require no such incremental assumption.
3) Investors on the whole seem to care less about dividend yields than they perhaps should. When was the last time the dividend yield of a stock was a key component of your investment thesis on that stock?
4) Dividends by their very nature lack volatility. They produce returns uniformly over time in a very steady fashion. Contrast this to a portfolio whose return is generated entirely off capital gains and one can see why this may outperform most notably on a risk-adjusted basis.

Some thoughts
1) As a startup, it makes some sense that WSDT is focusing itself on one particular investment methodology. From a marketing point of view, this probably makes for a more unified, clear PR message-- dividend stocks tend to outperform. From a corporate identity point of view, it also makes WSDT more identifiable as a company-- "Ah yeah, WisdomTree, the dividend ETF firm." They may broaden themselves in the future, but proven performance in the dividend space probably won't confine them to the niche they are trying to carve for themselves as "that dividend ETF firm".
2) The pedigree of WSDT's management team is both a blessing and a curse from a buyout point of view. Maybe someone would consider buying this company out, but with a team consisting of superstars like Siegel and Steihardt and ETF veterans like Morris, I would think their payoff profile would be better as a standalone entity, leveraging their identity in their marketing pitch. In the event of a buyout, not only would not only be diluting their equity stake, they would also be diluting their ability to leverage their high profile identities. How would Siegel and Steinhard stand out if they were representing a handful of a sea of ETF's for a company like Barclay's? There is no wow value to that. There is wow value to saying superstars have started a firm focused on an underappreciated low-cost investment methodology, and have brought on board high profile veterans of the ETF space to make it happen. Finally, Steinhardt has a 60+% stake in this company. To acquire this company would require his approval. Would Steinhardt sell out at a time like this, before any blood has been shed? Granted, the return he's generated on this company has been enormous. I just get the impression that he decided to get involved with WSDT because of its longer term prospects, so it would seem unlikely that he would sell out in the 3rd inning.
3) Why release 20 ETFs targeting dividends? Are each and every one of these dividend ETF's special, adding value to different investor groups with varying risk preferences? In steady state, the shotgun approach is good at reaching investors across the spectrum, but until they reach steady state, they may be sacrificing the liquidity and perceived appeal of their flagship ETF. I assume they have one or two flagships which they are expecting to be the most likely to perform exceedingly well, because that has historically been the case for other companies. The others in that case may end up being a distraction.
4) What of industry concentration? Certain industries tend to yield more than others. I wish I could read the papers they have put out as I believe at least one draft is public information, but I would assume that their pursuit of high dividend stocks has concentrated their portfolios on particular sectors. I would imagine that this has big implications on the nature of the risk their portfolios take on relative to alternative portfolios which are more broadly exposed with respect to industry. When they release information on their portfolio methodology one may want to take heed of how much industry risk they are exposed to. Their portfolios may be more sensitive to external factors which impact certain industries as a whole, and may be more difficult to diversify off. This may also make risk assessment more difficult, as broad industry trends are "low resolution" by nature.
5) Where do they propose the alpha comes from? If the market were to become arbitrage free tomorrow, this portfolio shouldn't outperform other portfolios which are equally diversified with comparable cost efficiency. Stocks with very high dividends deserve those high dividends because they don't feel their personal growth prospects merit reinvestment in the business, and stocks with low dividends deserve low dividends because they can bring about greater long term shareholder value through reinvestment. In this paradigm, it may be of value to ask the question "where is this outperformance coming from?" This question by itself could be the subject of a very lengthy
research project which I am sure has at least been thought of by our friends at WSDT, in addition to the host of research papers that I haven't read. I would offer the following 'outperformance buckets', categories from which this outperformance may be flowing.
a) Investors tend to underappreciate dividends as a form of shareholder return relative to capital gains.
b) Companies that pay out higher dividends on average tend to be managed by executives who tend to grow shareholder value more than is recognized by the market as a whole.
c) Stocks that pay dividends that are too high (ie. companies that cannot in the long run support their high dividend) tend to be demanded more than is rational by "yield hogs," generating more in the form of capital gains than is justified.
d) Investors underappreciate the volatility benefits of a regular dividend payment relative to the allure of capital appreciation.

From their Filings
WSDT released a form N-1A, but not under WSDT's filings-- they filed under "WisdomTree Trust"-- on March 13th 2006. They didn't give out any information regarding what their expense ratios will be, which is a bummer. I also didn't see any information regarding the rebalancing methodology, which will to a large extent determine how much turnover to expect, which will obviously drive their expense ratio.

Managers

Kurt Zyla and Todd Rose are managing the domestic funds.
Lloyd Buchanan and Robert Windsor are managing the international funds.

They are given a relatively small amount of flexibility as their mandate is primarily to track underlying indices, with only a small (5%) amount of leeway. Therefore I assume they will spend the bulk of their time making sure to mimic the output of a process which is computerized and automated in nature. They are Bank of New York guys who I have never heard of.

WSDT the stock-- worth it?
It is impressive how much progress they've made over a relatively short period of time. A market cap of $284mm implies $14mm in earnings at a 20 multiple. $14mm in earnings at an expense ratio of 70 bps under a seemingly reasonable cost structure implies perhaps $5B in assets under management, given that this business isn't labor or asset intensive, and seemingly its only real variable costs are transaction costs and the call option-type compensation structure which I am sure exists for the current executive team. PowerShares had $3.5B according to the latest data I was able to find, and they were out for 3 years plus. The market is currently around $400B, which implies WSDT would need to grab a small but respectable portion of the market. I would expect the industry over this time to grow, feeding off weakness in the mutual fund space, which I assume to be around $6-7 trillion right now. If even 5% of current mutual fund dollars were to be put into ETF funds through the addition of a retirement platform or an equivalent, that would nearly double the ETF market size. There is a lot of room for the ETF space to grow.

Also, the jury is still out on the ability of small startup ETF's ability to survive in the face of a market where the two largest managers account for 69% of the ETF market. PowerShares effectively removed itself from the market by getting acquired. I would assume the star power of the current management team and the BOD have a valid shot at replacing the marketing and distribution muscle it cannot hope to match its larger competitors on, but this nevertheless remains to be seen.

Given the risk of the binary nature of this stock's eventual outcome given the fact I doubt they will get bought out (perhaps a bad assumption), I would consider this if it could be a triple in three years. To be a winner it would have to do something like $15B in aggregate as a company in three years. Can a fund which isn't attempting to track the S&P or some other broader index attract this kind of flow in that period of time? It is possible, but I am not sure how probable that is.

Proactive Forecasting

2006-01-29T20:56:00.000-08:00

Joel Greenblatt in both ‘You Can Be a Stock Market Genius’ and ‘The Little Book that Beats the Market’ follows a similar investment generation methodology. He finds baskets of companies which, as a group, tend to outperform the market. He then digs into those baskets with fundamental analysis to juice the returns further, with the knowledge that even if here to add little or no value in the fundamental analysis process, that he still has positive expected returns to back him up because of the risk/reward properties of the baskets being looked at. His “baskets” included spinoffs, partial spinoffs, stock recapitalizations, merger securities, and stocks which are cheap and good, where cheapness is defined by earnings yield and goodness is defined by return on invested capital. For one reason or another, all these groups taken as a whole outperform, so even if he were to pick stocks at random from these lists, under the right set of conditions, he would still outperform.

So the way I see it, his methodology is able to take advantage of the benefits of both quantitative analysis and fundamental analysis.

Quantitative analysis is very good at using large amounts of historical data to back-test things which we may intuitively believe to be true. In this fashion, it can be very helpful as a check, and it can help us form a more reasonable expectation of the sort of returns we can expect from a particular situation over time.

Fundamental analysis is less useful for back-testing because proper analysis requires so much time, but it can reach a depth of understanding which just isn’t possible with quantitative analysis.

Greenblatt (and Pzena), by leveraging both, haven’t done too badly.

The goal of their forecasting is to find pockets of companies which tend to outperform the market. The thing which should be noted, though, is the fact that all their predictor variables are pre-visible—they are all things which are known with complete certainty at the time of investment. For example with the magic formula, the ttm ROIC and earnings yield are by definition already known—there is no uncertainty that those numbers aren’t true.

If the only goal of forecasting is to find groups of companies which tend to outperform, why should we constrain our predictor variables like this though? I would introduce the notion of “cost of error in my predictor variables” as well as “predictability of my predictor variables.” In this context, I would claim the following:

{Usefulness of an input variable} = f({ability to know input variable}, {ability of input variable to predict output variable}, {cost of error if input variable’s actual value deviates from expected value}).

What typical regressions assume, in this paradigm, is that the cost of not knowing what our input variable’s actual value is is infinite. This forces us to make forecasts entirely on the basis of past data. I could see some value though in including input variables which are forward looking—I might not know what their value will be exactly, but if I know that I can predict those input variables with a good level of confidence (through a lot of due diligence, for example), then those input variables could be a lot more useful than input variables which strictly look to the past. While I'm on the subject of typical regressions, I'd also like to add that most people tend to get more than a little bit lazy in their data collection. Why should I constrain myself to variables that I can easily get, or that I can easily quantify? This misses out on the whole notion of cost. There are a lot of "fuzzy" variables that could provide wonderful insights to any quant model, if only someone would just go and do a little more digging-- be a little more subjective-- and stop being so damn traditional for once.

Anyways I digress.

If I find an input variable which I think can predict with a good level of confidence future returns, but I’m not 100% sure what the input variable’s value will be (for example, next year’s earnings), then {ability to know input variable} decreases but {ability of input variable to predict returns} increases. As long as the cost of deviation is low, I could very well favor this input relative to historical inputs.

This takes Greenblatt’s methodology one step further and completes it. In this context I would run screens like the following: find all companies experiencing massive EBIT growth relative to their current EV/EBIT, and which subsequently are able to maintain EBIT growth over the next four quarters which is at least twice the level of the EV/EBIT. See how these companies have performed over the past 20 years. Analyze the distribution for patterns—are there periods of time where this sort of methodology fell out of favor? Do the losers exhibit a certain quality in a non-random way? If these companies dramatically outperform the overall market, then I know that if I were to screen for companies with massive EBIT growth relative to EV/EBIT, and I was able to predict with a high degree of confidence that that EBIT growth would hold up for at least a year for some subset of this group, I would probably consider constructing a trading strategy around this.

This again uses quant in addition to fundamental analysis, but brings them together much more tightly. This could be useful for the idea generation process. It requires a high level of discipline in the stock picking process, in a similar fashion to Greenblatt and Pzena. It might make a few people some bucks.
-Dan

Randomness Kills Simplicity, But Hey, That's Reality

2005-12-28T03:39:00.000-08:00

“Things Should Be Made As Simple As Possible, But Not Any Simpler”

I was actually feeling quite content as I boarded a bus to New York City today regarding some of the concluding thoughts in the last post about schema theory, and the ebb and flow from complexity to simplicity. As usual of course I brought with me my pseudo-bible, “Fooled By Randomness,” to re-read it... again. As has always been the case, it definitely put me in my place, so I thought I’d temper some of the optimism of the last post with a dose of what Taleb knows best—randomness. Thoughts are again welcome.

Inductive Reasoning
Schema Theory and inductive reasoning have a lot in common. Inductive reasoning involves observing empirical data and searching for patterns of behavior which form the basis for hypotheses about the nature of reality. In other words, it wades through large amounts of data and attempts to make sense of it all through causal links and unifying properties. This is somewhat similar to how a financial analyst gathers a lot of information which at the start seems independent and distinct, but which over time (hopefully) comes together under some line of logic to form a complete understanding of the company and the nature of its business and dynamics.

Taleb’s Issue With Inductive Reasoning
Taleb took more than a few shots at inductive reasoning, and rightfully so. Inductive reasoning’s conclusions are very sensitive to the properties of the process whose observations we analyze. If some process we see is very well behaved, for example is normally distributed, then the information gain we receive with each additional observation is a quantifiable amount which we know a priori. But how can we know in reality with no ability to see the future that a process will continue to behave in a normal fashion going forward? And when the distribution underlying the process becomes increasingly non-normal, we start to run into serious information gain problems.

Taleb characterized this as playing Russian roulette using a gun with 1,000 chambers. If I were to play this Russian roulette with no knowledge of the number of chambers or the number of bullets in the gun, and it just so happened that after 500 trials I was still standing, I would probably start believing there were no bullets in the gun in the first place—induction might lead to a conclusion like this given my knowledge of guns and the number of trials, but this would obviously be wrong.

The main point I’d like to drive home then is the fact that induction naturally and unavoidably simplifies the world. Drawing positive conclusions from an incomplete data set is to some extent what we have to do if we want to do anything, and yet it naturally leads to error. Knowing that such error is always possible and will probably lead to mis-evaluation requires an acceptance and appreciation of randomness. And randomness is the bane of the simplification process I mentioned earlier. The company no longer occupies one mental slot in my brain. All those facts relating to the company which cannot be logically connected to my paradigm of “the company” must sit uncomfortably in other mental slots. It’s inefficient, but it’s also how things are, so what can you do but accept that.

Conclusion
So when Einstein said “things should be made as simple as possible, but not any simpler,” what I think he’s acknowledging is the fact that there is a natural limit to the amount of simplification which can occur. Because of randomness, many things cannot and should not be connected if ones goal is to obtain a rational view of reality for the purposes of forecasting.

It’s a bit sad to believe that we can only truly know that which is false, and can never really know that which is true (Popper). We can only make our best guesses, over and over again, and hope that through personal risk management, the randomness which plagues the decisions we make based on those guesses aren’t so correlated that we suffer terribly. This was Taleb's conclusion, to the best of my understanding. It's not as if he ceased to make decisions. He used statistical inference for all that it was worth to make investment decisions, but then made sure to separate that process from his weighting methodology to tailor his risk profile to his liking.

Not too happy a blog post, sorry guys.
-Danny

Taking Another Look at Arnott (Why Not?)

2005-12-25T01:52:00.000-08:00

As long-time readers know, I am interested in indexation. I have a few thoughts on Arnott’s Fundamental Indexation. Before diving into the improvements though, I thought it might be of value to take a closer look at the theoretical underpinnings of his rationale, which I break up into a few parts.

I'd break things down to two claims. One claim is that the S&P is inefficient because of cap weighting and the other is that Fundamental Indexing can do a better job. They seem to be theoretically somewhat orthogonal so this could help flesh things out. In the interim, I throw out some implications and a test I’d be interested to see.

As usual if anyone has any feedback I would be highly interested to hear it. This is one of the more technical posts as a word of warning.

The Inefficiency Claim
Inefficiency is pretty clear. As I see it, it's due to the fact that deviations from intrinsic value, net-net, tend to have zero expected value in terms of returns and mean revert.

Assume that all stocks have some deviation which is due to intrinsic value and another due to idiosyncratic noise. Hypothetically if I know a priori the future evolution of the changes in intrinsic value of all stocks, and I were to net all stock prices by my perfect estimates of intrinsic value, I would be left with a set of residuals whose returns should have zero mean and a mean reverting tendency. If deviations are comparable in terms of returns and not dollar value, then small caps and large caps are equally likely to deviate by, say, 1% from intrinsic. In reality this might not be exactly the case but it is within a reasonable level I would expect. However the dollar value impact of the deviation will be much larger for the large cap relative to the small cap. On a period by period basis then, if I were to invest as if I were the S&P, I would systematically emphasize fluctuations of large cap stocks more than small cap stocks-- and rightly so if the variation were due to intrinsic value shifts. But if one were to run the simulation mentioned above, one would see that if all stocks' prices were initially set to intrinsic value, the idiosyncratic variations force the market to over-emphasize the fluctuations of the stocks with the positive idiosyncratic residuals relative to a market which fluctuates entirely off of changes in intrinsic value. The mean reverting property of the idiosyncratic noise is then the killer, as it probabilistically speaking puts some drag on the stocks with the over-emphasis. Thus, the problem.

Is there a flaw in that logic?

The Implications of S&P Inefficiency
If the S&P is indeed inefficient, there are quite a few consequences. "The market" is supposed to be mean variance efficient. We use it all the time in our finance courses as the basis behind the market risk premium. We use it to get our hands around the tradeoff between risk and expected return. All of this would basically be wrong. If the S&P is indeed inefficient, we might have to raise the hurdle rate of our projects by a couple hundred basis points.

Of course, it was wrong beforehand too. To be technical, the stock market is a pretty poor proxy for the real market—the whole economy, with a lot of very particular nuances (Zack, I’m sure you explain this 10x better than I can). This just means that even when representing the stock market, the S&P does a poor job.

The Improvement Claim
The second claim is that Fundamental Indexing can do better.

I can't be as confident but I guess the rationale from my point of view goes something along these lines. All stocks in the S&P are supposed to be weighted by their intrinsic values. But if one makes the assumption that stocks deviate from intrinsic, the argument above implies cap weighting, although it is a great proxy for company size, has problems. Why not try out other things which are proxies for company size which might not have the bias that cap weighting has? Income, for example, has a 95% return correlation with the S&P, almost as much capacity as the S&P, also tends to favor very large companies, and doesn't create marked deviant industry allocation. It doesn't take on much more small stock risk from Fama-French, and rebalancing schemes can bring turnover down to the level of the S&P itself. It definitely has more F-F "value" to it but it's not taking on more risk in terms of liquidity, interest rate regime or bull/bear market cycle. It's just trying to proxy for market size without bias, albeit with lower data resolution.

Tempering Expectations; Possible Improvement
While the above rationale is intuitively appealing, its improvement relative to the S&P is a function of the degree of mean reversion there is to the idiosyncratic noise. If “irrational” price movements take years to correct themselves, then attempts to trade this noise, while expected value positive, could take so long and suffer large enough drawdown that it could very well be unfeasible to trade on.

That being said, Arnott himself showed that historically, a fundamentally indexed portfolio outperforms by approximately 200 basis points—this is a sizable margin considering the large back-testing period he considered.

To take a closer look at the inefficiency, one can make a direct link between a fundamental metric and market cap. Take free cash flow (‘FCF’), for example, as our fundamental metric. Market cap (‘MC’) is simply FCF multiplied by MC/FCF, the FCF multiple. Looked at from this angle, the, the inefficiency implies mean reversion in the multiple-- MC/FCF for example. But he never does out the statistics from what I could see in his paper-- he simply turned to other stats which implied mean reversion somewhere. So I'm thinking he could be missing some alpha which could be gotten with a little additional complexity. If all companies are reduced to two numbers-- FCF and P/FCF for example-- then weighting entirely on FCF implies independence between FCF and the multiple on forward returns, right? But I would think that a company which does 50M in FCF on a 20 multiple has a different payoff profile than a similar company which does 50M on a 3 multiple. The multiple implies something about the quality of the underlying earnings, and quality isn’t picked up by FCF on a standalone basis. While Arnott's methodology would definitely reallocate towards the lower multiple company relative to the higher multiple one, it might still be giving too little credit to the 20 multiple, because the market seems to be saying there is something about that FCF which is more valuable to investors.

Has anyone seen a test done which buckets the market by FCF, then buckets again by multiple, creating a matrix of subgroupings, then populates that matrix with 1 year forward returns on a year by year basis? Collection of say 50 years of data would create a 3D matrix. With this one could test the claim that FCF and P/FCF are indeed independent of one another and see if there is any additional insight which could be gained.

Closing Thought (Thanks Mike!)-- Schema Theory
Mike over at TaylorTree posted a kind reference to a couple of my prior posts in one his last entries. I agree with him completely when he references the tradeoff between simplicity and complexity. I just thought I'd chip in with a few thoughts which come from the intriguing field of cognitive development... and my favorite theory of how we acquire knowledge, Schema Theory.

Under schema theory, knowledge takes the form of a multitude of 'schema', which, broadly speaking, are mental representations of what all instances of something have in common. As an example, my "house" schema represents what is common to all houses that I've been in. A house has parts, it's made of many things, it can be used for a variety of purposes, ... the list goes on. This is important because when I look at 1,000 houses, they aren't all completely different from eachother-- they have broad similarities which I have mental categories for with which I can compare the houses.

The transition from complex to simple and back to complex might at least partially be explained by how schema theory explains our learning process. Schema decompose complexity through categorization and abstraction. I'm not big on terms so I thought an example might make things a little more clear.

When dealing with new experiences, we have a tendency to treat them as new and different from what we've experienced in the past. For example, if someone were to throw me a ticker and have me look at its business, I would, at the onset, treat all new information I take in regarding the company as new. I would probably begin by gathering general information about the company-- business line, industry, margins, growth, etc. To a large extent, those data points I pick up, at least at the start, don't really have a place. They are just distinct facts. From a cognitive utilization point of view, this is really, really inefficient! I'm being forced to use all of the slots I've got up there in my brain just to digest all these little random tidbits of information!

What happens over time though is that linkages form. The high margins of the company make sense because they've been able to grow sales without any corresponding growth in assets, so much of the sales growth is simply going straight through to the bottom line. Assets aren't growing because their business does a remarkable job of flexing capacity. Their margins are staying up because of cost-related nuances. The magnitude of the sales growth is explainable by the geography the company resides in and the customers it does business with. All the facts-- the qualitative concepts and the hard numbers-- naturally fall into place, and instead of thinking of the company as 10,000 distinct data points all independent of one another (complexity), it is instead "the company" (total simplicity). All facts are entagled in an fact web which sticks so tightly to itself that they really are all one idea in your head. It goes from using all of our cognitive slots to one of them. And it does so by characterizing the company through the same analytical categories which were used to analyze the hundreds of other companies that have been looked at.

In this context it kind of makes sense that things naturally ebb and flow from simple to complex. We are constantly trying to expand our intellectual borders, learning new tools, new ways of looking at things... but at the same time we are naturally also doing some heavy duty simplification. Making things complicated and simple are the pillars of cognitive development, and something which can be optimized on.

Responding to a comment; model building thoughts

2005-12-18T22:45:00.000-08:00

I'm not quite sure how but a comment by one of my readers somehow evaded me until now. I thought it might be of value to post some thoughts in response.

I would first of all emphasize how extremely basic that article is, and some of the major caveats which might be of value to consider. I'll walk through it a little.

"Step 1. Decide on the time frame and the general strategy of the investment. This step is very important because it will dictate the type of stocks you buy."

While this sounds stupidly simple, it's surprising how often it isn't adhered to, directly or indirectly. As investors, we are subject to a wide range of psychological biases which cloud our ability to make rational investment decisions. Quite a few of them revolve around irrational response to unexpected events... which can have pretty dramatic repercussions on all aspects of our investment making process, including time horizon. I think a lot of this can be dealt with by thinking a little more deeply about the assumptions underlying the investments we make, which I wrote about a while back in Assumptions Management. I can't stress enough how important I think it is to come to grips with the assumptions we are making when we invest in the companies we invest in-- if I fix my time horizon at six months, does that imply I'm willing to stomach any and all price movement in between? Why? Might it be of value to consider risk re-evaluation points so that you can adapt to the changing underlying fundamentals of the companies you've invested in? If so, what is a logical structure for those re-evaluation points-- a function of time? A function of the influx of news? Quarterly, after the release of the latest K or Q? Could one also deal with adaptive conditions by making shorter term forecasts so that, should negative residuals appear, you could go in and figure out why reality deviated from expectation?

More fundamentally, why will my strategy do any better, risk-adjusted, than the market in the long run? If I know that it can't, then why do I believe that it can outperform over the short run, and how do I know when to switch out because my system has stopped working? If I can't answer all these questions with some degree of confidence, I think one is probably making an uninformed investment decision.

"If you decide to be a short term investor, you would like to adhere to one of the following strategies:..."

This is somewhat silly. First of all "momentum trading" and "contrarian strategy" are two sides of the same coin. The author is referring to autocorrelation trading, or the identification of companies whose price processes tend to be serially autocorrelated with past price movement in some form under a certain set of initial conditions. Yes, autocorrelation can have a positive coefficient (trend following) or a negative one (mean reverting, aka contrarian). Great.

While a lot of short term trading is autocorrelation based, this isn't the case for all short term trading, unless one greatly expands ones definition of "autocorrelation" to include a lot more than past price history. I know very little, but I can assure you that these are two of many, many other forms of short term trading.

"Step 2. Conduct researches that give you a selection of stocks that is consistent to your investment time frame and strategy. There are numerous stock screeners on the web that can help you find stocks according to your needs."

I am surprised that steps 1 and 2 have made no mention of historical backtesting of some form or another. Again, I think this comes back to two of the pillars of investing IMHO-- risk exposure and investment assumptions. Different investment methodologies expose us to different forms of risk. Do we know exactly what risks we are exposing ourselves to, and is there a reason why we want to be exposed to them? Even if I have run all the statistical tests in the world and all seemingly indicate that I am looking at a sustainable chunk of alpha, is there no way in some state of the world for that relationship to not hold in the future?

Let's say I'm looking at Greenblatt's magic formula. Its generated some great returns on a risk adjusted basis over the past couple decades. As an individual investor looking to invest my retirement savings for the next 20 years, what sort of things should be running through my head? One possible concern is that given the increased exposure this strategy will get, a large following of individuals will pile on. ETF's will be created which will do the same. If the investment management business were to universally believe that this will generate alpha relative to straight investment in the S&P, then would the marginal buyer, the guy who gets in after everyone else has bought, expect to outperform as well? One of the sad things about many if not all short term trading strategies is that they are only valuable if no one else knows about them and you are able to trade without creating any footsteps.

But there are more concerns. Let's say Greenblatt's formula became extremely popular. At some point, would it be unheard of for companies to tailor their financials to attain better ranking, even if this didn't accurately represent underlying financial reality? While this sounds like a silly concern, I can guarantee you that hordes of companies are doing exactly this in some way shape or form-- window dressing, tailored compensation schemes, ...

"Step 3. Once you have a list of stocks to buy, you would need to diversify them in a way that gives the greatest reward/risk ratio (The Sharpe Ratio). One way to do this is conduct a Markowitz analysis for your portfolio. The analysis is from the Modern Portfolio Theory and will give you the proportions of money you should allocate to each stock. This step is crucial because diversification is one of the free-lunches in the investment world."

This is a whole other topic of its own and is typically used by quants. Again, we are looking at risk... except now it's portfolio risk we're dealing with. We all deal with portfolio management to varying degrees. The only point I'd make about Markowitz has to do with stability. Markowitz optimality is only as good as the assumptions underlying that optimality. Just because a portfolio historically had a certain risk/reward profile doesn't mean that it will continue to have that into the forseeable future. Thus stability becomes important as a measure of just how realiable the past data is.

One insight about Markowitz portfolios for example is that historical risk happens to be a better indicator of future risk than historical return and future return. Knowing that, I would heavily discount a portfolio whose performance as defined by some measure of risk adjusted return like Sharpe if it is driven by return. I would also then perhaps choose portfolios which as a pre-condition jive with my risk tolerance, because I know I can trust historical risk to some degree, and then spend the bulk of my time assessing the expected return of the stocks in my portfolio.

The most true line in that article, IMHO, is the one below:

"Stock picking is a very complicated process."

Hope this helps.
-Dan

Response to Gavekal's Indexation Article

2005-12-18T12:48:00.000-08:00

Response to “How do we invest in this brave new world? Is indexing the answer?”by Charles and Louis-Vincent Gave and Anatole Kaletsky

Gavekal's article was quite thought provoking and very interesting, and revolved around a few central tenets.  One tenet is that the existence and rise of indexation will lead to more inefficiency in the market rather than less.  There were a few reasons.  One reason was that the increased importance of the index made the index the reference point for risk.  Another reason was that due to its being capitalization weighted, the purchase of the index led ones portfolio to be systematically overweighting the stocks which probabilistically speaking are the most overvalued, and vice versa.  It’s a relatively complicated article and there’s no way I can do it justice in one paragraph, so I recommend checking it out.  It is available to the public for free over here.

I just had two questions.

It seems an underlying assumption made in the paper is that “indexation” is and will be, primarily, investment in something which tracks a broader market segment like the S&P.  However might this be changing, albeit slowly, as more investors begin to see the investment appeal of ‘alternative’ indices?  Arnott’s indexation methodology is being implemented at PowerShares and Allianz.  Rydex is actively pursuing a number of innovative strategies.  Its S&P equal weight has a tinge of Arnott in it and has outperformed materially for a quite while, arguably not only because of its relative overweighting in small caps but also perhaps because of some degree of nuances due to its rebalancing. WisdomTree is supposedly coming to market with other innovative products.  Greenblatt at the Conference last week made a very compelling case for a strategy which could very easily be converted into an ETF product, and I would be highly surprised if it isn’t.  All of these products can be invested in by your typical individual investor.  I agree that to some degree these are untested concepts, but they are interesting trend in the ETF world, and one which might have implications many years down the road for those investors who don’t have the time to be effective price choosers in the market mechanism.

Secondly, at that point it could be of value to take a second look at how investment professionals add value to the market. They are compensated for efficiently pricing stocks so that capital is more properly allocated to those who need and deserve it.  If alternative indexes like Greenblatt’s become very popular, it’s as if a sliver of alpha has left the system for a mere handful of basis points.  It would put mutual funds in an awkward position because the relative importance of their benchmark is diminishing, and yet they are forced to remain chained to its fluctuations.  This could have a crippling effect on them and their performance.  And hedge funds would then have to find increasingly innovative ways to generate the alpha their investors are looking for in a seemingly shrunken opportunity set.  Under this paradigm, money would begin to flow, probably slowly at the start, out of mutual funds and the market would evolve into how I think it probably should be—ETFs and professional money managers (hedge funds), focused on absolute returns and on products all up and down the risk spectrum in all shapes and sizes to accommodate the risk preferences and hedging needs to better serve their investors.  While mutual funds have a leg up organizationally and operationally because of their firm entrenchment in various retirement programs, I am optimistic that market efficiency will overcome this if an ETF which charges a mere handful of basis points can do all of what the mutual fund does but at a far cheaper price.  We can somewhat see it coming already, with the rise of ETF’s which are much more friendly to employees at companies—ETF’s which are actively trying to gain ground in the various channels which have traditionally been dominated by mutual funds.  It is in their best interests, and rightly so, to push as hard as they can into these channels to steal market share.  Given their structure, I believe they have a good shot at succeeding.

Once again great article, I was just wondering what your thoughts are on these questions and thought they might be interesting.

Exclusive vs Inclusive; Thoughts on Model Building

2005-12-18T12:28:00.000-08:00

This is a work in progress. I actually disagree with some of what I say below. I think working on a trading desk, trying to piece things together as a trader would, is what pushes in-house models to be more complex than less. Humans are great at capturing some forms of weird idiosyncrasy. That naturally causes the models they would 'like' to build in a very complicated direction.
That being said, there is a world of difference between models which attempt to reach very specific conclusions and then expand, and models which start by making very sweeping, broad statements and over time becoming increasingly granular. Perhaps the market in question and the granularity of your data determing to some extent what the "optimal" problem solving paradigm is.

Thoughts on Quantitative Trading
Being able to identify homogeneity in the financial markets seems to be a driving concept when doing quant trading. Classification and homogeneity are two sides of the same coin-- if all securities in the financial markets were unique, all being driven by uncorrelated processes, it seems that you're shit out of luck. There's no way to build a trading system which makes buy and sell recommendations based on cusip (well, perhaps... there is actually some homogeneity here too (image placeholder)); we're in business when we can find ways to classify securities in some way. A useful classification is able to identify things which tend to trade the same way-- and of course when two things trade the same way, we quants would call a proper long-short of the two a stationary, mean reverting process (this, by the way, is the essence behind cointegration-optimal hedging and indexing).

So let's assume for a moment that the goal is identifying homogeneity in some way, shape or form in the financial markets. Where the hell do you begin. I believe you begin by making the decision of whether or not to adopt an inclusive or exclusive paradigm.

The inclusive paradigm, which seems to be the most popular (perhaps because it relies on the least granular information?), is to identify very broad trends in the market. For example, there may be tens of thousands of stocks trading right now, but if I were to bucket them into capitalization-based deciles, trends begin to form when looking at one-year-forward expected returns. In other words, broad-based homogeneity begins to surface. At that point, we may attempt to identify what we consider to be "the next best classifier," which would then split the deciles into subdeciles, each of which is then even more homogeneous. I bet a lot of people have made good money adopting this paradigm, and to be honest, it's the paradigm I personally have had the most experience with up until this point.

But inclusive classification has many downsides which aren't entirely obvious. First of all, the sometimes extreme level of broadness makes it all the more difficult to identify what classifier is indeed the 'best'. Second of all, inclusive classifications tend to carry with them longer time horizons, which aren't necessarily able to be traded on by desks or funds which need strong enough mean reversion to ensure them a decent probability of success over shorter time intervals. That being said, there are some serious benefits to a proper long-short-based inclusive classification trading strategy. Most notably, as long s one is dealing with securities that have less dimensionality—less complexity—than others, the value of this paradigm IMHO improves dramatically. The reason is because there is so little one needs to then control for. It makes some sense, then, why this seems to be the sort of paradigm from which most ETFs have been created. They strip away idiosyncratic risk as much as possible, they can carry with them lower transactions costs, and retain the ability to expose you broadly to the form of risk you’d like to be exposed to.

But the same isn't really true of other forms of securities. Most securities, in fact, are extremely complex when you think about it. Take municipal bonds, for example. While it may be conceivable to construct a broad trading strategy around municipals, a ton of polluting factors makes things more difficult. First of all there is the issue of liquidity (this actually exists with equities as well). Two securities may look the same and be structured in the same fashion, but if one happens to be less liquid than the other, the more liquid security in an efficient market should demand some sort of a premium. This would then require quantifying the bid ask spread. But that is a classification nightmare in and of itself. Next take the fact that bonds can be issued in any number of states, have all sorts of varying call provisions, bond types (ie. revenue, GO, double barrel, water and sewer, credit rating, insurance, ..., ). It's a fixed income instrument, but it has quite a few idiosyncratic elements. Broad categorizations inevitably fall into the trap of being too general.

So rather than pursue the inclusive paradigm, the paradigm then becomes that of exclusion. That is, find on some truly granular level those securities which tend to be homogeneous in some fashion. Then (as long as your dataset is granular enough), peel off the layers of idiosyncracy from your generic set to other sets, quantifying the various credit spreads which should be applied relative to your reference rate (in the case of municipals, the municipal curve).
It's interesting that these paradigms are so vastly different from one another.
It's also interesting to contrast these lines of thought with that of value investing. Value investing seems to thrive on the idiosyncracy of individual stocks. And yet that is what in some ways kills quant strategies.

Thoughts on Implementation of an Exclusive Trading Model
The question which inevitably pops up is how you actually implement an exclusive model. There may be some theory which is more established, but I think I've come up with a decent work-around. First of all your dataset will of course have to be reasonably large. Even then, the question becomes how one can create a truly homogeneous set of securities when securities have so many differentiating characteristics.

Well, how about this-find the largest group of securities with a reasonable sample size that is as homogeneous as you can possibly make it. I'd call it the path of smallest descent. Lets say you've got a humongous database and you query for data through this a program (ie. SQL). Then scan through all of your variables and identify the one which, when fixed, leads to the smallest decrease in securities. Then do that again. And again. And so on until you are left with the biggest possible generic and homogeneous set of securities as you can find. If you have exhausted all of your variables and you still have a good sample size from which you can get statistically significant insights, good for you. Typically that's not possible if your dataset is granular enough, in which case things get uglier. You start relaxing some of the fixations. You allow for more than one moving part at a time. But if this is the case, then now you have a new objective- relax the fixations which pollute any inference you want to make the least. If you want to examine the behavior of 20 year bonds, for example, you might want to consider making that a range from 19 to 21. Or at the very least, if you want to make an inference on how variable A affects yield, and you need to let one other variable float, it would probably be best if that variable didn't have any sort of systematic relationship to variable A. That way, on average, your inference on variable A should still be correct.

That's just a start. The guiding theme is to make sure that you are making clean inferences. Clean inferences come about when all polluting factors are held constant. So once you reach whatever conclusions you wanted to reach with your relatively small generic set, expand that set by allowing a new parameter to vary, then solve for how that new parameter affects your system. And so on. It's an iterative process which takes a long time. It might not be the best way to go about trading, but it is capable of using your entire dataset and it's highly specific.

The methodology above is interesting but not always useful, and probably doesn’t jive well at all with how the typical value investor thinks about investments. The way I see it, we have a sort of mental playbook which we cycle through when analyzing a stock. Is it a stock which is beaten down hard but has had strong profit growth over the past 5 years, historically strong margins and what have you? This is an exclusive way of looking at the market, whether we call it that or not. We are mentally filtering the market down to very specific subsets, excluding all the rest, knowing well that there are probably a large number of stocks which have as much or more potential than the ones we’re looking at. It might be of value to chew on this a little.

Estimation versus Decision Making; Thoughts on Asymmetric Cost Functions; Thoughts on Stability; Generalization

2005-11-29T01:12:00.000-08:00

I've been thinking a lot about prediction lately because of work I've been tooling around with and had some thoughts I'd like to bounce off people.

The Dilemma
I've been tooling around with more useful ways to generate useful relationships between input and output variables and had the following worry (this is pretty basic; sorry everyone). Least squares minimizes the sum of squared residuals. Normal neural networks also use MSE as their objective function which they try to minimize... through error back propagation instead of through a few simple statistics. The thing about MSE, though, is that it implicitly assumes that positive residuals are just as costly as negative residuals. In other words, if my program predicts that a stock will return 10% over a 3 month horizon, it's just as bad if the stock actually returns 12% as it is if it returns 8%. This obviously doesn't jive with intuition on a couple levels. Not only is incremental loss worse than incremental gain, losses generate more dis-utility than gains generate utility. I honestly don't remember off the top of my head what the actual multiple is, but this is pretty established through controlled experiments. Therefore, I was thinking it is then not terribly accurate to use algorithms which minimize MSE. Why not tweak the cost function to make the residual error condition on the sign of the error, so that one becomes more sensitive to losses? In this way, if one can find sub-segments of ones data which, post-regression, still show up with nice expected returns, one can rest more assured that such trades are indeed good trades to put on, and place your bets accordingly.

Seemed to make some sense. Stunk in practice.

Issues with Asymmetric Cost Functions
The issue with what I just described is that it's combining two things-- estimation and decision making. The regression estimates; the asymmetric cost function adjusts ones decision. It's not right to combine these things, in my opinion.

Standard regression and a traditional neural network are pure estimators of the multivariate relationships within ones data. "Pure" in the sense that given an asymptotically large amount of data, one should theoretically converge to the true stochastic relationship. If one can't, then ones algorithm isn't a good estimator, and it's hard to have faith in the results. Estimation is looking to give its best guess of the current situation, paying no heed to utility or cost.

I tend to think that once the estimation has been done, only then is it valid to begin the decision making process. Now if the relationships in ones data were completely deterministic, there would be no need for decision making. The estimator would leave you with zero residuals, and one could trade away, theoretically, as long as it's legitimate to assume that the future will behave like the past.

Of course in the real world there is stochasticity. One cannot eliminate it. So in my opinion, the proper way to go about incorporating asymmetric cost is to take the original data set, run the best pure estimation algorithms you've got, and base your decision making on the resulting residual plot. Of course when I say algorithms I'm also factoring in not only a neural net and/or regression model, but also the resulting residual analysis, looking for serial autocorrelation and returns-based factors. The whole deal. Net it all out. What I want is a set of residuals which has no trending. I believe that a pure choice can be made on the distributional properties of this set of residuals. If I can safely assume this normalized set of residuals to have some volatility clustering, any other conditional volatility effects, and some other effects which perhaps I can't really explain, then I'm finally getting somewhere with regards to decision making. Maybe I can go back into other datasets and find some possible reason why the process behaved the way it did during its abnormal period, and do my best to generalize that in such a way that I can incorporate some risk of that happening again in some form.

I guess I'm essentially fundamentally splitting the concept of risk from the concept of return and dealing with each entirely separately. Expected returns are what they are. One has used algorithms which hopefully give you unbiased estimates of them. It seems to me that the decision then is simply a function of "risk," with an elementary adjustment for return. That makes sense to me. Much more sense than packing everything into the regression itself. It's sloppy, it doesn't seem to me to be pure, but perhaps I'm missing something.

Stability
Another fundamental issue with prediction is stability. Am I looking at a statistical fluke in my data-set, or is this real? There are of course many techniques which can help give good guidance in this direction like bootstrap and cross validation; the general intuition behind bootstrap being 'well, just how different is this from noise, if I were to make the assumption that I am indeed looking at noise?', and the intuition behind cross validation being 'wait a minute, wouldn't it make more sense to get some feel for how my predictive algorithm does on out of sample data, on average?' Which of course naturally leads to PMSE.

Anyways, one perhaps "dumb" way to increase stability is as follows. Feedback heavily encouraged btw. Take the hypothetical case that I am trying to predict 3 month forward returns. I can of course simply gather the 3 month forward return for all stocks. However this could be a major strain if I am dealing with a small-ish data set and a lot of predictor variables. The problem I see with this is that 3 months is, in some ways, arbitrarily fixed, and it is just one data point among hypothetically tons. The POINT of the regression is in some ways to be able to identify outperformance. Outperformance over 3 months is awesome, but there is nothing special about that number. Therefore, throw in 1 month, 3 months and 5 months, for example, and minimize the aggregate cost function on all three. If the results at 3 months were a statistical fluke, it would be more likely that that stock would then underperform over the two other time horizons. Conversely, if the stock is truly an outperformer, it would have heightened probability of outperformance at 1 month and 5 months as well. By throwing in additional outputs which straddle yours, you stabilize your results, I would think. But I could be wrong.

Generalization
I really think good decision making comes down to properly delineation between risk and return, and how one can go about gaining confidence in one's estimate of the two. It's so simple to say. If only it were easier to implement in practice!

Heavy Short Interest in ETF's

2005-10-17T00:48:00.000-07:00

Sorry for the lack of entries of late. I can't talk about what I've been doing recently.

Interesting article today about short interest in ETF's. There apparently are now a half dozen ETF's with short interest levels greater than 100%, with the weighted average is around 21%. Needless to say it's concentrated in popular sectors which may require sector-specific hedging to remain sector-neutral-- gold and oil. I didn't even know it was possible for an ETF to have a short interest of 308% (Retail HOLDRS--'RTH').

I honestly am not sure what the profit implications are to an ETF sponsor with such high short interest. Are the economics the same? I would assume that healthy short interest would be a boon for ETF's, since one of their primary purposes is as a hedge. Again no need to beat a dead horse but I would hope that puts WSDT at a natural 21% discount to the average ETF... or else something is seriously wrong.

Rydex Preaches "Essential Portfolio Theory"

2005-10-11T22:53:00.000-07:00

Rydex Introduces "Essential Portfolio Theory"

General
Fancy name but essentially what they're trying to preach is diversification across asset classes-- not only stocks and bonds but also real estate, commodities, and much more. In doing so, they intend to provide value through diversification, hopefully moving up that efficient frontier through the use of relatively less correlated assets. Additionally, though, one must ask the question-- would individual investors even know what the efficient composition should be of these asset classes, even if one knows that diversification is a good thing? Probably not. Rydex can spend some bucks on a few geniuses and then spread the overhead over the hopefully large number of people who end up buying into the ETF's. Obviously this is something an individual investor could only do through concerted effort and much more resources expended.

Rydex's claim is that a strategy like this one used to only be available to institutional investors, but Rydex intends to bring them to individual investors. This makes sense. I wonder just how much turnover there is relative to some of the more traditional indexation strategies, but my guess is that it isn't bad.

People Involved
Princeton professor John Mulvey is helping Rydex in the construction of EPT-based portfolios. Given the information provided about his consulting background, and his expertise in large-scale optimization models, it seems pretty clear that he is doing some linear or non-linear programming for portfolio optimization purposes. For those who are unfamiliar with how these programs work I'll attempt to shine some light on the subject.

Linear and non-linear programs maximize something called an objective function subject to a set of constraints. For example assume that you are an institutional money manager and cannot put more than 1% of your wealth in any individual stock, no more than 10% of your wealth in any particular sector, cannot go short, can only invest in equities in the US and in China, and know that your investor base is primarily looking for a slow and steady return with little volatility. One could structure a linear program to create an efficient or optimal portfolio, given some past price (and perhaps volume) data on the instruments you are allowed to invest in. Rebalancing could be done every so often by re-running the program which is trained on perhaps some sort of a rolling time horizon and/or forgetting time (both of which can also be tweaked, although one must watch out for non-stationarity and overfitting as usual). The optimal program would probably be something along the following:

Minimize the volatility of your portfolio holdings {X(1),X(2),X(3),...X(N)}, where X(1...N) comprise the weightings of each stock which can be in your portfolio, subject to the constraint that
(1) your expected annual return is R(target),
(2) {X(1),X(2),X(3),...X(N)} must all be less than or equal to .01,
(3) {S(1),S(2),(3),...S(n)}, your corresponding sector weightings, must all be less than or equal to .1,
(4){X(1),X(2),X(3),...X(N)} must all be greater than or equal to 0 to avoid going short,
...
etc etc. 1...N encapsulates the constraint on the universe of potential holdings, and R(target) is probably a spread off of the risk free rate.

Turning to an EPT portfolio, then, one will probably see something similar to this. Perhaps they are trying to maximize returns subject to a minimum level of volatility. Their investment universe 1...N is most certainly quite large to account for the broad number of asset classes being drawn upon. Their rebalancing time is probably pretty big so as to keep turnover low. Finally it is only reasonable to assume that they are factoring in the differential transaction costs between these different asset classes, because it is most certainly more expensive to trade, say, a corporate bond in a local Brazilian paper company than it is to buy up a handful of shares of IBM.

Finally, given the fact that I assume these EPT portfolios are going to be around for a while, I would assume that the portfolios would be conditioned to be multi-period optimal as well through the use of simulation or a variant of the Kelly criterion or something like that.

Last Bits of News on This and Bigger Picture Perspective
Not much else to say. Mulvey and Reilly, the Director of Fund Research at Rydex, spoke in Manchester on June 27th and June 28th regarding EPT as part of a very large conference which included the likes of Colin Powell. So the word has been out for a few months already.

Overall it seems that the ETF world is moving in the direction WSDT is moving in. These Rydex ETF's and some of the promotional material they've been spitting out smack of a more active, more broadly diversified, generally more innovative breed of ETF's. Like I've said before, conditional on WSDT releasing something of value with little overlap to these other more innovative ETF's, this sort of movement is indeed quite good. When Rydex goes over the news wire saying that Modern Portfolio Theory could use some help given the competitive nature of today's market environment, and that more needs to be done by the individual investor should the individual investor want to meet his or her investing goals over the longer term, Rydex is basically saying (IMHO) "I will pony up a lot of money to educate these stupid people who just don't understand that they are investing sub-optimally on a risk adjusted basis and could use the helping hand of firms like ours and WSDT who provide lower cost, more efficient investment products."

That being said, still left wondering what WSDT is going to do.
-Dan

ps. Funny to note-- so on the one hand you have WSDT which is heavily based out of Wharton, whose head of fund research is a Wharton professor. On the other hand you have Rydex which is coming out with innovative indices and is drawing on the brainpower of a Princeton professor. It seems we cannot escape this rivalry between Princeton and Wharton (go Wharton!).

Thoughts on Insider Trading in Small Caps vs. Large Caps

2005-10-02T22:25:00.000-07:00

Don't have much time to write but I just thought I'd share a few thoughts regarding illegal insider trading. After reading about the recent Citizens insider trading case, one might wonder why there seem to be so few cases of insider trading in small companies. Two reasons come to mind.

Journalists don't care much for the smaller stories. People don't know the companies, the amount of money being made or lost is typically smaller, and in general there's little ability for journalists to sensationalize the story.
Might a similar logic hold true for the SEC? I've heard that this may very well be the case! In an ideal world, it would be great for the SEC to go after each and every case of insider trading. But the sad fact is that they are constrained by their resources. This naturally causes them to deal primarily with bigger companies and bigger trades.

I'm not endorsing people to go out and try to obtain material non-public information from small companies. That being said, it makes an interesting case for insider trackers. Maybe it's more profitable to follow the little guys, not only because small cap companies tend to be less followed, but also because the insiders themselves may know that they are less exposed to the headline and legal risks which their big cap counterparts are exposed to. This double whammy might create interesting profit opportunities.

What We Can and Cannot Take Away From Clinical Studies Regarding Investments; A Generalization

2005-09-29T19:52:00.000-07:00

An interesting way to increase the information set from which we make judgements on human judgement relative to quantitative estimation in an investment framework is to, of course, draw on similar comparisons from other disciplines.

One such discipline is clinical studies (props to Kelvin for the article). A number of papers have dived into comparisons of the two, making clear that much of the estimation currently done in the medical field by doctors and the like should actually probably be made through actuarial methods. The bottom line, it seemed, was that actuarial methods dominate their clinical counterparts in almost every study that has been performed. The tests usually consist of assessing the probability of having a machine and a clinician make a judgement about the nature of a person's illness given the same dataset, and comparing the respective frequencies. Even when clinicians are given an informational advantage, they still don't beat their machine counterpart. In many cases, the new information doesn't help them at all.

I would encourage people read some of the findings-- it's really interesting stuff! But before everyone goes off and becomes quants there are a few things that should be noted; the caveat is that investing is not the same as making a handful of prognoses at a hospital.

Unlike in a hospital setting where everyone needs to get diagnosed (deferring judgement isn't an option), investors have the liberty to avoid that which they have no "edge" on. Charlie Munger comes to mind. In some ways he does precisely the opposite of what the clinician is told to do. He sits on his hands and waits until he sees what he perceives to be a huge opportunity and he puts on a position in size. Market making is another story.
Incidentally, this is why I think many rapid fire trading strategies tend to be short vol. Processes assume a certain set of statistical properties until they don't. Shocks to the system and regime shifts don't lend themselves well to automated models, which might have a difficult time assessing when it's time to re-evaluate the model. I would tend to say along these lines that Charlie Munger's methodology is long vol.
Liquidity removes some of the comparability between the two fields. In some sense I guess there is no liquidity in the medical world-- you make the choice, then are subject to a binary outcome-- yes or no. In the markets it has implications on competition and hence efficiency, transaction costs, market impact, etc.

That being said, some of the criticisms of clinician's assessments reminds me very strongly of the psychological biases subject to investors. Overconfidence when it isn't merited (being Fooled By Randomness), viewing historical events as more causal and less random than they actually were, the phenomenon of being flooded by data to the point that judgement is actually impeded, misconceived disdain for aggregate statistics, improper and randomly varying factor weighting... these are universal decision making problems.

Given the nature of the decisions being made by clinicians, it makes a lot of sense to me that a quantitative framework is more appropriate. That being said, I don't believe the same is necessarily true of the financial markets. Or perhaps I am being fooled by personal bias :)

-Dan

ps. In the same way that one goes about increasing ones information set by looking at comparable situations, the same can be said of stocks. Of course we all know the age old trick of looking at comps. I'm actually referring to estimating "comparability" by whipping out the time series of a stock with every other stock in the market and rank ordering them in terms of absolute value. Of course it might be of value to decrease the resolution of the series to get a more fitting view of reality. One may also want to make other adjustments. But the bottom line is this-- there are some stocks out there which have correlations over the past year that are literally up around 60%. This is ridiculously high. One will also find that certain industries just happen to correlate more than other industries. This has profound implications on our ability to make individual stock bets.

Why should I look at Beta? We look at Beta because our stocks tend to be positively correlated with the market. But if you actually do out the numbers, with daily resolution the absolute correlations are typically quite low. Betas of 1 or 2 or more are typically are a result of having a much higher relative vol.

Now imagine that you have a stock whose correlation with its industry is around 40 or 50%. Do I want to focus my attention on my one company? Perhaps, but from a risk management point of view, there are marked differences between this and a more statistically disperse industry.

Furthermore, correlation studies have implications on information gathering. And hedge effectiveness. But I will leave that up to my readers to think about.

Updates

2005-09-29T00:07:00.000-07:00

I will be a bit on the busy side over the next few weeks. Good news-- I will be a guest speaker at an Information Systems/Information Management Seminar Series in the Operations Research Dept at the University of Pennsylvania on October 28th. Needless to say, the subject is, tentatively, "Web Mining, data integration and the stock market."

Hopefully I don't say or do something stupid, and more importantly, hopefully I actually have something which people might consider interesting!

Highlight of the week: Last Thursday, I got to shake Jim Simons' hand. Arguably the best hedge fund manager in existence. Quite an honor.

Our Worst Enemy is Ourselves

2005-09-26T20:19:00.000-07:00

Our Worst Enemy is Ourselves

Prior to the internet, large scale privacy abuse was all but impossible.  When information was stored in physical documents at home, privacy abuse was simply too expensive to scale.  The same is not true for information stored on the internet.  The density of the internet’s network structure makes it very vulnerable to targeted attacks.  Voluntary or not, the social transition to internet connectivity will inevitably lead to a loss of personal privacy, especially as advertisers find increasingly innovative ways to exploit information about us and our social networks.  Much of what society now considers private will not be so in 20 years because of the internet.

There are many legitimate arguments which run contrary to this notion.  We value our privacy very highly and have explicitly built it into our Constitution through the Fourth Amendment.  We have regulatory groups in place to enforce society’s privacy.  These groups have spurred on the creation of laws and acts like the Electronic Communications Privacy Act, declaring that email is a private means of communication and should be subject to the same level of privacy as phone calls and letters.  Technology has been created to proactively counter privacy abuse—encryption techniques have become more powerful, and an active market has been built around spam filters.  For each virus that has wreaked havoc on networks of computers, there has been an add-on created to neutralize it.  Speaking more broadly, our free market system itself has eliminated privacy abuse—problems of the past have created a consumer demand for protection, which in turn has led to the creation of electronic security companies to effectively meet this demand.

However, can we honestly say that we don’t want to give up our privacy under the right circumstances?  While it is indeed of value to us, history has shown that we are willing to voluntarily sacrifice privacy for functionality.  Gmail, Facebook, Google Search and VisiblePath are notable recent examples of this.  Gmail is perhaps the best free email service available today, with 2.6GB and a very useful search capability.  However its useful services come at the expense of privacy—Gmail has robots which scan all of our emails so that it can craft targeted advertisements.  Facebook allows students to connect more easily with friends, but asks students to voluntarily disclose personal information like phone numbers, email addresses and interests.  Google’s search engine vastly expands users’ ability to retrieve information.  Users tacitly compensate Google by allowing Google to bombard them with advertisements tailored by prior search history and location.  VisiblePath scours the social networks of employees systematically through their emails and address books to identify potential connections with other corporations.  This improves corporate efficiency at the expense of employee privacy.  137M US citizens, 45% of the current US population, use the internet. 84% of these users regularly use search engines like Google, and 92.5% regularly use email services like Gmail.  These percentages will inevitably continue to grow, making it all the more profitable for companies and advertisers to innovate and expand their offerings.  Are we going to enact regulations we don’t want to enact?  Is the free market system going to create products that respect user privacy but have no consumer demand?  Our problem, if it is even valid to call it one, is that we want to give up our privacy.

Yahoo Stock R Mining Functions

2005-09-25T19:14:00.000-07:00

Here are some functions which may be of use to those of you who use R. Gotta do my part for the open source movement! Pretty tame. "yimp" gathers price data for an arbitrary number of stocks over an arbitrary time period. "ksImport" gathers a handful of key statistics for whatever stocks you want and throws them into a list. Check it out. If anyone has any follow-ups, corrections or comments please feel free to email me.
-Danny

#Yahoo Price History Gatherer -- for example, yimp(c("IBM","GE"),20050101,20050901)

yimp <- function(ticker.list,start.date, end.date, data=TRUE, plot=FALSE){
     Source = "http://ichart.finance.yahoo.com/table.csv?"
     startmonth <- as.numeric(substring(start.date,5,6))-1
     endmonth <- as.numeric(substring(end.date,5,6))-1
     nstocks <- length(ticker.list)
     for(i in 1:nstocks){
          if(startmonth <10){
               startmonth <- paste("0",startmonth,sep="")
               }
          if(endmonth <10){
               endmonth <- paste("0",endmonth,sep="")
               }
          Query <- paste("&a=", startmonth,"&b=", as.numeric( substring( start.date,7,8) ),"&c=", as.numeric( substring( start.date,1,4)),"&d=", endmonth,"&e=", as.numeric( substring( end.date,7,8)),"&f=",as.numeric( substring( end.date,1,4)), "&g=d&ignore=.csv",sep="")
          download.file( url=paste( Source,"&s=",ticker.list[i],Query,sep=""),destfile= "tempfile",quiet=TRUE )
          temp<- read.delim("tempfile",sep=",",as.is=TRUE,fill=TRUE)
          temp <- temp[,c("Date","Adj..Close.")]
          colnames(temp) <- c("Date",ticker.list[i])
          time <- sub("-","",sub("-","",temp[,"Date"]))
          tempnames <- colnames(temp)
          temp <- data.frame(strptime(time,"%d%b%y"),temp[,2])
          colnames(temp) <- tempnames
          if(plot==TRUE){
               windows()
               plot( x=temp[,"Date"], y=temp[,ticker.list[i]], type="l",col="blue",lwd=1, main=paste("Prices for ",ticker.list[i]," from ", temp[1,1]," to ",temp[nrow(temp),1],sep=""), xlab=paste("Date",sep=""), ylab="Price")
               end <- nrow(temp)
               mid <- mean(temp[,ticker.list[i]])
               sdup <- mean( temp[,ticker.list[i]]) + sd(temp[,ticker.list[i]])
               sddown <- mean( temp[,ticker.list[i]]) - sd(temp[,ticker.list[i]])
               lines(c( temp[1,1],temp[nrow(temp),1]),c(mid,mid), col="red",lwd=2)
               lines(c( temp[1,1],temp[nrow(temp),1]),c(sdup,sdup), col="red",lwd=1)
               lines(c( temp[1,1],temp[nrow(temp),1]),c(sddown,sddown), col="red",lwd=1)
               }
          if(i ==1){
               list <- temp
               }
          if(i !=1){
               if(nrow(temp)>nrow(list)){
               #if the temp is larger than list, then set the temp dates as the list dates, append to all
               #columns in the small list NA's until they match in length to temp, then append temp to the
               #end.
                    list2 <- list
                    list2names <- colnames(list)
                    tempnames <- colnames(temp)
                    list <- temp[,1]
                    oldlength <- nrow(list2)
                    for(k in 2:ncol(list2)){
                         newtemp <- as.numeric( append(list2[,k],rep("NA",(nrow(temp)-oldlength))))
                         list <- data.frame(list,newtemp)
                         colnames(list) <- c(colnames(list)[1:(k-1)],list2names[k])
                         }
                    colnames(list) <-c( tempnames[1],colnames(list)[2:ncol(list)])
                    list <- data.frame(list,temp[,2])
                    colnames(list) <- c( colnames(list)[1:(ncol(list)-1)], tempnames[2])
                    }
#Note: this makes the assumption that up until we have no price data for a particular stock, all stocks in
#the set trade on the same days. This will be true almost all the time, except for instances in which a
#particular stock is forced to cease trading (for example, for regulatory reasons).  I have yet to see an
#instance of this, but it could very well happen I would imagine, unless yahoo corrects for this.
               if(nrow(list)>nrow(temp)){
                    tempname <- colnames(temp)
                    newtemp <- as.numeric( append(temp[,2],rep("NA",(nrow(list)-nrow(temp)))))
                    list <- data.frame(list,newtemp)
                    colnames(list) <- c( colnames(list)[1:(ncol(list)-1)],tempname[2])
                    }
               if(nrow(temp)==nrow(list)){
                    list <- data.frame(list,temp[,ticker.list[i]])
                    colnames(list) <- c( colnames(list)[1:(ncol(list)-1)],ticker.list[i])
                    }
               }
          }
     if(length(ticker.list)>=3){
          list <- list[,-4]
          }
if(data==TRUE){return(list)}
}

#Key Statistics Importer -- Grab a handful of Key Statistics (ie. ksImport(query="IBM"))

ksImport <- function( file = "tempfile",source1 = "http://finance.yahoo.com/q/ks?s=", source2 = "http://finance.yahoo.com/q/in?s=",query){
pointer <- ":</td"
offset = 2
nstocks <- length(query)
keynames = c( "Market Cap ", "Enterprise Value ", "Trailing P/E ", "Forward P/E ", "Price/Book ",
      "Enterprise Value/EBITDA ", "Trailing Annual Dividend ", "EBITDA ", "Net Income Avl to Common ",
      "Revenue ", "Total Cash ", "Total Debt ", "Average Volume ",
      "Shares Short ", "Shares Outstanding:")
      temp = as.character(Sys.Date())
stats <- matrix(0,(length(keynames)+2),nstocks)

for(j in 1:nstocks){
     temp = as.character(Sys.Date())
     url1 = paste(source1, query[j], sep = "")
     download.file(url1, file, quiet=TRUE)
     x = scan(file, what = "", sep = ">")
     if(length(grep("no longer valid",x))!=0){
          query[j] <- strsplit(x[grep("no longer valid",x)],split="?s=")[[1]][2]
          url1 = paste(source1, query[j], sep = "")
          download.file(url1, file, quiet=TRUE)
          x = scan(file, what = "", sep = ">")
          }
     if(length(grep("There is no  data available",x))!=0){
          stats[,j] <- "NA"
          }
     if(length(grep("Invalid Ticker Symbol",x))!=0){
          stats[,j] <- "NA"
          }
     if(query[j]==""){
          stats[,j] <- "NA"
          }
     if(sum(nchar(x)>15000)!=0){
          x <- strsplit(x[nchar(x)>15000],split=">")[[1]]
          }
     if(query[j]!=""){
     if(length(grep("There is no  data available",x))==0){
     if(length(grep("Invalid Ticker Symbol",x))==0){
          for (s in keynames) {
               loc <- grep(s,x)
               if((s=="EBITDA ")&(length(loc)!=1)){loc <- loc[2]}
               if((s=="Revenue ")&(length(loc)!=1)){loc <- loc[2]}
               if((s=="Total Cash ")&(length(loc)!=1)){loc <- loc[1]}
               if((s=="Average Volume ")&(length(loc)!=1)){loc <- loc[1]}
               if(( s=="Trailing Annual Dividend ")&(length(loc)!=1)){loc <- loc[2]}
               if((s=="Shares Short ")&(length(loc)!=1)){loc <- loc[1]}
               if(length(grep(pointer,x[loc]))==1){
                    grepped = paste(sub("</td", "", x[loc + offset]))
                    }
               if(length(grep(pointer,x[loc]))==0){
                    i=1
                    while(length(grep(pointer,x[loc+i]))==0){
                         i <- i+1
                         }
                    grepped = paste(sub("</td", "", x[loc +i+offset]))
                    }
               temp = c(temp, grepped)
                       }
          url2 = paste(source2,query[j],sep="")
          download.file(url2, file, quiet=TRUE)
          x = scan(file, what="",sep=">")
          s="Industry:"
          grepped = paste(substring(sub("</b", "", x[grep(s, x)][2]),11))
          temp = c(temp, grepped)
          stats[,j] <- temp
          }
          }
          }
     }
for (i in 1:length(keynames)) {keynames[i] = substr(keynames[i], 1, nchar(keynames[i]) - 1)}
        keynames = c("Date", keynames,"Industry")
        output <- data.frame(cbind(Keyname = keynames, Statistic = stats))
     colnames(output) <- c(colnames(output)[1],query)
     #tidying up the format
     output <- t(output)
     colnames(output)<- output[1,]
     output <- output[-1,]
     names <- colnames(output)
     if(length(query)==1){
          output["Industry"] <- sub("&","&",output["Industry"])
          if(output["Trailing Annual Dividend"]==""){output["Trailing Annual Dividend"] <- 0}
          output["Trailing Annual Dividend"] <- sub("%","",output["Trailing Annual Dividend"])
     }
     if(length(query)>1){
          output[grep("&",output[,"Industry"]),"Industry"] <- sub("&","&",output[grep("&",output[,"Industry"]),"Industry"])
          output[output[,"Trailing Annual Dividend"]=="","Trailing Annual Dividend"] <- 0
          output[grep( "%",output[,"Trailing Annual Dividend"]),"Trailing Annual Dividend"] <- sub("%","",output[grep("%",output[,"Trailing Annual Dividend"]),"Trailing Annual Dividend"])
          output <- data.frame(rownames(output),output)
          colnames(output) <- c("ticker","date","mktcap","EV","PEttm","PEfwd","PtoB","EVtoEBITDA","DivYld",
               "EBITDA", "NetIncome","Revenue", "TotCash","TotDebt","AvgVol", "TotShort","TotShares","Industry")
          }
return(output)
}

IXDP is now WisdomTree Investments (WSDT.PK); Deeper Look at PowerShares

2005-09-21T18:32:00.000-07:00

[Dated post-- most recent update on WSDT on June 20th 2006 is here]

It's official.

PowerShares-- Bulking Up Its Management Too?
Interestingly enough, it seems that PowerShares is in some ways following WSDT and upping that management team. They hired Benjamin Fulton as SVP of Product Development and Edward McRedmond as SVP of Portfolio Strategy.

Taking a Look at Fulton:
Surprisingly enough, Fulton spent some time at Nuveen Investments. As a side note, WSDT has had some background with Nuveen if I remember correctly; I believe one of their earlier index ideas was in some way related to something Nuveen had created. Thankfully no lawsuits were thrown (I guess WSDT was in the right!). Needless to say nothing came of those indices so nothing to worry about on that front.

The article linked above mentions his being an MD at Nuveen with a focus on product development. Will most likely be a big logistical help for PS. That being said, Nuveen is an ETF sponsor and has introduced ETF's of its own, which begs the question. Might it be a little more helpful to hire someone with some real ETF experience, especially if you are poaching an ETF sponsoring firm like Nuveen? If he had any direct ETF background, they would have said so in the articles I would imagine.

Bottom line IMHO is that this is definitely a step up for PS. Fulton is nothing to shake a stick at. But it might have been nice to have had a little more ETF-specific experience. His background in bringing products to market puts him in a position similar to Morris at WSDT. I like Morris more.

Taking a Look at Edward McRedmond:
The article linked to above speaks to McRedmond's background pretty thoroughly. He seems to have done some solid analysis of the ETF space and probably has a stronger grasp of the product than most people. Only question I have about him is why, after a full 17 years at AG Edwards, he couldn't move any higher than Associate Vice President. At Citigroup, time-adjusted, this doesn't amount to a super ton.

Getting a Better Picture of PS's ETFs:
They've got around 23 ETF's in total which they basket into four flavors.

A Closer Look at PS's Dividend ETF Portfolio:
One very popular flavor is dividends, the oldest of which is PEY, the High Yield Equity Dividend Achievers ETF. They now have 4 dividend-based ETF's in total-- the other three are the International Dividend Achievers (PID), Dividend Achievers (PFM), and High Growth Rate Dividend Achievers (PHJ). Statistics on these portfolios are contained below.

Enough with the boring details-- can you guys see anything interesting about the historical performance statistics? This is not too hard to see.

... the historical performance of the newly created ETF's suck, unless I'm really missing something. The Sharpe for the flagship PEY knocks the freaking socks off of the three new ETF's. And at the same time, the historical Beta is around half that of the newbies! They are publicly announcing this themselves?

This begs the question-- why the hell should I invest in these other funds if the performance is so much worse, even in the past??? You can trade all the options you want on these things (yes, on the AMEX you can trade options on the newbs), but returns will not magically appear. Pile onto that the fact that the newbs are probably far more illiquid, and all I can see is a pretty bad deal. But hey, maybe I'm missing something. Moving on!

Other ETF's in the Portfolio:

PS also has a large basket of industry-specific ETFs (ie. Biotech&Genome portfolio, Food&Beverage, Leisure&Entertainment, Pharmas, ...) and another basket of style-specific ETF's (ie. Value, Growth, varying cap ranges). They have two funds which track the broader market with the Intellidex Enhancement. Finally, they've got a couple of weird ones which don't really fit into any of the above classifications (a China ETF and an alternative energy ETF). The China ETF is basically filled with a bunch of ADRs. I honestly haven't done too much about it, except that it's pretty heavily weighted towards oil right now. Me being my usual cynical self, I will just throw a points out here to jab at this China ETF a little, and open up to discussion why this may be the case. Will leave the rest for another day.

Here are hypothetical historically backtested results for "Dragon Halter": Beta is 1, Sharpe is 1.02, Correlation is 0.5. All statistics are based on the past 3 years relative to the MSCI EAFE, which is supposed to be representative of foreign stocks. As expected, these hypothetical statistics handily beat the MSCI EAFE, which has a sharpe of 0.14 and a beta and correlation, by default, of 1.

They then show their hypothetical performance over the past year, and their actual performance since inception, as of June 1st 2005. No statistics given for these time periods, except that the performance was markedly worse. Over the past year they had a theoretical return of 4.92%, lagging the S&P and the EAFE. Since actual inception, they have lost money and are currently down 5.40%, while the EAFE and the S&P are up 9.87% and 1.17% respectively. Past performance is not indicative of future results; it seems for this China ETF, we may not even want the hypothetical past performance at all.

Fundamental Indexing
Last thing I thought I would bring up is Bob Arnott from RA. I wrote about him a while back myself. To recap, I was highly impressed with his study. That being said, it seemed he didn't fully flesh out statistics driving the implied investment thesis pertaining to mean reversion.

Well, apparently PS (in addition to Allianz) is jumping on the idea and creating an index around Arnott's research. The expense ratio will be 60 basis points which isn't bad. That being said, something tells me Arnott will be the winner in this one, making some serious jack on the licensing fees off of two companies. Should one get off the ground (doesn't matter to him which one, which would explain his licensing to two companies), he will probably be collecting a nice little check.

So What Is WSDT Thinking About?

Looking back, there have been a handful of strategies mentioned in studies done by Professor Siegel. One, incidentally, was a dividend study. I guess that space seems taken! The other was a study showing the historical performance of the original stocks in the Dow I believe, and how they haven't done all that badly if one were to reinvest dividends, reinvest gains from acquired companies, etc etc. I am doubtful that they would somehow base a strategy off of this.

And with Arnott essentially throwing his strategy out among ETF sponsors for them to tear at eachother, I am not too sure they can really do much with a cap-adjustment strategy.

Index volatility-dependent autocorrelation trading anybody? What do you think Chilton? ;)

That's the latest from me on the enhanced ETF space.

Commentary On The Trouble With Value

2005-09-19T21:53:00.000-07:00

I'm sure the vast majority of you guys have read this already, but I find this sort of analysis to be really cool. I don't have a ton of time so I will briefly sum the paper up with some bullets and graphs before making some comments of my own.

Fact #1: The Market Has it Mostly Right-- P/E Ratio is, in fact, one of the best indicators of relative 1 year forward earnings.
-The graph below sums this one up nicely. What it says, for example, is that when the P/E ratio was in the bottom 10% of its history, earnings growth is 23% below average, and conversely when the P/E ratio was in the top 10% of its history, earnings growth was 26% above the mean. I assume they either took the P/E ratio of the market with annual sampling over its history, or they took all companies available at all years, and annually sampled their P/E ratios. The latter might be subject to survivorship bias conditional on the integrity of the dataset.

Fact #2: Value Stocks have indeed outperformed the market historically

The graph below makes this clear. What it doesn't show, though, is how volatile this outperformance has been, which is where things start to get interesting. For those who are looking at this for the first time, what it says, for example, is that if all stocks over all years were thrown into buckets ordered by their P/E ratios, and one were to calculate next year's return relative to the market return that year, the highest bucket underperformed the market on average by 2%, while the lowest bucket outperformed the market by 3%.

So what GMO did to dig into this a little more was compare the Russell 1000 Growth index versus the Russell 1000 Value index. The author assumes this to be a good proxy for value versus growth, so perhaps one might want to know exactly what the difference is between the two:

Russell 1000® Growth Index: Measures the performance of those Russell 1000 companies with higher price-to-book ratios and higher forecasted growth values. Is constructred to provide an unbiased barometer of the large-cap growth market.

Russell 1000® Value Index: Measures the performance of those Russell 1000 companies with lower price-to-book ratios and lower forecasted growth values.

I would give Russell the benefit of the doubt on this one, but it should be noted that its index doesn't appear to be constructed perfectly on the basis of Price to Book, and perhaps only loosely based on Price to Earnings or Sales.

GMO's piece then delves entirely into statistics on P/S and P/B, with P/S, the metric providing the most "trouble with value" but in some sense the least valid at least relative to P/E, explained first.

This begs a question and a hypothesis.

If the comparison of R1000V versus R1000G is our proxy for value versus growth and both are primarily based on P/B, why would GMO adjust valuations using P/S and P/E? To be totally consistent, if they are going to adjust by P/S, they should construct an alternative index which splits out stocks into 2 buckets based on P/S. Same goes for P/E and P/B. To do otherwise is inconsistent, even though the results may very well be similar!
It seems to me that the P/S example was put forth first because it elicited the most "trouble with value." It should be noted that Rob Arnott, in his construction of a more "pure" S&P index in that really really awesome paper he wrote a while back, P/S simply wasn't as good a representative index than P/E or P/Cash Flow, if I remember correctly. So this coupled with the lack of consistency mentioned in (1) lead me to wonder whether things are necessarily as bad as they appear for value.

Fact #3: Given the recent major outperformance of value relative to growth, value may not have all that much more room to outperform, and indeed may underperform if history is a guide for the future.

I do agree with their main hypothesis, which can basically be summed up with a few more bullet points.

Even though value has outperformed growth by 2.2% on average over the past 26 years (the history of the R1000V and R1000G), R1000V actually underperformed R1000G over the entire history as recently as 2000!
Yes, this was due to there being a bubble in 2000. (This may take a couple read throughs) If one were to hold constant the P/S or the P/E of the value stocks divided by the P/S or the P/E of the growth stocks over the whole time period from the indexes' inception through 2000, value would have actually outperformed growth. The reason is because this relative P/S or P/E measure contracted big time, causing much of the underperformance of value relative to growth from inception to 2000.
Historical P/S and P/E of value relative to growth implies value is 1.7 and 1 standard deviation expensive relative to growth. This doesn't bode terribly well for value relative to growth. The next bullet goes into some numbers.
If one were to take all value over all years and bucket them by P/S and P/E, one could compare the returns over the following year for those stocks net of the return earned on growth stocks over that same year. If one were to do so, one would get a graph as per the one below:

What this says, for example, is that as one goes from the lowest decile of valuation (the lowest P/E or P/S bucket) to the highest, the outperformance of value relative to growth decreases. In the 10th bucket, the outperformance disappears when valuation is measured by P/E and goes negative by P/S! So they say this bodes poorly. And indeed, intuitively it does.

Comments:

Honestly I don't have all that many beyond the inconsistencies and the journalistic concerns mentioned above. The only additional point I might add is that R1000V and R1000G are large cap indices. I would be interested to see how a more total market index plays out, as well as the numbers for small cap portfolios.

Do I believe the value premium has vanished? Nah. But some additional points do merit making.

As GMO mentioned, growth is more volatile and has a higher beta than value. If we have another tech bubble in 2006 (haha yeah right) and all stocks happen to go up like crazy, ah well. Value will underperform but there will be returns to be had. Small loss on an absolute basis. However if the market tanks or treads water, do I want to be in growth relative to value? While I don't have the numbers in front of me, my gut says that value tends to outperform relative to growth in bear markets because value is arguably less susceptible to multiple contraction. This would imply that from a defensive standpoint in this scenario, value would outperform.

Maybe I am fooling myself, but I tend to prefer the risk-reward characteristics of a value-biased portfolio. There are psychological tricks I think we investors are subject to when looking at relative studies.

Relative studies have a difficult time judging absolute performance.

How Did Social Networks Become So Popular So Fast? Some Thoughts.

2005-09-18T00:33:00.000-07:00

One of the things I (along with numerous others I'm sure) have been paying attention to is the white hot popularity of social networking sites these days. I am no expert and there are other sites like Minority Rapport, written by my good friends Doug Sherrets and Jon Turow, which have done an excellent job of tracking their growth and evolution, I thought I'd share some thoughts on why I think things have evolved in the direction they have. To put it simply, we have been bombarded by technologies which have allowed us to communicate increasingly easily with one another. It's only natural that our attention has turned to studying the dynamics of social networks and the rise of well constructed social networking websites. Society needs a structured way to leverage its newfound ability to communicate, and social networking websites offer us this leverage. In this context, it will be interesting to see how social networking continues to evolve. I offer some thoughts at the end.

Below I expand on this idea with a network analysis twist.

In the not so distant past, the primary means through which a person could connect with someone else was face-to-face conversation. This had a marked impact on the dynamic of a person’s social network—it was highly dependent on ones physical location. Because the typical person back then was also highly constrained in his or her ability to move from place to place, we had little ability to surmount geographic constraints. The social benefit to understanding social network dynamics was small because social networks were, simply put, not dynamic. Contrasting how things were with how things are leads to an important conclusion. The social benefit to understanding social network dynamics is heavily dependent on our ability to communicate with one another, and as a result, has been heavily driven by technological change.

The advent of the phone technology eliminated the need to be within a stone’s throw of someone to communicate with them, increasing our ability to communicate. We could connect with important people we hadn’t even seen before as long as we knew their phone number (perhaps through someone in our social network!). The advent of transportation technology markedly increased our ability to communicate because of its ability to increase our geographic range of motion. The advent of email technology allows people to structure their thoughts in the form of a letter and send it to someone across the globe within seconds. The advent of instant messaging technology goes one step further, allowing people to have multiple interactive conversations with each other simultaneously. Because we have been bombarded by technologies allowing us to communicate increasingly easily with others, it is only natural that our attention has turned to studying the dynamics of social networks. Society needs a structured way to leverage its newfound ability to communicate.

However, looking at individual technologies in isolation misses the lion’s share of how technological advance has aided communication which in turn has driven the importance of social networks. As Watts stated in “The Connected Age,” there is only so much which can be learned about the dynamics of a network from a study of the individual component pieces—one needs to think about the network dynamics as a whole. The same concept applies to how technology has driven the growth of communication. While it is true, for example, that cars increased our geographic range of motion, the coupling of cars with cells phones allows us to remain in touch with the people we meet in far-away areas when we return home. The same can be said of social networking websites. Users are far more interested in social networking sites because cars, cell phones, email and instant messaging services make it all the more easy for users to contact and communicate with the people they see on a website like facebook. The evolution of all these technologies in conjunction with one another has driven communication and the study of network dynamics far more than the component technologies could possibly explain in isolation from one another.

What impact does all of this have on corporations? I think it makes a hell of a difference! Communication flow is something which can be monitored, and can lead to quantum leaps in corporate efficiency, IMHO. While individuals may have privacy concerns (and rightly so), there is a goldmine of information which can quite easily be made available to corporations who so desire to scrape it up.

IT crises are exacerbated by communication bottlenecks, so wouldn't it be helpful to know where those bottlenecks are most likely to occur, probabilistically speaking, by analyzing the network flow of emails to and from the IT department?
Stress testing with the proper communication monitors in place could allow corporations to simulate such crises, track the communication flow in real-time and improve corporate communication flow with a solid post mortem analysis of that communication flow.
Corporations could identify the communication gaps which may exist between it and other corporations. Knowledge of such gaps could be indicative of future problems or of potential vulnerability, and could be a stimulus for value-added change.
The list goes on and on. These are all changes that are most definitely possible now given the current state of technology. While no one may act on this technology as much as they could, it wouldn't surprise me at all if we were to see more of a concerted move in this direction at the expense of personal privacy.

Social networks contain a wealth of valuable information. The scary part is that we must lay ourselves bare to unlock the value. Given how competitive the business world is right now, I am not too optimistic about the implications on privacy-- but hey, at least our economy may run more smoothly.

WisdomTree Investments: September 9th 2005 Update

2005-09-07T23:51:00.000-07:00

I will get back to Lo's paper soon, but I just thought I would update my prior post on IXDP, Index Development Partners, WisdomTree Investments or whatever else you want to call it.

Today, they announced the hiring of Richard Morris as the Deputy General Counsel (article here). This new addition interests me, because he seems to fill part of the hole I mentioned in my prior post; that is, regulatory issues and concerns. Morris was senior counsel at Barclays as Barclays went out to launch its very first iShare, which has since become the 800 lb. gorilla in the ETF market. His experience at the SEC further reinforces the unique regulatory skill-set he can bring to the table at IXDP. Putting it all together, their management team is now as follows:

CEO: Jonathan Steinberg

Chairman: Michael Steinhardt

Director of Fund Services: Michael Jackson

CFO: Mark Ruskin

ETF Distribution: Ray DeAngelo

Senior Investment Strategy Advisor: Jeremy Siegel

Senior Analyst: Jeremy Schwartz

Deputy General Counsel: Richard Morris

Board of Directors: Jeremy Siegel, Frank Salerno, James Robinson IV, Michael Steinhardt, Jonathan Steinberg

Looked at another way, they now have 7 senior managers. Two deal primarily with general operations (Steinberg and Ruskin). One deals primarily with more ETF-specific operations (Jackson). Two are solely geared towards the research and development of innovative indexes (Siegel and Schwartz). One will deal primarily with the legal and regulatory issues associated with ETF sponsorship (Morris). One is geared primarily towards marketing newly sponsored ETF’s to various clients and platforms—brokerages, retirement platforms, individual investors, hedge funds, mutual funds (DeAngelo). Thus, the management team seems to flow from the index creation process all the way to the marketing of funds to a wide array of investors.
Key take-aways to me at this point:

Regulatory concerns seem less of a constraint to me than they did pre-Morris.

-However I am still confused as to how they can go about expediting the sponsorship process.

IXDP’s management team has far more depth and breadth than that of PowerShares, which may allow for more explosive growth post-sponsorship than PS could ever dream about.

-PS now has around $500M AUM, 4 partners and around 8 employees. It has 4 ETF’s and 24 awaiting approval (article here).
-Its head portfolio manager doesn’t have quite the same reputation as Jeremy Siegel.
-PS’s distribution and marketing capabilities seem relatively constrained.

My initial estimates for cost were no good; way too low.

-My gut is saying that they’ll need a lot more than $4M in steady state to run the operation they’re looking to run. The size and stature of the management team implies very ambitious plans.

The kicker, it seems, is whether or not enhanced indices will attain proof of concept. And can IXDP pay the education costs necessary to spread the word, as BGI has (and PS currently isn’t)?

-I am not yet sure PS has proven that enhanced indices ‘works’; will these indices really outperform over the longer term?
-PS doesn’t have the infrastructure to do the marketing necessary to educate consumers properly. I don’t expect IXDP to get any real substantial spillover education benefits from PS.
-BGI, as a case in point, has spent large amounts of money marketing its products—seminars, white papers, advertisements, and more (article here). This is in addition to large sums of money they’ve spent to construct and rebalance the $115B AUM in the 99 ETF’s that currently trade under the BGI name. BGI itself has around 2,000 employees.

The future for IXDP still seems bi-modal to me.

-Cost structure getting large, will have to get larger.
-High education costs with little help at this point (unless they or their indices are bought out).

I haven’t had the time to do proper due diligence on just how costly this will be, but one might want to take a step back and think about just how many of the first movers marketing truly new products were the eventual beasts in the space they were moving to occupy. BGI had marked advantages, most notably a large base of capital which it could fall back on to pursue a longer term goal. Will IXDP, through Steinhardt and other financiers, be able to secure enough financing to do the same as an upstart with no parent?

When Andy Lo Says There are Large Systemic Risks in the Hedge Fund Space, It Might Be Time to Start Worrying-- Introduction

2005-09-07T00:27:00.000-07:00

When Andy Lo Says There are Large Systemic Risks in the Hedge Fund Space, It Might Be Time to Start Worrying

Background on Andy Lo
Let me preface by saying that Andy Lo is one of the smartest people in the financial world, IMHO. A financial engineering professor at MIT, he has written numerous papers and books on computational finance and financial engineering. He and Wharton Professor MacKinlay co-wrote the famous paper a while back on the notoriously high serial autocorrelation of the market (back when that was actually a tradable phenomena, prior to the autocorrelation’s subsequent demise in absolute terms). He’s currently at the helm of a $400M hedge fund, AlphaSimplex. And of course, he’s received numerous awards.

Summary of Andy Lo’s Paper
Anyways, he wrote an interesting paper back in August (you can read it here) regarding the risk/reward profile of hedge funds, on average, relative to traditional investments and the implications of that profile on systemic risk in the financial markets.

Specifically he creates metrics to track liquidity risk and the importance of leverage. These are obviously highly tied to systemic risk—should a highly levered investment vehicle experience a sharp loss and the bank loaning the vehicle funds decides to retract some of that credit, the vehicle will be forced to liquidate positions he may not want to liquidate leading to some major market impact. And all else equal, the less liquid the assets being invested in, the more market impact there will be. That it is the essence of what happened to LTCM back in ’98. Sharp losses, credit retraction, forced selling, market instability.

So if hedge funds happen to be more highly levered and are investing in less liquid investments, all else equal, we may want to start worrying that the probability of an LTCM-type blowup will go up. So do hedge fund returns correlate on the downside? For the more sophisticated readers, you may want to skip the sections with heading “Basic Fact”—they don’t really bring much new to the table.

Basic Fact #1: Current Dynamic Risk Measurement is Lacking
The first thing that Lo establishes is the inadequacy of many of the more common risk metrics, especially when they are evaluating active trading strategies. He poses the question—imagine someone were to come to you with a hedge fund that has an average monthly return 2.6x that of the S&P while “risk” as measured by standard deviation is only 1.6x that of the S&P, with 6x less down months and twice the sharpe ratio of S&P and only 60% correlation to the S&P over a 7 year period (1992 to 1999), would you seriously consider investing in that fund? Well, a simple strategy that happens to match that payoff profile is, simply, selling puts on the S&P according to a simple rule. And while no fund would actually go about doing exactly that, there are very creative ways they can do exactly that so that no one knows what the hell they’re doing.

Obviously Lo is hitting on small sample bias in the presence of trading strategies with high skew and kurtosis (tail risk; a strategy that typically has many small positive payoffs and a few really big negative ones). Taleb has been preaching this for years.

Basic Fact #2: Downside Correlation Can Cause Market Neutral Hedge Fund Return Correlation to Go from 1% 99.9% of the Time to 99% .1% of the Time.
Lo calls it phase locking risk and explains it very simply and elegantly by taking into consideration two hypothetically market neutral hedge funds. I’m too lazy to write out the math but the key takeaways are as follows.

During times of market stress, market neutral funds which ordinarily have arbitrarily low correlations to one another can experience arbitrarily high correlations.

Small sample bias again discourages proper estimation of the “true” statistical properties of the moving parts involved.

In fact, the inherent non-stationarity of real-world processes can completely preclude proper estimation of conditional downside volatility and probability (this is just my opinion).

In any case, more sophisticated risk metrics are needed in the face of basic fact #2, which must be able to measure the non-linear impact of having, say, heavy credit or sector exposure. Or how about capturing the systemic risk of investing in an emerging markets fund and a fixed income fund relative to investing in 2 market neutral fixed income funds.

Non-Basic Fact #1: The Dynamics of Hedge Funds Do Indeed Differ from Traditional Investments
A huge number of studies have been done and have come to the following tentative conclusions:

Hedge fund returns have abnormally high positive serial autocorrelation.

“Market neutral” hedge funds may not be all that market neutral when one moves away from ‘beta’ towards a perhaps more applicable measure of market risk.

Hedge fund performance is indeed inversely proportional to size.

Operational risk (fraud in particular) is the primary cause of hedge fund blow-ups.

The list goes on. The point is that one doesn’t typically see these sorts of characteristics in traditional investments.

Statistical Analysis of Hedge Fund Returns Databases:
Lo then goes into some truly very interesting hedge fund returns EDA (exploratory data analysis). Below are some of the truly cool statistical facts from within the CSFB/Tremont Indexes:

Historical average returns vary widely between strategies, with dedicated short sellers on one end of the spectrum at -0.69% and global macro at the other end with 13.85% (the latter fact surprised me greatly!).

Historical correlations with S&P also vary widely between strategies, with Long/Short Equity funds on one end at 57.2% (this seems dangerously high) and dedicated short sellers at the other at -75.6%.

Rolling correlation is on the rise for multi-strategy HF’s and fund of funds, which makes sense—as assets under management goes up, it becomes increasingly hard to not be like the market!

On to the main event—can we measure ‘hidden’ exposures like downside correlation risk, fat tail risk and illiquidity risk? The summary and my thoughts on his results and their implications to come soon.

Thoughts on the Nature of Good Analysis

2005-09-03T15:49:00.000-07:00

Sorry for the lack of posts; I've been a little busy. I've been doing a lot of thinking about the nature of good analysis, and the pros and cons of being systematic relative to a more unstructured analysis. I've reached a few tentative conclusions.

As I've said before, I believe the rational paradigm is to be systematic when it's applicable to be systematic. Emphasis on applicability. Just because I have a certain skillset doesn't mean that that skillset will actually be useful in all contexts! That is most definitely true with value investing.
As Charlie Munger famously said, it really helps to have a lattice to structure the information that you take in. The information "sticks" better as a result and it opens the door wide open to levels of analysis that are inconceivable under another approach. For example, lets say you're looking at some company's balance sheet and you see that they have x square feet in land on their books. How do you process that data point? Well, it'd probably be more useful to consider that in the "breakup value" paradigm and not really a DCF standpoint. Or when I'm looking at stock prices, what information is there to be gained from that? Maybe it might be helpful to see how correlated your stock is to other stocks and to the overall market. The whole point is that it really does help to have those paradigms-- that latticework-- in your head so that you can turn that data into real usable information. It's very helpful to build the proper paradigms for thought.
There are indeed benefits to wading through information in an unstructured way. Even if at this point in time, we have a pretty good general idea of how we should be processing information, things change. What was important yesterday may not be quite as important today. Or entirely new paradigms may form. All this implies that it might be a good idea to always keep an ear to the ground and scour through bucketloads of information that may or may not be all that helpful, just to make sure that you haven't overlooked something which may be of the utmost importance.

IMHO, thinking about how exactly we should be processing data is extremely useful. Have you ever had that feeling after reading every article in a magazine or newspaper that it all simply went in one ear and out the other, and none of the information really stuck? I sure have. Useful paradigms are the solution.

On the Nature of Outliers: Quant Vs. Fundie Analysis

2005-08-26T00:07:00.000-07:00

I've been thinking a lot about the implications of one of my prior posts, "Useful Applications for Quantitative Ability with Fundamental Analysis." I think there are a few things worth mentioning about the nature of residuals versus outliers, and how that plays into this whole schema I've written about god knows how many times.

The bottom line:

Quants may toss outliers to denoise their data so that they can properly estimate the "true" relationship between two variables. Once they have established the "truth," they can simply trade the noise. That is the game of tons of prop trading shops, and it does make some sense. I have done it myself while at a big bank. And we traded a ton of bonds.
Fundamental analysts ('FA's') actively seek those same outliers which were thrown out. Rather than trade continuously, they sit on their hands in waiting most of the time. And when those outliers surface themselves, the FA's put on their positions in size. This also makes sense.

Those simple facts have huge implications on the applicability of quantitative methods in a qualitative setting!

Sure, I could de-noise my time series prior to calculating rolling correlations of every stock on every other stock, and sure I could calculate the correlations of the wavelet spectra, but while that may be more technically precise, it first of all dramatically increases the computational time. But even assuming computational time wasn't an issue, it's not really hitting at the point.

Traditional correlation and the correlation of wavelet spectra are not orthogonal concepts. They are generally jabbing in the same direction. If that is true, then turn to what the goal of analytics are in a deep value setting, and what deep value investors are attempting to do. They are attempting to find situations which are completely out of the ordinary, and are content on sitting on their hands until they are able to find such a situation.

If I am looking for a situation that is truly out of the ordinary, then statistics and hardcore mathematics will not help me 99% of the time, because we aren't trading noise, we are trading outliers. Whatever intuitive concept I am trying to pick up with statistics would have to be so extraordinary that at that point, any statistic generally pointing in a similar direction should be flashing red lights!

I know that a lot of times, a good investment comes as a result of many small oddities lumped on top of eachother. In this sort of situation it does help to have the additional precision. But the driving notion is to keep in mind the nature of the diminishing returns due to precision in a value framework.

Taking a Look at Index Development Partners

2005-08-25T00:51:00.000-07:00

Taking a Look at Index Development Partners (IXDP.PK)
Below I take a look at IXDP and how it fits in the ETF industry. I conclude that while it’s not a deep value investment, it at the least is an interesting stock. At its current valuation though this is kinda ridiculous, unless they do something non-ETF related. I start with a write-up I did back in April 2005 on the ETF industry in general with a focus on equity ETF’s and IXDP in particular. At the bottom I update the situation briefly.
Size:
Total ETF assets accounted for $226.21 billion at the end of 2004, a 49.8% increase over the level of the previous year, according to the ICI.
Growth Prospects:
Some industry experts said it will be hard for ETFs to keep up that kind of growth without new products. Unfortunately, most equity indexes are taken, which means that it will be difficult for ETF providers to come out with new domestic-equity funds. But new products will come. The difference is, some will probably have an actively managed flavor. The first steps towards actively managed ETFs are going to be enhanced indexing, which is where Index Development Partners (IXDP.PK) enters the picture.
Comparable:
PowerShares Capital Management is one company which constructs enhanced indexes. It currently has seven new enhanced indexes based on Intellidex, a quantitative methodology, and now has around $500M in assets (PowerShares is planning to release a few new ETFs over the summer, some of which are based on Intellidex and some of which are going to be more ‘traditional’ they say). Rydex is involved with more passive strategies as well, but it has some pseudo-active strategies too. Its RSP S&P Equal Weight Index (rebalancing periodically) currently has ~$760M in assets.
Avenues for Growth:
Industry analysts, however, stressed that while steady streams of new products are expected, they aren't necessary for the industry's assets to increase. While ETFs grew tremendously last year, total assets are small compared with the more than $8 trillion in mutual funds. If one holds the supply of wealth fixed, this means one big potential source of asset growth comes from taking sales away from the mutual fund industry.
The key to capturing more assets is education. Another potential key is the inclusion of ETFs on retirement platforms. Thus, growth is as much an exercise in marketing and business strategy as it is one in quantitative finance. Michael Steinhardt has specifically stated his interest in targeting all the important constituencies—brokerages, retirement platforms, individual investors, hedge funds, everyone.
My Spin:
Investment strategies can be broken down into three broad categories—passive, pseudo-active, and active. The passive category has been largely exhausted. BGI was the victor in this field with its portfolio of iShares. There may still be some room for growth in passive bond and international strategies, but passive domestic equities are pretty much entirely covered. Active strategies are more the domain of hedge funds, which allow for complete investment flexibility, or other investment vehicles. Active strategies would be a difficult market to enter because it is highly competitive.
However the same isn’t necessarily true for pseudo-active strategies. What puts them in a unique competitive position is two-fold:

They are active enough to “fine tune” passive index investment, potentially augmenting the risk-return characteristics of the investment with simple generally quantitative rules.

They are passive enough to avoid the often onerous expenses charged by hedge fund and mutual fund managers alike.

There are currently two big players in the pseudo-active ETF market segment—PowerShares and Rydex (only some of Rydex’s portfolios however). While it is true that Barclays, Vanguard, and State Street hold the lion’s share of assets in the ETF market overall, pseudo-active strategies are fundamentally of a different type than the sort of ETFs currently being offered.
The Business Model:
So this is the bottom line for ETF success as a business, as far as I can see it. Their main goal is to get huge amounts of investment in their funds so that they can collect the expense fee. ETF companies usually have a wide array of funds, which leads me to wonder what the costs/requirements are to registering an ETF. Whatever the requirements are, by casting out a wide net of distinct ETF’s, the ETF companies can get many disparate investment groups to invest in their products who wouldn’t have done so otherwise. The shotgun approach is probably a solid way to grow assets in the long run.
Cases in point:

Macro hedge funds and quant funds are heavy players of SPX and other passive index ETF’s, for obvious reasons. Liquidity is already huge so ETF’s are in some ways able to capture the oft cited “hedge fund wave.”

Hedge funds and individual investors can make sector specific bets with iShares, so BGI has created a ton of sector specific iShares.

Individual investors wanting broad exposure to the markets without (1) getting charged like crazy for investing in many disparate stocks, and (2) needing to do DD on what stocks lead to the most “representative” mix to get proper diversified exposure.

Retirement planners can just tuck money away in ETF’s instead of more expensive mutual funds, or more risky actively managed investments.

Expense Ratio Case Study—Rydex:
Rydex generates revenues daily from its expense fee. As an example, take the Rydex equal-weighted S&P tracker (RSP). It has $765M under management and charges 40bps in the following way—Monday through Thursday count as 1 day, and Friday counts as 3. So they get .4%/365 of total assets on Monday through Thursday, and 1.2%/365 on Friday. So that fund generates around $3M in revenues annually, spread evenly throughout the year. Not much, but first of all, Rydex has $10B under management. Also, just imagine Barclays with its $110+B in assets, charging more than normal (the majority charge around 70bps), pulling in a steady $770M. Granted, they probably need to spend a good amount on transaction costs, but for crying out loud, they are doing passive indexing, and SPY is able to charge a meager 11bps! I would be surprised if they are paying more than 15bps, because even the SPY is generating a profit. This would mean that with the bulk of Barclay’s iShares, they are probably making around 55bps, implying pre-tax profits on the order of $605M.
There is one other cost which should be mentioned, licensing fees. When a company launches an ETF tracking a particular index, say the S&P, the ETF company will have to enter into a licensing agreement with S&P. This cost will probably be in terms of basis points. Vanguard ran into problems because of this back in 2001, as it was first launching its VIPER ETFs, which were referenced off of the S&P500. They believed that their existing agreements with S&P were enough, and further licensing agreements weren’t needed. S&P disagreed. Having a cheap expense ratio was of crucial importance to Vanguard, which is why this point ended up being hotly contested—Vanguard didn’t want to have to mark up their expense ratio by another handful of basis points.
Now things are quite different for enhancement strategies, because their stated goal is not solely to be representative of an index, or to have a broad exposure to something or other. If all you wanted was an ETF which has broad exposure to something or other, there is probably a passive ETF trading with a cheaper expense ratio right now. The draw to enhancement strategies lies in the potential for the 100-200bp potential upside relative to the reference index, accepting the sad fact that the expense ratio will probably be higher than their passive counterparts. Before I go into potential markets, it’s probably of value to do some back of the hand valuation calculations using PowerShares, the only true enhancement-focused comparable in the market right now, as a comparable. PowerShares was founded in August 2002. It now has around $500M in assets and 11 publicly traded funds. Its 2 oldest funds are less than 2 years old. Bond hopes to have between $2B and $3B by year’s end. Similar to IXDP, PowerShares received $10M in venture capital this year. PowerShares charges a maximum expense ratio of .6% (yet again implying big profits to Barclays).
IXDP now has a market cap of $10M. Assume that it makes a 40bp spread on {expense ratio – transaction costs}, an estimated 15bps below Barclays. Taking a look at operating expenses, before they stopped filing they were incurring around $310k in costs per quarter, or $1.24M at that run rate. Those are all probably research costs. When things start getting interesting, they will also be incurring a lot more business expenses—flying from place to place, lobbying to get advertising or to get on one platform or another—so the past is not a good predictor of the future in this case. Let’s say $3M steady state operating expenses just to throw out a number. If the above assumptions are true, they will need to have $750M under management to break even. $1B under management implies $1M in pre-tax profit. $10B implies $37M in pre-tax profit. Using PowerShares as a rough guideline, if IXDP successfully releases a few strong indices, it could hit $1B in a couple of years. IXDP has some superstar backing—the star power of the likes of Steinhardt, Steinberg, and Professor Siegel will be a plus when it begins marketing.
So the big question is what constituents would want to get involved with enhancement indices. I have a fundamental belief (as a pseudo-efficient markets believer?) that mutual fund money will slowly begin turning to ETF’s, so I believe there will be money flow for good strategies in the coming few years. Beyond that, it’s probably helpful to consider money flow from the various market participants:

I don’t see why anyone would short an enhanced index. If people were, I would start getting worried. So this eliminates all shorts (as a funny point of comparison, PowerShares touts that it can be sold short on a down tick—great…)

If costs are low, if the fund still retains its ability to track the S&P or any important index, and if liquidity is high, IXDP’s indices could get a lot of long money. If the above assumptions are true, then IXDP had better get portfolios out for all major indices!

Steinhardt’s stated goal is 100 to 200bps over a reference index in the long run. This is too small for a long short after interest, so don’t expect anyone to put that trade on.

It might be difficult to convince retirement platforms to consider IXDP because of the uncertainty associated with any form of active management.

I see big upside in Professor Siegel’s and Steinhardt’s ability to convince people that IXDP’s portfolios will be able to outperform the market on a consistent basis. Then anyone who wants to go long “the market” should consider IXDP’s portfolios as an alternative to its more traditional counterparts (ie. QQQQ, SPY).

Update
Since the time of writing the prior post, a few things have changed.

IXDP is changing its name to WisdomTree Investments, Inc.

The stock is now trading at 3.95, and has just completed another round of equity financing. It now has 94M shares, implying a market cap of $371.3M. Siegel and Steinhardt were among the buyers. Steinhardt's cash infusions make me feel a little more comfortable that this thing won't go under.

They’ve brought on board a few more people—Ray DeAngelo as the director of ETF Distribution, Michael Jackson as the new Director of Fund Services, and Marc Ruskin as the new CFO. They seem to have some pretty solid credentials. Finally, for those who have been paying attention, Wharton grad Jeremy Schwartz appears to have gotten a promotion. He is now a senior analyst at the fund.

Putting it all together, things are quite a bit different from the way they were.

Name Change:
The fact that the company is changing its “strategic focus” from developing indices to being an asset manager interests and troubles me. Maybe it’s just me, but “asset manager” sounds quite… active. Perhaps more so than I would hope from a company whose prior investment thesis was built on the notion of creating a ‘small protected niche’ in the ETF space, creating and sponsoring innovative ETF’s. Does this imply that things are simply going so well on the index creation side that they are now focusing on higher goals without compromising the quality of their indices? From what I’ve seen and heard, this does NOT seem to be the case. But perhaps I’m reading too much into “asset manager”—perhaps they are just reinforcing the fact that ETF’s are a great asset management product for individual investors.

Market Cap:
This thing is getting huge on no earnings. With the new full-time employees, my estimate of steady state expenses is probably on the low side. On the upside, it should be noted that in the latest equity issuance, Professor Jeremy Siegel and Michael Steinhardt were investors, although just how much wasn’t disclosed. For Steinhardt of course, this is peanuts. Even if he bought all of the 5.77M issued shares, that would amount to 10% of his existing stake. Steinhardt purchased his stake out of an equity issuance of 56.25M shares at $.16 and ended up with a 65.2% interest in the company (a 2370% return in 10 months... he hasn't lost his touch!).

Re-Evaluating Costs:
Costs prior to the discontinuation of their filings was on the order of $1.2M. I had allocated around $1.8M in possible future steady state annual expenses. With the addition of 4 executives and still nothing out yet, I could very well be undershooting it, because they haven’t had to build any infrastructure yet. While I have the utmost faith in Jeremy Siegel and Jeremy Schwartz, in all likelihood I expect they’ll need to hire a few more research assistants. If they do get an ETF off the ground, something tells me they’ll also need some more operations people. I’m tempted to peg expenses at around $4M to $5M. This implies they’ll need anywhere from $1B to $1.25B to break even. If PowerShares is any indication, a successful ETF or two could put them at around $1B within the next couple of years. $2B would put them at $3M to $4M in pre-tax profits. With a market cap of $371M, I am not too pleased.

Open Variables
Star Power: One open variable in all this of course is the star power of the management team and the experience of new executives. PowerShares is a bunch of people cooped up in a room in Chicago with seemingly few connections. WisdomTree will have far fewer frictions conditional on their release of a solid product.
Registration Frictions: From what I've heard from competitors, it is no easy process to obtain sponsorship of an index, taking upwards of 2 years. There are only around 7 companies with proper registration. Even assuming that IXDP has an index right now and has already filed, this would be somewhat damning. Perhaps IXDP has a work-around, but for now this should be a further point of caution.

Useful Applications for Quantitative Ability with Fundamental Analysis

2005-08-23T13:54:00.000-07:00

I just finished successfully coding up a data miner on another database and am starting to reach a few tentative conclusions on how one can go about building a more structured, more efficient, overall better value framework using a quantitative skillset in addition to a qualitative one.

Benefits of Quantification:
When you think about it, what exactly is it which quantitative operations have which their more qualitative counterparts have less of? I would sum it up with two things:

Quantitative programs can be more precise (many times perhaps overly so!) than the human brain on its own.
Quantitative programs can be far more systematic than the human brain on its own.

Case in point regarding (1): when I proposed a correlation-based trading strategy to one of the smartest quants I know, one of his first reactions was to replace correlation with the correlations of the wavelet spectra and eliminate the leptokurtosis which may muddy results with a shrinker of some sort. By understanding the underlying properties driving our process (or set of processes), one can leverage a technical background to be more precise.

Case in point regarding (2): see some of the below posts for some of the more boring applications of data miners. Programs allow me scale analysis up and across the entire stock market. By slicing the market in intuitively reasonable ways, one would hope to find stocks or situations which are deviant enough from what one would expect to merit diving in with fundamental analysis. As an individual investor though, I couldn't possibly with my one brain look through the whole stock market in multiple ways. Quantitative programs are what allow me to be systematic.

Using Quant in a Value Framework
I guess for right now, the conclusion I've come to is that for a deep value investor, quant can be helpful when providing econometric analytics because of its ability to be more systematic than humans can. So when I pull up a stock that I want to research, with a few clicks I will know where my stock fits in the entire universe of stocks in intuitive and useful ways on many levels (ie. how is the industry doing, where is the P/E of my stock relative to overall market and industry and how has this evolved over time, how does the size of my company stack up with others in the industry, what are similar stocks so that I can scrutinize them, how is insider buying in my industry and in my company relative to other companies in the industry, etc etc). Those are all things I can get immediately with a fine tuned program, and I can delve as deep or shallow as I want because I created the programs and am familiar with manipulating the databases. A human couldn't do that except with great effort or at the least a lot more time expended.

Pros and Cons of the Brain and of Machines; Creating Complementarity
The question then becomes how I can combine these two distinct skillsets in useful ways. I think it boils down to identifying where each can add value relative to the other.

The human brain is much more capable of identifying idiosyncrasy. One of the common problems with relying entirely on a quantitative methodology is its inability to pick up on all the idiosyncrasies which the human brain can see. And yet at the same time quant can be far more systematic than the human brain ever could. Therefore I think it makes sense to tune my brain with quantitative analytics, and tune the analytics with my brain so that I can leverage idiosyncrasy while at the same time leveraging a systematic approach. I'm not sure how much quant's precision can help in a value framework, but I think its ability to be systematic can be of great value.

Thoughts are welcome.

A Statistical Look at the Stock Market, Part 3: Profitability of Industries and the Overall Market

2005-08-22T14:20:00.000-07:00

Having looked at the capitalization breakdown of the stock market, it might be of value to see how earnings or a cash flow proxy ties into the picture. While at this point I can offer no more than a snapshot view (constraints on using yahoo—I’m working on tying in another website with better data), there are nevertheless some interesting facts which pop out. The first question I had was “OK, so the stock market has a capitalization of $23T—how much is it spewing out in earnings?” Well, we can try to give our best guess. Due to the dirtiness of yahoo data, while I had market cap data for 6,146 stocks (I call this dataset “M”), MC and EV data for 6,082 (I call this dataset “ME”), MC, EV and Income data for 4,427 (I call this dataset “MEI”) and MC, EV, Income and EBITDA data for 3,389 (I call this dataset “MEIE”). MEIE had an aggregate market cap of $18T, so (big assumption here) I will simply make the assumption that the value metrics obtained on this smaller (but still large) set are indicative of the overall stock market, more or less. Keeping the about caveats in mind, let’s see what we’ve got.

On an aggregate market value of $20.6T, MEI generated $1.23T in earnings, implying a market profit margin of 6% and a market P/E of 16.7.
On an aggregate EV of $21.4T, MEIE generated $2.42T in EBITDA, implying a market EV/EBITDA of 8.83.

The question then becomes: which industries are contributing the most to market earnings? And how does market value correlate to industry earnings? We can look at this in terms of industry P/E ratios and graphs of total earnings vs. total price. For robustness I limit this analysis to industries with 8 or more companies because anything less and we greatly increase the chance of small sample bias.

As expected, industry market cap is a positive function of the industry earnings. Let’s dig a little deeper into the actual numbers and pull up some names to see how these companies straddle the average P/E of 16. Below is a P/E histogram.

Because of issues with Blogger I am unable to post the full list of industries with their corresponding PE ratios, but below I will post some:

As expected, biotechs as an industry are very, very highly valued with an industry P/E of around 50.

Commodity-focused businesses seem cheap by P/E standards. Steel, copper, the tanker industry, oil and gas companies … all are at the bottom of the list.

REIT’s may seem on the expensive side but P/E probably isn’t the most relevant metric for that industry.
For those who are interested, I have the same information available on EV and EBITDA. Once I can incorporate how these metrics evolve over time, hopefully that’ll add another layer of depth to the analysis. And, of course, it would be interesting to see how mean reversion plays its hand. I will leave this for later.

Keeping an Eye on the Environment

2005-08-19T02:16:00.000-07:00

If I have the ability to learn on an intimate level how one and only one industry operates, what sort of characteristics would I be looking for in that industry? For starters I've got a few ideas.

1. The total number of stocks trading in that industry; the more the better. This is because I would have the ability to make bets on a wider array of stocks; in other words, better breadth (thanks Alex). From a completely pragmatic point of view, a larger number of stocks increases my chance of finding one or two that are really really cheap witout having to move outside of my sphere of confidence.
2. The absolute correlation of the industry with the overall market; the less the better. If I have little ability to predict what the market will do going forward, do I want my investments to be driven by the market? I may know that the market generally has positive expected returns over long time horizons, but from a risk management point of view, that's a tricky argument to play. It's an unhedged risk.
3. The level of flux inherent within the industry; the more the better. Even if there were a ton of stocks in my industry, if they were all perfectly correlated with one another that wouldn't leave me with too much to work with-- arguably, the wider set of stocks would offer me little advantage in that scenario. Flux implies whatever steady state that industry will evolve into has yet to surface. This is the sort of environment in which fundamental analysis and critical thinking can be truly value added.
4. The market value of the industry; all else equal, the more the better to avoid possible capacity constraints.
5. The level of competition within the industry; the less the better. Small caps may have one fortieth the market value of large caps, the competition is also markedly lower, and reasonably so. For investors who don't have to worry as much about capacity constraints, one could make a few compelling arguments for why small caps may be a lucrative place to be.
6. The amount of insider buying within the industry; the more the better. This is a very nuanced subject which I won't go into here. I have issue with most academic studies on this topic because their academic techniques strip a lot of them of creativity and flexibility. There are a few great books written on this... but needless to say, the data is there to be inspected.

The cool thing is that literally all of the above factors can be quantified! I will attempt to lay out some actual numbers soon. God knows how helpful such an analysis would be, but it's definitely very doable, and for a stupid kid like me, it might actually prod me on to focus a little more on one particular industry in addition to the generalist individual stock stuff I've been doing up to this point.

Quantified or not, from a risk management point of view it may be of value to take a second look through your portfolio and ask yourself just what sort of environment your investments are living in... or whether you have a coherent investment paradigm in the first place.

A Statistical Look at the Stock Market, Part 2: Market Caps with Industry Focus

2005-08-17T16:18:00.000-07:00

Industry Breakout:
Another interesting statistic might be just how big each industry in the market is in market cap terms. Could be helpful when considering what your capacity may be, should you focus on a particular market segment.

According to yahoo finance, there are approximately 221 industries. Obviously it's then impossible to really list them all out-- but what we can do is take some of the most populous industries as well as some other notable ones to try and draw some conclusions. Below are a few interesting facts (chart of the 26 biggest industries below):

Believe it or not, it appears that there are more regional banks and S&L's than there are stocks in any other industry. There were 220 S&L's in the set, 146 banks in the northeast region, 139 banks in the mid-atlantic region, 101 banks in the pacific region and 95 banks in the mid-west region. The only other industries which are in the top 10 were 'business software and services' (#3), 'business services' (#5), biotech (#6), independent oil and gas (#7), and 'scientific and technical instruments' (#8).
That being said, regional banks and S&L's are by no means the largest industries. The market size for S&L's in aggregate is $134.6B. Banks in the northeast were even smaller at $81.8B.
Perhaps as expected, the largest industry in terms of market cap is Major Integrated Oil & Gas at $1.4T, or 7.2% of our economy. The funny thing is that there are only 10 stocks in the industry (of course, the 10 include XOM, BP, and RD).
Given all the talk of real estate bubbles, why not take a look at the REIT's as an industry in terms of size and composition. From what I can see, there are 157 REIT's thrown into 7 categories-- Diversified, Healthcare, Hotel/Motel, Industrial, Office, Residential and Retail-- with a sum total market value of $302B split. This, by the way, is almost in perfect agreement with Barron's own estimate of the market size of the REIT industry of $300B. While $300B may not sound terribly bad, one must also remember that market cap understates the industry's economic size, which is probably better estimated by enterprise value. In EV terms the REIT market is $600B, twice as large.
Looking at the numbers though, I am not so sure Barron's argument that REIT's have been so bought up that dividend yields aren't compensating enough for their riskiness-- the numbers that I have indicate the average dividend yield is 6.25%, not their quoted 4.5%. For all you yield hogs, check out RPI, CUZ, SAX and AZL. What kind of a whacked up stock has a dividend yield over 50%??

This is all a very low-level analysis, but at least to me it's underscored how important it is to question assumptions and double-check numbers. The information is there, whether it's individual stock data, industry data, economic data, or what have you. As long as you're facile enough with data manipulation to tinker around and do the proper tests (which doesn't take any rocket scientry), I think it's possible to get a much deeper understanding of the nature of the relationship between a set of processes you happen to be looking at. Overlay on top of that some solid individual stock analysis and your analysis could be all the more thorough.

The question then becomes... are there any quantitative ways one could go about identifying an industry in a state of flux, and are there any properties I would hope to see in the industries and/or individual stocks I invest in? That's the topic of another posting.

A Statistical Look at the Stock Market, Part 1: Market Cap

2005-08-17T13:20:00.000-07:00

Having finally had some time to sit down and play around, I've created a few neat functions in an attempt to get a better picture of the composition of the stock market, and hopefully pick up a few neat facts. This is the first of what will be a series of entries, winding around from market cap facts to beta information, to industry statistics, to aggregate value metrics, to aggregate correlation analysis before (hopefully) joining it all with a look at insider trading.

Rather than rely on second-hand information, I decided to dig into yahoo Finance. It's possible to create a function which systematically extracts all the ticker symbols on the yahoo Finance page by going to the screener, setting a non-constraint, and creating a program which cycles through the resulting output and picks up on the location of unique tickers in the HTML from the source page. Out of a stated total number of tickers of 6930, I was able to pull 5958; far from everything, but a respectable fraction.

One can then create another program which takes a user-specified list of tickers and digs into the 'key statistics' and 'industry' tabs on yahoo Finance, pulling out the columns of interest from both for all tickers, spitting the output into a matrix. For starters I pulled market cap, industry, ttm P/E, fwd P/E, Price/Book, EV/EBITDA, Dividend Yield, Beta, and Industry.

I'm going to go back in for a second round, but here are some facts which I picked up.

Market Cap:
Size:
The aggregate market cap of my 'market' was $22.1 trillion. The stated market cap of the NYSE is $20 trillion, indicating to me that this index, while imperfect, is indeed fairly robust.

Capitalization Breakout:
Now let's say you're a big fund manager with a huge line to throw around, and as a result you can invest in nothing but large cap stocks ($1B+ MC). What is your universe? How about mid cap investors? Small cap?

-According to this data, there are 1852 large caps, 698 mid caps, and 3344 small caps. In other words, there are nearly twice as many small caps as there are large caps. The benefit to sticking with large caps is really obvious from the data though. The large cap space has $21.1T in market cap terms. The mid cap and small cap spaces have $509B and $488B, respectively. In other words, relative to a small cap investor, the large cap investor has half as many stocks but 40 times as much potential total equity to invest in. I was actually surprised that the small cap space wasn't more disperse-- however it should be kept in mind that the total number of small caps is highly likely to be biased downwards, because many small caps don't have legitimate key statistics data and the truly small stocks (with prices less than a buck) were kicked out.

Comparison to Hedge Funds and Mutual Funds:
To put this in perspective, we can look at the emergent hedge fund and mutual fund industries.

While $1T seems to be the most popular estimate of hedge fund assets under management, John Mauldin actually estimates it to be closer to $2T (original study done by Strategic Financial Solutions). They arrived at that estimate by analyzing 12 hedge fund databases, removing duplicates and clones, and differentiating between fund of funds and single manager funds. According to them, the ~4000 single manager funds accounted for the lions share of assets under manager at $1.5T, with a mere 175 or so past the $1B mark. In total, they estimated ~7,700 hedge funds and CTA's (commodity trading advisors).
The mutual fund space is much, much larger, with almost 8000 US-based mutual funds and $8T under management. Worldwide, the mutual fund industry is much larger, at an estimated $15T.

The numbers speak for themselves-- it's somewhat staggering that worldwide, there is an estmated $17T in assets under management, 85% of the NYSE in market cap terms and 77% of my total market estimate, but a few points merit making:

It goes without saying, but these hedge funds and mutual funds are not only investing in equity, but also in debt, the world market value of which is larger than the equity markets. The market value of worldwide equity in dollar terms has been estimated to be $36T. That of the bond market is $49T, 36% larger. The sum total of these two is $85T, which implies that mutual fund and hedge fund assets are a less alarming 20% of total worldwide debt and equity markets.

It might be of value to take a look at the actual distribution. I plot a histogram of it below:

(Sorry for the lack of clarity). The left blue line is the median market cap, at $337M (small!). The right blue line is the average total market cap of $3.75B, indicating a huge amount of skew from some of the bigger stocks in the index. As expected, Exxon Mobil is the largest stock in the market. The smallest stock goes by the name of LFG International Inc., with a market cap of a whopping $5k. I wonder why it even bothers to be a publicly traded company, considering the fact that their capital structure is apparently almost entirely financed with debt (its enterprise value is $1M).

The key take-away for me from this is that I should make sure to be more specific when I say that I'm in a crowded space or not. There may be a lot of money in hedge funds, but to then reach the conclusion from this fact that the market has suddenly become really really efficient as a result might be a stretch. I may want to pay attention to the breakout of hedge fund assets deployed in the various market classes.

The fact that small caps as an asset class are 40 times smaller than large caps is a double edged sword in my point of view. It is definitely true that a lot of people can't play there because it truly is a small asset class. That being said, its small size may prove to be a liability should hedge funds, hungry for new places to go and new asset classes to trade, decide that small caps might provide a decent return on their time. There isn't all that much to go around.

Mining the Web for Indicative Data

2005-08-15T00:06:00.000-07:00

I've spent the past day learning how to mine Yahoo Finance for its data, and I gotta hand it to Yahoo-- its website is quite mine-able. R has been my weapon of choice up to this point. The question has been the following: is there some way to leverage the huge databases stored on the web, explicitly (when you simply reference a pure data file in the proper format) or implicitly (when you are gathering the information by doing a raw scan of a page), in an other than pure quant setting? A variant of the results may prove helpful to pure quants too, but that admittedly wasn't the goal this time around.

R is an environment which is predominantly used for statistical computing and data visualization. It is remarkably robust and reminds me of how powerful and useful the open source movement is for guys like us. It's not even that I have a huge list of packages I can use in addition to the already expansive list of functions R in the raw can handle. It's that I can very easily pull up the code underlying the functions, looking into the guts of sometimes quite complicated functions.

In this case, the ability to pop the trunk on R packages is what saves a couple of the functions in the fBasics package-- yahooImport and keystatImport. yahooImport was created to pull price data on an arbitrary number of stocks over an arbitrary number of days from yahoo finance. keystatsImport was created to pull the stock's current key statistics in a similar fashion. To put it frankly, they don't work properly at all anymore because Yahoo has changed the way that it references information on its site. By having access to their code, it took a few hours to not only fix the bugs but improve on the functions. For instance, why limit oneself to pulling key statistics? Why not pull industry-related information? Why not gather more detailed financial statement information? Why not gather all this information is a scalable way so that I can see how 1000 or more stocks co-evolve? The cool thing about these functions is that they allow you to essentially have access to a huge library of information without needing to have that data on your computer at all. You just rely on the fact that yahoo data is stored in a structured format-- as long as that is true (and yahoo doesn't run bots to kill your queries), then all you really need is an internet connection and zap, you've got the data in a piece of software which can cut the crap out of it.

My only real issue with yahoo is that it's only really good at providing you snapshot statistics when you're dealing with anything but price. There's no way for me to extend the mining to look at the past 50 years, let alone the past 20 (or 10)-- I'd get killed by survivorship bias, the fact that indicative data changes with high likelihood over long time horizons, and the lack of more robustly historical financial statement data. This makes it harder for value investors to reap some of the potential benefits of yahoo mining; no historical perspective. There may be value in analyzing pure price data in a fashion similar to this, linking the daily price data for, say, 2000 stocks with their corresponding indicative data, leveraging some of the other features on the site, aggregating in interesting ways, maybe going intra-day... but at this point that's not really my game.

Time to learn how to navigate password protected websites I guess. If anyone has any advice on sites or potentially useful things to find out, I would love to hear them. I make it a habit to share my results with the people who contribute, so it won't be a one way street.
-Dan

Systematic Value Investing

2005-08-10T19:36:00.000-07:00

Being Systematic in a Quantitative Environment--
If there's one thing which I've learned from this past summer, it's the importance of being systematic. Large projects make it less and less possible to just bull through a project with code line by line on the fly. Suddenly you need pseudo-code and flowcharts to map your way to the end before even touching real code. By taking that step away from your programming language, pseudo-code and flowcharts leave you with that much more ability to focus on the bigger picture-- with the intention of zooming back in after you've mapped your path to the finish line out. To write out all that pseudo code it helps to use scalable and robust functions; functions which can handle minor, or even somewhat major, variations in the underlying dataset without bugging out. If you didn't, God knows how long your code would end up being. Also, God knows how much more difficult it becomes to debug! Finally, God knows how tedious things would get as you vary the datasets you are analyzing! Finally, large datasets make it important to have efficient functions so that your functions run in a timely manner. Flowcharts, pseudo-code and good functions support exploratory data analysis and not the other way around.

Application to Value Investing:
I'm of the opinion that these sort of ideas are not at all foreign to value investing, as long as we're able to differentiate between the following two dogmas-- "be systematic" and "be systematic when it's applicable to be that way." Just because I have a hammer doesn't mean that everything is a nail. Hell, I'll be technical when it's applicable to be so. I wish I believed in some 'one size fits all' truth which I could subscribe to. But I don't. I'm more of the belief that if there is some all-encompassing truth does exist, it's more of a tapestry, stitching together a vast number of distinct skills, skillsets, and rules, the breadth of which is so large that we can only hope to capture a sliver. That makes sense to me.

With that in mind I'd like to turn to how value investors and most corporations gather information. This is a realm which seems to me to be less systematically thought about than it probably should be. Let's say your boss goes up to you and says "Dan, I've found an interesting stock and the ticker symbol is XYZ. By the end of the day I want a complete write-up on this stock's business, competition, industry and prospects for the future" (I'm sure we've all had a variant of a situation like this).

It's things like this which I think merit a little more systematicity than they're given, on average. How can I be more systematic in my information gathering? Well, for one, it might be of value to do a thorough search of as many data providers as you can, sampling them all in an attempt to wean out new quality data sources, and then recording those sources, perhaps in some sort of information architecture on a local intranet where your respected co-workers can offer their insights as well with categories and comments. One could also classify all information along multiple lines, essentially creating multiple information architectures allowing you to flexibly cater to the task you may have at hand. Finally some sort of a search component is a must, so that you have yet another way to swipe through your information database, this time with a user-specified keyword.

This sort of ideation, taking a step back from the actual grunt process which we call 'information gathering', might not be pseudo-code in the technical sense, but it sure starts me along a path to create a system from which I can more easily gather and process information on an ongoing basis. The above description might not be a scalable function, but it seems to be a pretty scalable system because it supports and encourages iterative growth as members of the intranet add their comments and input over time. Finally, the above description might not be a robust function, but it seems to be a robust system because of its ability to classify information along multiple lines with a search capability, allowing the user to gather information in multiple ways over the wide range of goals those users may have.

Of course one doesn't need to do all of these things, but hopefully you can see how being systematic can be a good complement to however else you happen to gather information.

Being systematic can help with many other aspects of our investment activities-- it might be of value to think about this from your own perspective.

Comments are welcome!

Categories: theoretical_intersections

Re: Phillymag's "Is Wharton Ruining American Business?"-- Show Me the Comparables!

2005-08-08T18:56:00.000-07:00

While I don't typically blog about news articles, this one merits a few comments because it's a good example of un-reasoned analysis. It reminds me of Nassim Taleb's disdain for journalism in Fooled By Randomness (one can get a feel for this disdain here perhaps?). Seeing as I'm from Wharton, perhaps I'm a little biased :)

The essential premise of the article was that Wharton MBA's obtain less of an eduation and are of abnormally low moral stature (hence the title of the article, "Is Wharton Ruining American Business?").

A number of statistics were brought up without comparables, so there was little ability to really get a feel for their significance on Wharton in particular relative to the overall average, or to the Ivy League average, or to Harvard, for example.

For example the number of actual names brought up of convicted or somehow 'sleazy' executives was relatively small, all things considered. And yet the high profile name of a guy like Rigas or Milken generates so much emotion to some that they don't realize that these people represent a small proportion of the overall Wharton population. How can one create a reasonable comparison across schools?

What I won't say is that all the points given in the article should be dismissed as one sided. That being said, the analysis is incomplete. If the author is going to say that Wharton's total applicant size has dropped 21%, it might be helpful to know what the comparable average was for other schools. It might be helpful to realize, for example, that business school applicant size is inversely related to the state of our economy (for example, the applicant pool for all schools was at record highs when the economy tanked). Overall, 2004 was a pretty good year. And $31.25 per hour at some pub for a handful of hours leading to a comparison to Grasso's $185M retirement package seems overblown.

Well reasoned articles are hard to come by. While I agree that ethics is something to keep a close eye on, and may be something that we should devote more money, time and effort towards, movement in that direction must start with rational, reasoned points that convincingly point to Wharton underperformance, and I don't see that type of analysis in this article. "A problem well specified is a problem half solved."

If Maureen is going to criticize Wharton in particular and not MBA's in general, I only see half the story.

Categories: article_commentary rants

Final Thoughts on Hakansson; Implications on Differential Cost and Ability

2005-08-06T19:36:00.000-07:00

My last post attempted to wade through some thoughts on derivatives and their value as securities. I just had a few more.

Some believe that derivatives can add value through their very existence because of their ability to lower transaction costs. While Hakansson called the transaction cost argument "weak," I still honestly don't understand why.

This is how I see it- even when dealing with derivatives that are totally redundant, the existence of differential hedging ability and cost among market participants implies individual investors (and more generally the 'less efficient') can derive value from the creation of derivatives by efficient low cost hedgers. The reason is obviously because if person A can produce a payoff structure at a cost of X and person B can produce the same payoff structure at a cost of X + e, then person A can be of value to person B by A's creating the payoff structure for B and selling that at a price greater than X but less than X + e.

As long as a payoff structure is demanded by inefficient investors (suboptimal hedging ability, higher costs, or time constrained perhaps), then from a business perspective I don't see how an efficient bank doesn't add value to the marketplace by supplying that payoff structure at a market clearing cost (a function of competition, supply and demand-- gotta account for market impact and the fact that other people may be better than you).

The above discussion is part of a more general message- differences create opportunities. The above example is an almost dumbly obvious portrayal of differences in hedging ability and cost structure. That's something one can build a business on. Differences in perception is obviously another major one, and is probably most in line with what most people consider 'investing' to be. There are other far more subtle differences. Understanding the nature of differences seems to me to be extremely important. All else is rarely equal.

Categories: theoretical_quant

Thoughts on Hakansson's Paradox

2005-08-03T06:56:00.000-07:00

Hakansson’s so-called paradox (JoFQA; Hakansson, 1979) poses a somewhat skeptical question regarding the value of derivatives: if options can only be priced because they can be replicated, then, since they can be replicated, why are they needed at all?

Interesting question, but the more I think about it, the less of a paradox it becomes to an intelligent financial engineer.

It is true that in some sense, the true 'value' of a derivative is in part its ability to create novel payoff patterns relative to that which could have created with the underlying. Stated another way, their value is their ability to be "non-redundant", because if you can replicate the payoff perfectly then to you it isn't really of any economic value except for the fact perhaps that you can hedge efficiently.

But it seems legitimate to say that a derivative sells, all else equal, at a price proportional to the basis risk one expects to take on when hedging that security. Thus for example, if one expects to take on a ton of basis risk should there be a market dislocation (ie. GM correlation trade or CDS selling), one will sell that at a premium to compensate for the risk. All forms of risk should be compensated for properly so this makes sense.

Even though the value of derivatives is very heavily tied to the concept of dynamic replication, this by no means that "options can only be priced because they can be replicated," as was said in Taleb (note: Taleb doesn't make the claim himself and he by all means has 100x as strong a grasp of derivatives than me). As one moves away from that which can be replicated, the basis risk goes up, as does the premium one expects to pay due to basis risk, plus the hedging costs themselves for the optimal hedge done by the optimal hedger, adjusted for liquidity concerns (if one differentiates between pure basis risk and liquidity risk, which I admittedly have blurred a bit).

Variance swaps are a good example of this. One cannot purely trade the underlying. However it can be said (if I'm wrong let me know) that their heightened popularity relative to volatility swaps is due to the fact that it's easier to hedge variance swaps with a strip of options up and down the strike scale. Theoretically if one had a continuous distribution of strikes all up and down the strike scale and all were highly liquid you would arrive at the theoretical "value" of the variance swap. The real value accounts for the fact that some strikes are less liquid, making it more difficult to hedge. The real value also accounts for the fact that one can slide right off the strike scale should the underlying stock tank like a stone because strikes simply don't exist in certain areas. Whatever residual basis risk one expects to incur relative to the optimal hedge's actual hedging costs are accounted for.

A few conclusions I've reached:
1) Theoretical value assuming continuous rebalancing is only the first step, and sometimes it's no step at all;
2) There are a ton of other things to keep track of when determining what a derivative is worth like basis risk, liquidity risk, and supply/demand factors;
3) We pay in a very real sense for the 'value' of a derivative, where 'value' is defined to by uniqueness and non-redundancy of the payoff pattern, and this is a very logical cost. To the extent that a derivative is not redundant, one must compensate the originator of the derivative with a VaR basis risk argument; to the extent that a derivative *is* redundant, however, one must compensate the originator for his hedging costs.
4) When the market pays too much attention to theoretical replication and forgets about the basis and liquidity risk, supply and demand for certain derivatives over others, and other real world stuff along those lines, watch out.

Watch out, CDS. You are a one sided market. One sided markets are one sided until they are not.

Categories: theoretical_quant

Unrelated: Creating Categories with del.icio.us and Technorati

2005-08-02T06:07:00.000-07:00

Sorry for the unrelated entry; I thought it might be of value to bloggers in general.

Categories, IMHO, allow us bloggers to create preliminary forms of information architecture out of individual blogs. Some blogs are more similar than others, so it makes sense to create tags to try and account for those similarities and differences. Some other blog services may provide categories but I believe blogger doesn't.

This is as foolproof a writeup as I could put together describing how to create categories using Technorati and del.icio.us. I thought it might be helpful for some.

Go to Technorati's website. Create an account by clicking in top right to get here. Go through all the steps to create the account. After creating an account, go into your account and claim your blog where asked.

Now go to del.icio.us's website. Create an account here too by clicking on the register link at the top right. Put in the information and confirm the account.

Now for each blog entry you write up, you need to tag in two places, as far as I can tell. First, you need go put a little code in your actual blog entry. Second you need to post a bookmarklet on del.icio.us. Below I explain how to do the two things.
One:
Ted Ernst at his great website has created a very helpful little bit of code to create categories for your blogs. So lets say that for this entry I would like to classify it into "data" and "information_management", two of the tags created in del.icio.us. Then I go here where it says 'Technorati Delicious Bookmarklet', click on the link, and type in "data information_management". You get some code as your output.

Now you need to replace the references to tedernst with your username for the del.icio.us account. Just put that code directly into your blog entry as I have done below.

Two:
Go back into your del.icio.us account and Click on "Post" in the top right. Copy and paste in the url of the blog entry you just wrote. The site then asks for a descriptoin as well as the tags you want. For this entry, I again put in information_management, data. Tags cannot have whitespace in between them, which is why I use '_' for tags with multiple words. You can choose whatever tags you think are most appropriate.

Then you you should be good to go. When you click on the category from a blog entry you should be able to see all the other blog entries you've written which are in the same category.

If I've done anything wrong just let me know! This is an evolving entry (as usual).
-Dan
PS. Watch out for stray commas. They can screw things up.

Categories: information_management data

Assumption Management

2005-07-22T16:19:00.000-07:00

Regardless of the discipline you practice, be it market neutral quant or deep value or event driven or what have you, it might be of value to think more carefully about assumptions.

To put it simply, if we didn't make assumptions we would probably cease functioning. Investment philosophies necessarily carry with them implicit assumptions; the question then becomes what assumptions are your model making and what impact do those assumptions have on your model's perception of reality relative to reality itself... and most importantly, in what sorts of situations will your model systematically deviate from reality and why? Not fully knowing the assumptions underlying a model that you trade seems to me to be similar to going into battle with a gun that you know nothing about. What happens when the gun jams? This is also very similar to accepting tips from a well regarded friend. He might be extremely smart. Hell, he might be right. But should the investment being tipped start tanking like a stone, it feels like shit. I'm sure you've probably had this happen at least once or twice. I sure have.

If you know how and why your model may not be working as well as expected, you can move to the sidelines and tune your model. If you don't, well, sorry.

Take, for example, a 'simplistic value-biased' investor who each year longs stocks in the S&P whose P/E's are in the bottom decile of S&P stocks and shorts stocks whose P/E's are in the top decile of the S&P. What assumptions is he making in constructing such a portfolio? He essentially is assuming that returns of stocks in the S&P are mean reverting over time conditional on the P/E decile one is in. But is it always that way? Maybe we can shine some light on that assumption by pulling up return data over the past 50 years or so to see how empirically mean reverting returns have been. In some years it may work better than in other years-- can we explain this? Intuitively one would expect a strategy like that to underperform during bubble times (ie. internet bubble). Is there some way we can forecast the occurrence of bubbles? Probably not. What is the worst that could possibly happen? Can we stomach the maximum drawdown? What's the expected return? What's the expected volatility of that return? Is there any way we can improve the risk-reward profile of the strategy? Throwing the model into the real world, do we have to worry about liquidity issues on the short end? Might we get squeezed out, causing the model to deviate from the real world? And these questions probably make me only half comfortable with my fundamental assumption.

Why does a deep value investor buy a stock? Because the stock will go up over time... but why? What assumptions are we making and why do we not have to worry about them?

Assumptions and risk are similar in some senses. When I make an investment, I am exposing myself to a plethora of different risks. Liquidity risk. Mark to market risk. Perhaps counterparty risk. Market risk. As an employee at an investment vehicle, I am also exposed to operational risk. What if my boss dies, leaves, loses his desire to work, or fires me? What if the electricity goes out in my building? What if my internet goes out?

The reason it has been said that derivatives are a good thing is because they allow for more efficient transfer of risk. In doing so, the people who want to expose themselves to more particular forms of risk are able to do so, perhaps making the market more efficient as a result. It's like the deep value investor who gobbles up huge tons of the market's individual idiosyncratic stock risk. They do so because they have thoroughly researched the risks they will be taking on and are in the best position to accept that risk. From a market stability standpoint, this is a good thing.

Managing risk is very important. By managing your assumptions you are managing your risk.

Unspoken Languages

2005-07-20T20:19:00.000-07:00

The past few entries have been quite focused on the thought process and foundational elements of trading/investing. For good or for bad, expect more of the same this time around.

I read a blog today comparing a bottoms-up approach to investing with a top-down approach. I think it's great that people are throwing out their opinions and notions like this because that's the sort of thing which spurs on knowledge and intellectual discovery. That being said, the blog troubled me. It wasn't even the content which troubled me; it was the paradigm underlying the ideas (I have written about this in prior posts). I am very much open to the notion that the bottoms-up approach is fundamentally flawed, and that filter rules or other such quantitative measures aren't "optimal" for a first pass-through analysis of the market. But it seems a little silly to me to extrapolate the performance of one mutual fund onto the whole field of bottoms-up value investors (extrapolation bias). It seems just as silly to discredit filter rules by discrediting a rolling P/E ratio on the market. Following insider trading doesn't work on average; numerous papers have pointed this out (although Professor Metrick among others falls into the other camp). Maybe it doesn't work on average, but then again, maybe it does *ahem ahem*

Taking a step back, imagine for a moment that you knew nothing of insider trading and were told to construct a trading strategy around it. What was your split second reaction? Read all news articles with the words "insider trading" in them? Go to Google Scholar or SSRN to pull up all academic studies? Find all books published on the topic?

My initial reaction was basically what I would now call exploratory data analysis followed by quantitative analysis with an overlay of academic papers. It wasn't necessarily the best approach, but it seemed to work decently well.

That sort of exercise really makes me think about what it is that makes a trading strategy successful. One element of my hypothesis is that it has to do with our ability to think, speak, and write in useful foundational languages. I would consider accounting, for example, to be as much of a language as French or SQL. If I can familiarize myself deeply with accounting mechanics and principles, it allows me to open up books that I simply wasn't able to open up before (literally). I don't really see how that is functionally different from my ability to speak a foreign language allowing me to converse with others who speak the same language. My ability to understand quantitative concepts allows me to understand quantitative trading strategies-- if I didn't speak the language, I would probably have attempted at another strategy which I had access to given the languages I did speak.

The implications of the above schema are interesting. For example, languages are not mutually exclusive, and languages can be mutually reinforcing. Perhaps this whole notion is silly and trite, but I've found it puts things in the right perspective when I attack problems. The languages we know, no matter what those languages may be, are nothing more than tools; no more and no less. How you choose to leverage those tools is up to you, but the tools are there nonetheless. There is nothing forcing you to use just one tool, and with time, there is arguably no tool you can't pick up by learning the appropriate language(s).

I would say that bottoms-up and top-down investing are as good as your ability to lever the tools you've picked up over time.

Categories: theoretical_intersections

Simulations Or Mathematics

2005-07-19T17:06:00.000-07:00

I can't make this terribly long because I will need to get home soon, but I believe the relative merits of simulation-based and mathematics-based valuation are an important thing to think about.

To put it briefly, I think it's safe to say that mathematics, and closed form solutions/analytic tractability were the primary tool we used to value securities all of, say, 25 (30? 35? More?) years ago. Black Scholes is obviously an analytic equation derived from the heat equation with a few changes of variables having time run backwards. One can just as easily derive analytic equations for binary options. One can also value numerous other types of derivatives in a similar fashion. One simply needs to assume risk neutrality (that is, that the underlying has a risk neutral drift equal to the risk free rate and a volatility assumption) and you're good to go. But derivatives got a lot more complicated. How do you value a path dependent security like an asian option? How about a rainbow option? A range option? Or how about an option on a basis swap which pays a fraction of floating LIBOR and receives floating BMA, the municipal rate? Suddenly one is interleaving multiple stochastic processes in contorted ways, making it more and more difficult to value things with traditional mathematics.

Enter simulations. As derivatives were getting increasingly complicated, computers were getting increasingly powerful. Raw computing power isn't elegant, but it can surely get the job done. Rather than spend days or weeks searching for the proper way to value a CDS with a variable floating notional dependent on the level of interest rates, one can simply make an assumption on the laws governing its motion (as was done mathematically with the assumption of risk neutrality!), and simulate that stochastic process over and over and over again with a good random number generator. The simulated average terminal payoff is ones best guess for the value of that security. As the number of simulations, this should indeed converge to the theoretical value, assuming that ones assumptions on motion were correct. Furthermore with variance reduction methods, one can decrease the number of simulations necessary to converge to a solution that one is satisfied with; a solution with a standard error below some threshold amount (ie. .5% of the terminal value). Simulations do more than allow you to remain ignorant of mathematics while still getting approximately correct solutions, however. They also give you more flexibility regarding the laws of motion the underlying must follow. Stocks tend to jump up and down randomly at different points? Well, just add in jumps of random magnitude at random times (ie. jumps following a Poisson process with magnitudes that are Gaussian centered around some mean jump amount). Suddenly your model seems oh so much more accurate.

However the flexibility of simulations regarding laws of motion like those stated above are only half of the pie.
1) We cannot live for 1,000,000 years. We cannot possibly enter into 1,000,000 variable notional CDS contracts right now, so even if we assume the correct law of motion, hopefully we can see that the theoretically correct expected terminal value will in all likelihood diverge dramatically from realized terminal value. Over shorter time horizons, other factors become increasingly important. In fact it could be argued that they are so important that the simulated value is useless, to some degree. One must remember liquidity. As I've said before (see "A Refocusing On Liquidity Risk"), a security is worth the cost of hedging that security's risk. As a financial institution issuing a financial derivative, my job is not to take on positional risk. I want to avoid making directional bets on individual companies or asset classes. I am a business, and I want to make money regardless of what happens to the underlying stock. Therefore to me, the value of a derivative security is most definitely proportional to the basis risk I expect to incur while hedging that security. It is not (or should not) be a function of my expectation of what will happen to the underlying stock. Said differently, banks are financial intermediaries facilitating the transfer of risk from those who want to avoid risk to those who want to expose themselves to risk. To do so, I create financial derivatives which have very tailored risk profiles so that individuals or financial vehicles can expose themselves to specific forms of risk while remaining unexposed to others. Equities are an aggregated, somewhat clumsy way of exposing oneself to risk, because equities themselves represent huge multi-dimensional clusters of risk. So I create financial derivatives; I attempt to sell them to one party and then hedge off my risk by synthetically buying the same security somehow (or vice versa). Other people may drive the value of that security up or down, but when it comes down to it, the value of that security is then, once again, a function of the basis risk between my hedge and my short financial derivative.
(As a side note, deep value investors then may be able to successfully make money, year in and year out, by identifying fundamentally undervalued companies whose financial situation subsequently changes for the better. These investors are (hopefully) able to ride out the mark to market gains and losses which they may incur over shorter time intervals, and are thus willing to expose themselves more to mark to market risk. The reason is because they are able to carry that risk (hopefully) without blowing up, under a reasonable set of assumptions. Deep value investors typically play with equity because derivatives are in some sense more of a zero sum game, and their values are simply derivatives of that of the underlying, making an adjustment for liquidity. Not only do you get killed on the bid ask, but seriously, if you want to make a directional bet, wouldn't it make sense to take a position in the underlying?)

So I suppose this is something of a paradigm shift. One must make a fundamental choice between establishing a position and taking on position risk, and exposing yourself to basis risk. They are quite different, and it seems that in some ways simulations may be left a bit lacking. Mathematics may be lacking as well, of course :)
-Danny

Categories: theoretical_quant portfolio_management theoretical_intersections

A Refocusing On Liquidity Risk

2005-07-19T16:55:00.000-07:00

I met an interesting guy today.
One point that he made was a fundamental paradigm shift from 'risk' to 'capital', and how it might be of more value to think of things not necessarily in terms of risk but in terms of capital-- how the accumulation, flow, supply and demand of capital is in many ways a more 'pure' driver of profit and loss. He mentioned having a discussion with another of the top brass at Citigroup regarding the lack of performance Citi was getting from a certain variant of a spread option. They reached the conclusion that it was not model adequacy necessarily which was creating issues; rather, it was the simple fact that it wasn't a liquid enough instrument. He spent the greatest amount of time on GM and Ford as a case in point; how it was an ideal example of how the supply and demand of capital (or lack thereof) wreaked havoc on hedge funds and banks alike. The correlation died, and while people can attempt to backfit in exactly how those securities (the mezz, the equity, and the super high grade debt piece) co-moved in the way they did, it essentially came down to supply and demand-- a large number of people putting on one position, another large number of people putting on another, a trigger event causing a spark, and there simply being no bid.
Discussion of capital flow is a perfect segue into another extremely important concept to keep in mind-- markets are non-Gaussian. Yes, we have all heard this numerous times and yes, Black Scholes is fundamentally flawed as a result, but I'm not sure it's possible to reinforce this enough.
We value derivatives by figuring out how much it will cost to hedge them. Other people may value things differently, but other people are probably wrong, to be honest. A distinction must be made between basis risk and position risk. When you put on a correlation trade on GM as was done before, and you know that in the event of structural shocks to GM, the bid will dry up on the security which secures the hedge will dry up, it is simply stupid to call your risk anything but positional. You are putting on a position-- yes, it might be fucked up, but it is nevertheless a position because you can't hedge away the systematic risk.
The important thing when putting on a derivatives position then becomes-- who are the other people in this market, where are the bids and the asks coming from, and how can I exit my trade, either synthetically or physically, should an event occur which may trigger a need to sell? If there are one or two constituencies dominating the supply of bids and/or offers, you are probably looking at a market which will be vulnerable to a dislocation. Think GM and hedge funds. Think convertibles. These are where the potential profit opportunities may come from.
Last thought. Two equivalent securities are trading at different prices in different markets. Does this mean the market is stupid? Not necessarily in the slightest. It probably means there is a capital flow imbalance creating the difference in price. How can one re-align the prices of those two securities? By finding a way to transfer risk from the less liquid security to the more liquid security. Find some way to get capital flowing. If you can create a financial instrument which will get capital flowing into the less liquid security, more power to you- you will make your profit, and you will also have distilled/disaggregated/de-concentrated the risk of that security. The main risk I am referring to is liquidity risk, which goes hand in hand with hedging costs, the ability to hedge, tying back into the discussion of basis vs position risk. That is what derivatives can do for us more easily.
Final idea. CDS is an example of a market where, in essence, we the banking community are net short. Quite a bit. While we may be actively trading these instruments to some degree, it is arguably safe to say that as the primary issuers, we are net short the securities (please correct me if I'm wrong). We are short way more than there is notional for the underlying corporate bonds. Not only that, the underlying corporate bonds are illiquid. This touches on the final point, which is that derivatives trading is changing in some ways. There is credit risk to these derivatives. Financial instruments like CDS have become extremely liquid- why? Well, for one, we haven't really worried all that much about counterparty risk. Too complicated! But where has that counterparty risk gone? It is still out there, and it is a systematic risk. We cannot fully hedge these securities, no matter how hard we may try, so what will happen when (not if) the CDS market sustains a serious shock which makes clear to the world the supply demand imbalance?

On Trading Strategies, THeir Dynamics and How That May Change

2005-07-14T09:53:00.000-07:00

Sorry I haven't been able to write too much. Finally on break so I think I'll jot some thoughts down which I thought were interesting. First I'll start with a couple claims regarding autocorrelation. "Autocorrelation" is nothing more than a fancy term to describe a particular dynamic that some processes follow, be they stock or bond prices, the ebb and flow of a government regime, or the performance of a class of trading strategies-- autocorrelation answers the question "when a stock goes up today, does it tend to follow that trend and go up tomorrow (positive autocorrelation), and does it revert to the mean and go back down tomorrow (negative autocorrelation)?" I posit the following: (1) autocorrelation exists when people aren't entirely able to detect and/or trade on it effectively or economically, for whatever reason, and (2) autocorrelation, once detected, tends to decline in absolute terms over time to a steady-state (and negative) level. There are some interesting consequences for where we are right now.First of all it's pretty much a moot point that detected autocorrelation tends to dissappear rather quickly. Our own Professor Mackinlay with Andy Lo detected positive autocorrelation within the S&P with a coefficient of around .3, which is huge, back around 20 or 25 years ago I believe. .3 means each move one day in percentage terms implied a move 30% as strong on average in the same direction the next day. However since then it's all but evaporated. A similar form of autocorrelation was later detected in certain industries... then it was detected in certain industries on intraday data. Arguably there is still some autocorrelation to be found intraday in semiconductor stocks (at least as of say 9 months ago). All getting arbed away.I bet that at this moment, the highest degree of autocorrelation exists broadly in 'trading strategies.' The performance of trading strategies themselves are at times very positively autocorrelated. It's something that we've all seen or at least felt to some degree, and it's been statistically detected for generic pairs trading portfolios. It makes intuitive sense, and up until recently, I'm not terribly convinced that the investment community has been able to take advantage of anything of this sort-- due to personnel risk, complications due to having multiple managers, inflexibility transitioning from one strategy to another, and personal bias towards certain skillsets relative to others by the person in charge. After building up a competitive advantage with a particular trading strategy, autocorrelation of trading strategies is the one killer which becomes almost impossible to avoid. All of these frictions in conjunction with one another make it very difficult to effectively "trade" trading strategies.My bet is that some of that may be changing now, or at least that it should, especially with the rise of fund of fund managers. Trading strategies themselves in a fund of funds context are becoming more tangible-- more like real assets-- because they have become so well defined. While diversification across strategies is great, I bet more aggressive effective flexible tactical allocation among strategies by a person very well versed with multiple unrelated strategies would have some pretty good juice to it. Multiple layers of performance fees can be a big problem, but I'm not sure anyone has as of yet been able to actually execute on this idea terribly effectively.Examples of positively autocorrelated strategies are numerous. Take for example convertible arbitrage. It isn't like their current problems just came out of nowhere... they've been around for a while, and they just got worse. More than that, their weakness then feeds on itself, as fund redemptions force funds to unwind their positions-- but of course their positions are probably more or less highly correlated to all the other funds out there in the convert arb space, so the unwinding of one portfolio naturally inflicts mark to market pain on most others. Another example is statistical arbitrage. I have a hunch that a class of traders may be next on the list... more on that later perhaps.I'll try to go one layer deeper. Note that there is a distinct difference between stating that trading strategies are cyclical and stating that trading strategies are trend following or positively autocorrelated. A cyclical industry is an industry which is in some sense both trend following and mean reverting at the same time. Cyclicality implies mean reversion over longer time horizons because no industry stays hot or dead forever. At the same time it implies trend following over shorter time horizons because success and failure tend to feed on themselves. In that framework then, I'm implying only one leg of the cyclical dynamic. I guess the reason is I'm not terribly optimistic that trading strategies in general will survive in the long haul. On a fundamental, some will live and others will die. Now I guess I'll bring up my example of a class of trading which I believe may diminish in importance to a large extent.I'm hesitant to believe that some forms of cash trading will remain around for much longer as well from what i've seen at my own desk and some of the others that i've seen/the people i've talked to. Hedge funds have the ability to lock up capital for 2 to 3 or more years and maintain that longer time horizon without having to worry about marking to market all the time. At banks though a ton of their trading simply doesn't have that flexibility. Very few traders are allowed to have that longer term outlook from what i've seen when things start to turn south. It raises an interesting question: when the primary activity of a desk, be it prop trading or market making, is to create really short term profits on a wide range of credits, is that a job that a large flock of cash traders should be doing in steady state? Or should it be a small handful of traders supported by computers and computer scientists? Some short term traders call it a battle of man vs machine on their desks, and some are saying the machines are making things a hell of a lot more difficult. Machines have that level of efficiency over short time horizons that is difficult to match both in speed and in scale by humans. Which brings back that old topic of quantitative methodologies vs qualitative-- quant shines with really high frequency data and with a large universe of liquidstocks on which to operate for obvious reasons. humans can't possibly absorb really high frequency data, let alone for 1000 stocks at the same time. high frequency data tends to work best over shorter time horizons, which implies that if we are to employ primarily qualitative methodologies, we may not want to be cash trading. haha. So yeah, trend following a agree with a lot when it comes to trading strategies. mean reversion i'll agree with sometimes (high yield being one of them... as well as deep value), but there seem to be a lot more exceptions. lemme kno what you think! -danny

Thoughts on Quantitative Trading, Again

2005-07-14T09:52:00.000-07:00

Food for thought. First part is a lead in to a discussion of the surprisingly complicated question of what "good" analysis really is. In my opinion at least, to attempt to bridge the divide between true quantitative finance with qualitative, it helps to have a robust definition for what good analysis is, regardless of its origin. I attempt one at the end of this long winded piece.
Parametric complexity refers to the number of factors that are analyzed in the investment process. Various price processes are examples of factors, but so are more qualitative things like the weather, the number of buildings a company has, the quality of a CEO’s education or the tenor of his voice.
Depth of understanding refers to how deeply one comprehends a given factor in question. So rather than evaluating a thesis, model or strategy based on how many parameters were analyzed, this evaluates just how well one understands the parameters in question.
I’ll use option valuation as an example to better explain what I mean by these definitions. The Black Scholes equation is what many use to value an option, evaluating the option’s price with 5 parameters—the current price of the underlying, the current volatility of the underlying, the current risk free rate, the time to maturity and the strike price. So even though a stock’s price is governed by an infinite number of factors (fear, greed, sentiment, etc.), no arbitrage restrictions have reduced this plethora of factors down to just 5. This model is pretty sparse in terms of parametric complexity—I believe the reason it does so well has to do with the depth of understanding of the parameters used. Only by fully understanding just how the option’s value is derived from the cost of its replicating portfolio can you model its price in such a succinct way.
Depth of understanding is also responsible for the more advanced incarnations of the model. People realized volatility wasn’t constant so they found ways to model volatility.
GARCH can usually give a much better estimate of one-period-ahead volatility with only one additional parameter, the volatility of the prior day.
Volatility functions try to give better estimates with a couple more factors, like money-ness and time to maturity. People realized interest rates weren’t constant so they were also able to adjust for stochastic interest rates. These are all adjustments which add few new parameters and yet in some cases can greatly improve the estimate of the parameter in question. This is possible through a depth of understanding of the variables in question. No one can say what makes a successful quant (I definitely can’t), but at least from what has happened in the past, it seems like some of the biggest breakthroughs have come not through constructing an incredibly parametrically complex model, or vastly increasing the complexity of an existing model, but through reaching new depths of understanding of whatever process or processes you are looking at with a manageable parametric complexity. What would a deep value investor look at when deciding on whether to buy an option on a stock? I can guarantee you they will allocate their time in a completely different fashion, probably focusing more on what is driving the stock process itself. They will probably take in way more factors than would ever be healthy for a quant model—the difference in the number of factors is probably orders of magnitude in size. Even the IVF, which is supposed to factor in the seemingly obvious skew factor, empirically does little better on out of sample data than constant volatility! The depth of a manager’s understanding of each of the factors that go into that manager’s model is probably one of the big differentiators of quality among investment managers. Parametric complexity can only go so far before one runs into some serious problems. I’m not entirely sure that deep value investing has such a limit on complexity. The commoditization of many quant strategies may correlate with how parametrically complex the process is.
Ways to Evaluate Analysis:
Temporal Relevance: if one were to decompose the information given in an analysis into its component factors, what is the time horizon for the various factors? Is that time horizon so short that it isn't grasping all relevant information pertaining to that factor, making the analysis not robust? Is the time horizon so long that you are incorporating irrelevant information in your analysis? Keep in mind that the time horizon can be a group of disjoint sets, and is in no way limited to only one continuous time period.
Idiosyncrasy : If one were to decompose an analysis into its component factors, how idiosyncratic are the various factors? Can we get outright datasets for the less idiosyncratic factors to improve the robustness of the analysis? Might it be possible to create 'fuzzy datasets' of our own, using pre-specified classification rules and a large news database, for the more idiosyncratic variables? Could we make our analysis more "rich" by incorporating more idiosyncratic elements instead of strictly hugging the data? Finally, could we be placing an irrational amount of weight on idiosyncratic elements to make up for a genuine lack of numerical data?
Dimensionality: If one were to decompose an analysis into its component factors, just how many factors are there, and are we weighing our factors rationally? How sensitive is our valuation to each of our assumptions? Did we make sure to construct a full dataset for each factor? If not we again run into robustness issues. In general, the more factors you introduce at the same time, the greater the risk that you are witnessing a random permutation which just so happened to fit a relevant back-test.
Orthogonality: In our analysis, are we using the same one general line of reasoning the whole way through, or are we calling on a large number of distinct and unrelated factors (or sources of information for those factors)? The more unrelated our factors and their sources are, the better, because that means our conclusion is less sensitive to any one factor or source and our inferential ability could be higher.
Popularity: If you've found a factor which is important that other people either don't know about, or are weighing improperly, you have much to gain. However if everyone knows that your factor is important, then that factor ceases to be useful as an input for inferential purposes—the update happens too quickly. Once that happens you would then need to find factors which have inferential power over the original factor which other people either don't know about or are weighing improperly. So an important question to ask yourself becomes—if one were to decompose an analysis into its component factors, how popular are those factors? For the more well known factors, are their values themselves predicted using other factors which are perhaps less well known? For the less well known factors, are we sure they aren't less well known for a reason?
A Factors-Based Approach:
In the same way that two orthogonal unit vectors can form the basis for 2-Space, perhaps a substantial number of factors of various forms can form a basis for our prediction space. A factors-based approach makes some theoretical sense to me because the factors you create can be used over and over again for different predictions. Factors can also be used to predict other factors, should your original factors become very popular. I suppose the key driver of this approach is the belief that everything is interconnected. You will also probably end up as a market historian as well as a mathematician—I think the skill-sets of both complement one another. Btw, I would be interested to see that proof on high dimensionality without overfitting. Unless the explanatory variables are completely unrelated to one another on at least a couple levels, it doesn't make intuitive sense to me why I should be able to avoid overfitting.

Indexation

2005-07-14T09:51:00.000-07:00

Indexation:
Arnott is a smart man. Finally got a chance to read through his unabridged articles and they contain some real gems. For instance, this is something to really sink your teeth into: "An efficient market in the pricing of individual assets, with pricing errors relatiz}e to true fair value, requires an inefficient market in the capiveighted indexes—and vice versa."
If one makes the assumption that there is some-- any-- deviation of stock prices from their true value, then it can be shown mathematically that market cap weighted indices will underperform other methods.
Interestingly enough, I'm going to see if I can take an opposing viewpoint to Arnott. I am not quite so sure that we necessarily must give up a valuation-based weighting schema for indices, unless the empirical mean reversion in the valuation component of company valuation is super duper strong. I think there might be ways to construct a more efficient index under this alternative schema.
I'm sorry if this entry doesn't make sense. Ask and I'll send the reference papers. One word of warning though, they are slightly complicated. Below is the commentary I've written up thus far.
----------------------------
Rob Arnott wrote an interesting paper in the March/April edition of the Financial Analysts Journal and I think he brings up some good ideas. I had some thoughts regarding his paper and a possible extension.

Arnott adds another layer of insight into the question of just what 'the market return' really is in finance literature. He cites CAPM as a somewhat valid economic theory which relies very heavily on what the assumed market return is. Some people posit that it's the S&P 500 return or some weighted average of past S&P 500 returns. His paper and subsequent articles dig into why some current assumptions (and Bill Sharpe!) are a bit silly. This is an important question because if the assumed market portfolio differs from the real market portfolio, our risk/return benchmark statistics will all be wrong, which is a bad thing.

Here are a couple key points I got from the paper, which may or may not be 100% accurate (please let me know if anything doesn't sound right).
To be completely theoretically accurate, the CAPM market index should be a measure of (1) the fair value of (2) pretty much all things which are able to be invested in, so that it's representative of the market as a whole. Finally, the CAPM market index is by definition mean-variance efficient.
Under the above definition, the S&P 500 return is a fundamentally flawed measure of the market return on two levels. First off, the S&P doesn't satisfy (2) above: "the simple fact is, the capital asset pricing model works if your market portfolio spans everything: every stock, every bond, every house, every office building, everything you could invest in on the planet including human capital, including the NPV of all your respective labors going into the future. There's no such thing as an index like that, it doesn't exist. So right off the bat you can say that the S&P 500 is not the market, and anyone who says that it's efficient because it is the market is missing the point: it's not the market." Second and perhaps more importantly, the S&P also doesn't necessarily satisfy (1) above either, because the S&P 500's weighting of stocks by capitalization maximizes the chance of having a 'fair value flawed' portfolio. Unless one believes that stocks do not fluctuate (even randomly and unknown to investors) from their fair value sometimes, then it can be more or less proven that cap-weighted indices will underperform over time. Below are a couple important points.
Price inefficiency, or deviation of stock prices from fair value, need not immediately suggest arbitrage! Suppose we merely know that some companies are overvalued and others are undervalued. We have no simple way to trade away this idiosyncratic noise in prices because we do not know which stock is currently overvalued and which stock is undervalued. Therefore this assumption is not all that crazy.
The cap weighting return drag thesis in my mind comes down to the following really cool statement: " An efficient market in the pricing of individual assets, with pricing errors relative to true fair value, requires an inefficient market in the cap weighted indexes—and vice versa."
The cap weighting return drag thesis may also at least partially explain the relative outperformance of value over growth, and of small caps over large caps.
It is valid to create a market portfolio indexed by more "fundamental" metrics instead of market cap if the alternative metrics can in some way remove the biases inherent in cap weighting schemes without introducing new risks or problems of their own. For example, one may weight stocks by some measure of historical free cash flow instead of market cap.
The kicker: a very robust sampling of 'more valid' valuation metrics consistently outperforms the S&P 500 in both returns and in risk control!

A number of points merit mentioning before I bring up one possible extension.
A market index is more 'valid' as a suitable CAPM market index if it can correct the S&P by underweighting the components which are most statistically likely to be overvalued and overweighting the stocks which are most statistically likely to be undervalued.
Rydex's equal-weight S&P touches in some way on this notion, but not entirely. Equally weighing all S&P components corrects for some of the bias inherent in the S&P's cap weighting, but it isn't perfect because it is in some ways blind to the "real" value of the S&P stocks.
Arnott brings up some interesting statistics regarding the performance of highest market cap companies as well as the performance of the top 10% of all companies on the basis of market cap in the S&P 500, and finds that there is a large and seemingly statistically significant probability that they will underperform the overall market over most time horizons. More than that, the expected underperformance is really big. He calls this a 'return drag,' and implies that this is what is causing the problems for cap weighted indices. He then offers valuation-agnostic metrics as a cure to the problem.

My possible extension:
Going Deeper Into Some of the Implications
Below are some of the implications which I think might be important but weren't satisfactorily covered in his articles (in my opinion at least).
First off the return drag he mentions is very striking but isn't 'clean' in my opinion. Market cap can be broken down into a more fundamental component like earnings or EBITDA and a multiple component like PE or EV/EBITDA. To say that high market cap implies mean reversion over some time horizon is equivalent to saying that high (PE and/or earnings) or high (EV/EBITDA and/or EBITDA) implies mean reversion—so which piece is it? To be cleaner, wouldn't it be better to analyze each component individually? Is it the earnings which mean reverts, or the PE? I bring up this specific way of decomposing market cap because Arnott's proposed solution implies the multiple component of market cap is the dominant mean reverter, because it is clear that all he is doing is weighting entirely off of the more fundamental component—earnings, EBITDA, cash flow, head count… these are all fundamental components. So why not do out the actual statistics on how much mean reversion there was in the components??? Doing them on market cap is nice looking but it isn't focused and 100% relevant to his proposed solution.
It seems that Arnott doesn't really attempt to correct for the implied mean reversion in the multiple. Instead, he throws out multiples completely and uses valuation-agnostic metrics, stating that his indices outperform the S&P. Does all this imply that it is not possible (or not economically worthwhile) to correct the market index for mean reversion in the multiple component? Where are the numbers?

So overall, I think that Arnott's papers are amazing and house some really crucial ideas. I also think that his proposed solutions are great, and correct for a lot of the inherent bias in the S&P 500. Furthermore the strategies used are extremely simple, which adds to the credibility of the underlying theory.

But that being said, it seems like he might be jumping the gun slightly by throwing out all information contained by multiples. By going straight off fundamentals, he seems to imply that multiples are irrelevant. But this is an inherent contradiction with his findings on market cap mean reversion. For example: suppose you have two companies, A and B, spitting out the same free cash flow this quarter. However A is valued at 50 and B at 150, implying that the multiple of FCF is higher for B than for A. Arnott's schema considers these two companies equal to one another. But Arnott himself admits that there is mean reversion in the multiple, so we could adjust for that by allocating more to A and less to B.

Thoughts on Quantitative Trading

2005-07-14T09:50:00.000-07:00

Being able to identify homogeneity in the financial markets seems to be a driving concept when doing quant trading. Classification and homogeneity are two sides of the same coin-- if all securities in the financial markets were unique, all being driven by uncorrelated processes, it seems that you're shit out of luck. A useful classification is able to identify things which tend to trade the same way-- and of course when two things trade the same way, a proper long-short of the two leaves you with a nice stationary, mean reverting process (this, by the way, is the essence behind cointegration-optimal hedging and indexing). So let's assume for a moment that the goal is identifying homogeneity in some way, shape or form in the financial markets. Where the hell do you begin. I believe you begin by making the decision of whether or not to adopt an inclusive or exclusive paradigm. The inclusive paradigm, which seems to be the most popular (perhaps because it relies on the least granular information?), is to identify very broad trends in the market. For example, there may be tens of thousands of stocks trading right now, but if I were to bucket them into capitalization-based deciles, trends begin to form when looking at one-year-forward expected returns. In other words, broad-based homogeneity begins to surface. At that point, we may attempt to identify what we consider to be "the next best classifier," which would then split the deciles into subdeciles, each of which is then even more homogeneous. I bet a lot of people have made good money adopting this paradigm, and to be honest, it's the paradigm I personally have had the most experience with up until this point. But inclusive classification has many downsides which aren't entirely obvious. First of all, the sometimes extreme level of broadness makes it all the more difficult to identify what classifier is indeed the 'best'. Second of all, inclusive classifications tend to carry with them longer time horizons, which aren't necessarily able to be traded on by desks or funds which need strong enough mean reversion to ensure them a decent probability of success over shorter time intervals. That being said, there are some serious benefits to a proper long-short-based inclusive classification trading strategy. Most notably, the broader the set of stocks involved in the long-short, the less exposed you are (obviously) to the idiosyncratic risk which is so prevalent in equities. Maybe in equities, the nature of equities' idiosyncracy makes this the best paradigm to choose. But the same isn't really true of more quantifiable securities; especially fixed income securities. Take municipal bonds, for example. While it may be conceivable to construct a broad trading strategy around municipals, a ton of polluting factors make things more difficult. First of all there is the issue of liquidity (this actually exists with equities as well). Two securities may look the same and be structured in the same fashion, but if one happens to be less liquid than the other, the more liquid security in an efficient market should demand some sort of a premium. This would then require quantifying the bid ask spread. But that is a classification nightmare in and of itself, if one makes the assumption in the first place that there is some way to quantify it (and yes, there is). Next take the fact that bonds can be issued in any number of states, have all sorts of varying call provisions, bond types (ie. revenue, GO, double barrel, water and sewer, credit rating, insurance, ..., ). It's a fixed income instrument, but it has quite a few idiosyncratic elements. Broad categorizations inevitably fall into the trap of being too general. So rather than pursue the inclusive paradigm, the paradigm then becomes that of exclusion. That is, find on some truly granular level those securities which tend to be homogeneous in some fashion. Then (as long as your dataset is granular enough), peel off the layers of idiosyncracy from your generic set to other sets, quantifying the various credit spreads which should be applied relative to your reference rate (in the case of municipals, the municipal curve). It's interesting that these paradigms are so vastly different from one another. It's also interesting to contrast these lines of thought with that of value investing. Value investing seems to thrive on the idiosyncracy of individual stocks. And yet that is what in some ways kills quant strategies.

Information Flow (April 24th 2004)

2005-07-14T07:21:00.000-07:00

Information Flow is an extremely powerful and important concept. There are many forms. We absorb and give off information constantly. Being in the "inside circle" is simply being in near a valued information source.
To become powerful is to find and hang around good information sources. Most industries are almost completely information run. If you are "in the know," it is infinitely easier to be at the top. Take advantage of asymmetric information. Leverage it. Harness it.
Information dissemination can bring people up and tear them down. The world runs on information. With a proper information distribution system, some things are inevitable. Bush may experience this firsthand in the coming months.

CAI analysis (Snapshot trade on May 29th 2004)

2005-07-14T07:18:00.000-07:00

Well guys I'm putting on my value investor hat, so watch out... today I'm looking at CACI (ticker: CAI). First I go through who they are and the general industry they are in. Then I look at trends and bring them into perspective in light of recent events.
To put it simply, CAI is a contractor for the government. The company is among the largest government information technology contractors, providing a wide range of services including systems integration, network management, software development, and engineering and simulation services. CACI also develops marketing software and databases for sales tracking, demographics reporting, and other market analysis applications, and it provides debt management and litigation support services. Contracts with the US Defense Department account for about 64% of its annual revenues.
While on the topic of revenues, lets take a look at the numbers. Sales is growing.
The story is pretty clear: topline growth because of the war in Iraq-- new contracts.
Growth is expected to continue going forward. CACI has plans to reach $1 billion in revenue in 2004 so it can better serve large clients such as the Department of Defense, which is increasingly trying to do business with a smaller number of large contractors. The company plans on joining that select group by growing through acquisitions and by making technology services a high priority.
Two of CACI's main clients are already part of the Department of Defense: the Defense Information Systems Agency (DISA) and the US Army's Communications-Electronics Command (CECOM). CACI also holds a significant GENESIS II contract with the United States Army Intelligence and Security Command (INSCOM). In addition, civilian agencies such as the Department of Justice (CACI staffs its litigation support services and maintains an automated debt management system), the Department of Veterans Affairs, the Securities and Exchange Commission, the Space and Naval Warfare Systems Command's Naval Tactical Command Support System, and the US Customs Services drive a large portion of CACI's revenues.
CACI acquired intelligence contractor Premier Technology Group, Inc., or PTG, for an undisclosed amount in mid-2003. PTG had revenues of $43.4 million in 2002. Most of its 360 employees hold high-level security clearances and are experts in intelligence analysis, information technology and security services, and logistics. CACI picked up some of the juicier government contracts through this acquisition.
Growth catalyst: Military aptitude (or lack thereof). I like CACI's industry, because frankly I don't see war going away anytime soon. However this fact by itself wouldn't mean much for government contractors. What makes contractors promising is the fact that government needs their help. Since the gulf war the government has been steadily increasing the amount of non-military related outsourcing it performs to: (1) cut costs, and (2) make up for the general diminution in military strength. (1) is highly debatable, especially in light of wasters like Halliburton and KPR . However trend (2) is real. So in come companies like CACI, responding to the increased demand for their services.
Present situation:
So now we zoom to the present. The company came under fire in early 2004, when CACI employee Steven Stefanowicz, who worked as an interrogator at the Abu Ghraib prison in Iraq, was accused of participating in the abuse of prisoners held there. CACI manages various facilities for the US Army under a blanket purchase agreement inherited when it acquired Premier Technology Group in 2003. When the scandal broke out in May 2004, various government agencies, which together oversee the contract, launched an investigation to determine whether CACI should continue to assist the US Army in placing new interrogators in Iraq. The results of the investigation will determine if CACI will be able to fulfill and collect the approved $66 million funding for the blanket purchase agreement. The company had collected $16.3 million when investigations started.
Now from what I've read, the stir over Stefanowicz isn't the issue, CACI isn't vulnerable over this. It is vulnerable because it had an IT contract with the Interior Department, and yet ended up in Iraq doing interrogations for the Army. Does IT have anything at all to do with interrogations? Frankly, no. Technically, what CACI did was illegal.
So what is going to happen?
-- First, hypothetically lets say that CACI loses its government business-- is there anyone who could replace them?
Were CACI to be fired, from what I’ve read it would be unlikely for other companies to enter into the interrogation line of business too easily. Titan, another of the big interrogators-for-hire, was also implicated in the Abu Ghraib snafu, so I doubt that they would be eligible as a substitute. Other companies could substitute CACI’s other legal contracts though, if the government decided to take away all of CACI’s contracts.

This is the other thing that was passing through my head as a read some of these articles—people were surprised as hell at the sensitivity of the tasks that private contractors like CACI were doing. Contractors for the gov’t routinely provide little information on the nature of the contracts it has with the gov’t, and sometimes can’t even acknowledge that certain contracts exist. This means two things: (1) There is good potential hidden upside for the private contractor industry, in the form of classified contracts, and (2) the companies which are now working with the gov’t on sensitive activities (ie. Interrogation) will naturally obtain classified information. The obtained classified information should create some good lock-in with the gov’t for future deals, and general leniency should the companies go on trial. You don’t want a large number of private companies to be walking around freely with classified information.
--Could the military take over what CACI employees were doing?
While the gov’t could possibly try to replace private contractors with army guys, it’s unlikely. The army is coming to the private companies and not the other way around b/c of (1) big need to keep costs down and be more efficient and (2) lack of manpower, general weakness of the military. (1) is debatable and might not leave CACI in a defensible position. The company is making a shitload from those guys. However trend (2) is here to stay. The government needs help badly, that’s why it’s outsourcing so much. I also think this may push the trial towards the lenient side, especially in light of the above point concerning CACI’s current stockpile of classified information.

-- An extra little tidbit:
One of the articles I read mentioned something interesting—“due to a loophole in prosecuting law, it may be difficult to pursue the contractors in court. The 2000 Military Extraterritorial Jurisdiction Act applies only to Pentagon contractors.” CACI is an Interior contractor, so some of the more snappy laws don’t apply to it… could come in handy.

I see Martha Stewart-like politics being the biggest potential downside risk for the company.

Short term: In general I see a 5% up movement as pretty darn reasonable, even assuming some shit does happen. There’s a general consensus among analysts that the recent situation shouldn’t affect the company’s long-term prospects, which gently implies to me that this downtrend might not carry its own weight for an extended period of time (the stock has dropped around 14% in teh past two days alone).

Long term: I see growth here as long as they don't lose business in Iraq.

Oh, and the chief executive bought $380k of the stock the day the stock initially went tumbling down, at 38.306. He increased his holding by 33%. It's now at 37.14.

My two cents.

Decomposition, Applied. A Few Thoughts

2005-07-14T07:17:00.000-07:00

I was poring over a set of 10K’s and 10Q’s to prepare a financial model when I started thinking about how inadequate it was. Many if not most of my thoughts are… imprecise and inaccurate. Thoughts have a tendency to carry a lot of underlying thought baggage.

Obviously the point of a financial model is to forecast a company’s performance going forward, usually for the next year or two. So here I am examining the company’s trends associated with its revenue, cost of sales, SG&A… that’s all of fairly limited value.

Thoughts should be decomposed. Revenue goes up and down, but revenue is an umbrella term which is simply the stitching together of multiple underlying revenue streams. The underlying revenue streams are of course the company’s various lines of business. In my case the company had three main segments: ambulance sales, bus sales and construction truck sales. However even these individual revenue streams can be decomposed. The ambulance segment for example is simply a stitching together of a bunch of ambulance product lines. Doing this drill one more time, each of the various product lines can be decomposed into all the factors which affect the product lines.

So now instead of looking at one number, “revenue,” we’re looking at, say, 250 numbers, each of which represents the various factors which affect the sales of the various products the company sells. Imagine repeating the same drill for cost of sales, SG&A, depreciation and amortization, … you can see how assessing the performance of this company is actually very, very painstaking.

But now we can finally start thinking for real. How will interest rates affect this company’s business? Well since the company manufactures its own chassis for its ambulance, bus and truck lines, one can get started by assessing how much interest rates will affect each group individually. How will a change in the value of the dollar affect the company’s position? Well a good portion of the company’s construction truck business comes from exports, as does ambulances, but to a lesser extent. Buses aren’t affected very much at all by such matters. You can see how each of the stimuli is being systematically thought through on this underlying level. Each of the stimuli affect the factors, which come together to affect the products in the product line, which come together to affect the product segment, which come together to affect the company’s revenue.

So you want to forecast the company’s earnings for the next four quarters? I’d start by seeing how each of these decomposed units will be affected by stimuli. Then I’d look for trends in the data which may point to the occurrence of some stimuli over others. Such as the coming rising interest rate environment. I’d work out how changes in the value of the dollar will affect the company. I’d examine how much competition there is in each of the segments, and if it will perhaps crunch that unit’s margin going forward. Is the company’s position pretty defensible? So lets say it is, and lets say you assume that unit is on a certain growth trajectory. Great, now what.
What will happen to cost of sales in this case, and SG&A? Which costs are fixed and which are variable? In other words, how much sensitivity will the costs have to the top line growth?

In other words, we’ve gone from examining a company at one instant in time, to examining a company in motion. With motion comes the notion of stationarity. But that’s a whole other story.

For now, I’d conclude by saying this. It’s good to think about very high level concepts. It’s just that a lot of thought is necessary to break down the high level concepts, so that one can think and assess the concepts accurately. The first step to understanding complicated issues it to acknowledge the issues as complicated in the first place, and to begin decomposing.

For myself, I can’t forecast where I’ll be in a year because I not only haven’t thought about it properly, but also there’s a lot of it which I can’t control. One low level concept I do know, though, is that adequate sleep will leave me feeling better rested and alert than inadequate sleep. And I need to be alert tomorrow so that I can finish that model.

Intellectual food for thought, major notions proposed in Fooled By Randomness, a good book.

2005-07-14T07:14:00.000-07:00

--We are subject to hindsight bias--the "I knew it all along" bias--so that past events will always look less random than they were. While discussing our past, for example, we tend to backfit explanations concocted ex post. We create reasons for why certain events occurred ('this happened for so and so reason, b/c I was this and that, which led to...), choosing (subconsciously?) not to accept the fact that chance may had at least some part in spurring on the event. Were we successful because of skill, or b/c we were lucky?
My solution: before making a decision, write down what decision you are making, and most importantly write down why. Write down what you would do under the various possible future states of nature, and if the future surprises you in some unexpected way, sell, get out, write down what surprised you and why. See what effect that surprise has on the stock, and attempt to incorporate that new possibility into your next decision. It is hoped that the reasons why you made that decision, if correct, will cause you to reach your expected conservative estimate of what will happen in the future. If they don't, then perhaps you will need to readjust your expectations on a more fundamental level.
--It is difficult to apply probability to real life-- unlike a gambler at a roulette table, mother nature doesn't tell you how many holes there are on the roulette table, nor does she deliver problems in a textbook way. In the real world one has to guess the problem more than the solution.
My solution is to make an active effort to conjure up the various possible states of nature, and then to assign probabilities to each. Erring on the side of caution is probably the best course of action. This notion is analogous to Graham's margin of safety.
--Risks are not good in and of themselves-- An example of naive empiricism-- the author of "the Millionaire Next Door" looked for traits that many millionaires had in common and figured out that they shared a taste for risk taking. Clearly risk taking is necessary for large success-- but it also necessary for failure. Had the author done the same study on bankrupt citizens, he would certainly have found a predilection for risk taking.
At the lower level, it is clear that risk in and of itself is not good. The effectiveness of risk is a function of the downside risk, the upside potential, and the underlying probability distribution for the various states of nature. So for every decision we make, there will obviously be only one final outcome (ie. I have made a profit of $1,000 off of that trade). However that final outcome means very little-- it is the product of only one state of nature. The "real" outcome of that decision is the sum of the outcomes under all possible states of nature, weighted by their respective probabilities discounted for volatility. An interesting point, though, is that it is easier to think of possible states of nature ex ante-- after the event has taken place, it is very very difficult for us to look at the past and examine all the other things that could have happened.
At the higher level, 1) we need to look not only at the characteristics of winners-- we also need to look at the characteristics of losers. 2) 'Necessary' qualities of winners does not imply causality, that certain qualities will necessarily cause winners to come about. 3) Finally, it's probably a good idea to get "the whole picture" before coming to a conclusion regarding the effects of certain qualities. Before I make the assumption that risk-taking is a good thing, I would like to examine its correlation to everything which has an impact on me. I would examine its correlation to financial success and failure, as well as emotional side effects. Finally, I would seriously consider whether risk-taking is right for a person like me--given that I am who I am, given that I have certain personality traits, how do the odds of financial and emotional success and failure change?
--Our assessment of risk is flawed even on a fundamental biological basis-- it is a fact that our brains tend to go for superficial clues when it comes to risk and probability, and these clues are largely determined by what emotions they elicit. Both risk detection and risk avoidance are not mediated in teh "thinking" part of the brain but largely in the emotional one (the "risk as feelings" theory).
This is a bit harder to control for, but to know of this fact's existence alone is a good thing. All we can do is strive for objectivity in our decision making and risk assessment processes.

Thoughts on Fooled By Randomness

2005-07-14T07:12:00.000-07:00

"I believe that the principal asset I need to protect and cultivate is my deep-seated intellectual insecurity. My motto is "my principal activity is to tease those who take themselves and the quality of their knowledge too seriously."" --Nassim Taleb
I begin with a look at black swan dynamics.
A black swan is an outlier, an event that lies beyond the realm of normal expectations. Most people expect all swans to be white because that's what their experience tells them; a black swan is by definition a surprise. Nevertheless, people tend to concoct explanations for them after the fact, which makes them appear more predictable, and less random, than they are. Our minds are designed to retain, for efficient storage, past information that fits into a compressed narrative. This distortion, called the hindsight bias, prevents us from adequately learning from the past.
a)
So for starters, because we have a tendency to consider many elements of our past with more causal links, more narrative attributes, and less randomness than had actually existed at the time, we effectively are doing two things. One, we are tricking ourselves, because there are many circumstances in which the causal links we 'create' are simply false-- they aren't actually there. Therefore two, because there is a disconnect between perception and reality, we are misjudging what we know. In other words, we are misjudging the quality of our knowledge. This is one aspect of Taleb's statement.
b)
"Lucky fools do not bear the slightest suspicion that they may be lucky fools-- by definition, they do not know that they belong to such a category. They will act as if they deserved the money. Their strings of successes will inject them with so much serotonin (or some similar substance) that they will even fool themselves about their ability to outperform markets (our hormonal system does not know whether our successes depend on randomness)... Scientists found out that serotonin, a neurotransmitter, seems to command a large share of our human behavior. It sets a positive feedback, the virtuous circle, but, owing to an external kick from randomness, can start a reverse motion and cause a vicious circle. It has been shown that monkeys injected with serotonin will rise in the pecking order, which in turn causes an increase of the serotonin level in their blood... Randomness will be ruled out as a possible factor in their performance, until it rears its head once again and delivers the kick that will induce the downward spiral."
The point is that random events, good or bad, have a suboptimal effect on our behavior which we cannot control. Because our bodies have great difficulty differentiating between a good stimulus having been caused by our own ability or by randomness, we unconsciously pump ourselves up when there is no logical reason for it. Undeserved confidence in one's abilities is dangerous. Our perception of our ability to perform certain acts changes to incorporate the newfound confidence. Thus when we perform those acts the next time, we subconsciously increase the odds with which we will be believe we will be successful at what we are doing, when in fact the odds should remain the same. In essence, we have subconsciously increased our perceived self-aptitude at performing that task-- we have subconsciously (wrongly) increased what we believe our knowledge level is. This point is another angle at which Taleb discusses the 'quality of knowledge' in his motto, because again there is a disconnect between personal perception and reality. It also highlights the correlation between the seriousness with which we treat ourselves, and the quality of the knowledge we possess.
When making decisions which require us to make intelligent guesses at what will happen in the future, it might be helpful to keep a few things in find. The first have to do with the points mentioned above-- any source of information or any assumption we lever to make a decision should be vigorously tested for its quality and validity. We should only very reluctantly believe anything as being a certainty, simply because there are so many cards stacked against our making an optimal decision. We shouldn't overly rely on past information, because each event which happened in the past was one of countless other possibilities.
Counterintuitively, the most important test of whether or not our decision was indeed a valid one is not what ended up happening as a result, because that result could have been one of any number of other equal or more possible other results. The most important test is how intelligent that decision was in the face of all information that one had at that point in time-- the optimality of that decision is a function of its expected value, factoring in all other possible future states of nature, and its variance when compared to all other decisions one could have made at that point in time.
To shine some light on this, consider the game of russian roulette. Lets say that a man hands you a revolver with one bullet in it. The rules of the game are as follows-- if you fire the gun at yourself and do not die, he will give you a million dollars in cash. If you do die, well, sorry. Lets say the man accepts, fires the gun and gets lucky. He is left with a million dollars, but his decision was obviously not the optimal one. We know that because we know all the rules of the game; the risks, the payoffs, and the underlying probability distribution. [The thought process going into the decision is called the 'generator.'] The whole point is that in real life, we don't know all the rules of the game. We don't know all the risks, the payoffs, and the underlying probability distribution because there are so many factors involved that it would be impossible to incorporate all relevant variables into a rational, optimal decision. So when we look back on historical events, we see the end result (the man getting the million), but we almost never are able to see the generator. Without knowing all the other alternative histories, we must be very very careful when making a decision now because it worked some times in the past. We can, and often will, end up with the metaphorical bullet in our heads because of an over-reliance on past data and an underappreciation for the only thing which really matters-- the generator, the search for the rules of the game.
One final thought while on the subject of using past data to draw inferences on the future: survivorship bias. Survivorship bias is another reason why we must be very careful when drawing on past data. I'll go back to the example of the authors in the book "The Millionaire Next Door" citing that risk-taking is a common quality shared by many rich people. The authors then imply that risk taking is a good thing; it is something we may need if we want to become rich ourselves. The natural reaction when testing this claim is to get an estimate of how many people are risk takers, and how many of those risk takers are rich. We can then reach some conclusion about the probability that a risk taker will end up rich.
There is one flaw to this measure of probability though. What it doesn't take into account are all the people who were risk takers, but through their excessive use of risky policies, went bust and cowered away into obscurity. All the real losers won't be taken into account in our statistic, because only the winners have survived up until this point.
One can see that the proper way to run this test would be to find all the people, rich and poor, who are risk takers today, and see where those very same people are in 10 years time.
But we can't really do that with history or historical information. Do most risk takers end up rich? Or did all the risk takers who didn't make it go bust, so that there weren't many "losing" risk takers left by the time the test was done?
The whole point is to be very careful about what you consider to be true. Chance plays a larger part than we tend to think, and there are numerous biases we have and we are subject to which will cause us to be swayed from what is indeed true.

More to come on black swan dynamics, which are profoundly interesting.

HTV-- the next MWY? (Aug 30th 2004)

2005-07-14T07:11:00.000-07:00

The essential characteristics of MWY’s story—

The company was in a losing position. It had been losing money for 5 years, and wasn’t well followed.
Sumner Redstone had been buying for 9 months, raising his stake in the company from 30% to 75%. He decided to buy all of his shares in the open market instead of tendering, which was odd. His actions only recently really became very noticed—looking into a database of news articles, it seems the first was written in April. So five months of buying went without much mention. Finally, looking to the transcripts of the conference calls, the CEO of MWY was very reluctant to shed any details. In the Q4 earnings call (which was held in February) made no mention of Redstone. Even in the Q1 conference call, when people asked questions about it, the CEO provided no answers at all. The price had gone from 2 in November to a high of 13 in July as a result. This move has inflated the company’s valuation metrics above the industry average (MWY’s EV/EBITDA is 22 while that of its peers is in the mid teens). It has also brought in the short sellers, because the fundamental underlying value of the company is undoubtedly lower than the current value. Short sellers now account for approximately 30% of the outstanding shares.
To take the company private or do a related transaction, Sumner needs 80% or more of the company’s stock. He seemingly reached this point, but in a surprise twist, the company disclosed that it had another 12mm more shares than it had previously disclosed. So he continued to buy.
HTV

Financials
Not much top-line growth at all. Revenue has been about the same for 5 years in a row after a ramp up period (1999 to 2003, in $mm): 661.386, 747.281, 641.876, 721.311, 686.775. Seems sinusoidal, doesn’t it?

However margins have improved dramatically over the past 2 years. After a multi-year period of low profit margins [(1999 to 2001, $mm): 4.89%, 6.01%, and 4.84%], margins jumped to 14.98% in 2002 and 13.72% in 2003. In general, margins are very nice right now. The EBITDA margin is at around 50% because HTV has very low operating costs.

Debt has gone down by about 25% over the past two years or so. Finally, the cash position has been rising steadily. Two years ago, the company only had 4.359mm in cash. Starting a year ago, it started to add to its cash position very heavily—5 quarters ago, cash stood at 7mm. It then tripled to 21.85mm, then tripled again to 71.53mm. Since then the cash position growth has leveled off, jumping to 116.7mm two quarters ago and 138.1mm last quarter.

Company Analysis

General
Hearst-Argyle Television is one of the country’s largest independent, or non-network-owned, television station groups. It manages 27 television stations reaching approximately 17.8% of television households in the US.

This is how the company makes money. The company gets the rights to broadcast certain TV shows and news reports on the channels that it owns. A large number of those channels which HTV owns are local. So it puts on its shows and tries to get people like us to watch them, which is of course contingent on a number of factors, but the main ones which the company can control is the quality of the TV shows it puts on and the quality of the news broadcasts. It receives advertising revenue from major companies (25% of its revenue is currently from auto manufacturers, however it also receives a large percentage from retail, some from pharmaceutical companies, as well as political advertising both local and national, among others). Obviously HTV wants as many people watching its shows as possible, because the amount of money and the general demand for HTV’s advertising spots will depend on two things mainly—the demographic watching the shows, and the total number of eyeballs. This accounts for over 90% of the company’s revenues.

Programming
It gets those programs in a number of ways. (1) It sets up agreements with networks like NBC and ABC to broadcast those network’s shows in exchange for the right to sell some of those ad spots. This is called a network “affiliation agreement.” It’s a symbiotic relationship. These transactions are called “barter and trade transactions.”

(2) It must compete for non-network programming, which involves haggling with national program distributors or syndicators that sell the types of shows which HTV buys, called “first-run (like the Oprah Winfrey Show)” and “off-network” packages, and “off-network reruns (like Seinfeld).”

HTV’s competitive advantage is its strong news offerings. Lots of people watch them apparently, as they are highly ranked and command a premium for the ad spots. HTV is weak during prime time. So its ad spots are much cheaper there because of the lack of demand.

Competition
Cable-originated programming is becoming a bit more prevalent. Made-for-cable programming has been gaining market share over the past year or so. This is an alternative to HTV’s broadcasting.

Also, of course because HTV touts itself as one of the largest non-network-owned TV station groups, networks are competition. HTV currently has network “affiliation agreements” with most of these companies and has had the agreements for a while now, but there is no saying that the networks won’t turn on HTV if the payoffs were right. The terms of the affiliation agreements was stated above—HTV gets network shows, network gets the right to sell some of HTV’s ad space. It seems like the networks have been playing off this point already. They used to compensate HTV for the broadcasting of network programming—but this is coming to a halt. Also, there is no saying that the networks won’t cut off agreements once the contracts run out.

Direct broadcast satellite (‘DBS’) programming is the final threat, which I think has the potential to be the worst. EchoStar (DISH Network), and DIRECTV, which transmit programming directly to homes equipped with special receiving antennas, bypass HTV entirely. Those customers don’t watch HTV’s broadcasts, they watch the dish network’s.

Cyclicality, Seasonality
Highly cyclical business. A quarter of the company’s revenue is coming from auto manufacturer ads. Things may be swinging in HTV’s favor right now, but that is not to say they won’t in the future. I get the impression that a recession would hurt this company very badly.
HTV is also seasonal. It has seasonally stronger first and third quarters. It also experiences higher sales in the even years because of political elections and the Olympics.

The Current Situation
The company is doing well, aided by double seasonal effects and a semi-cyclic upturn. It is an even year, it was the first quarter, and auto manufacturers have started to increase their ad spending to market new model cars. The company says organic growth will be driven by macro-economic factors, which leaves a bad taste in my mouth.

The last quarter basically had a revenue increase of 18mm on the prior year. 9mm of that was from political revenue (seasonality). The other 9mm can be further broken down into 5mm which was lost last year due to the Iraq war. So this means only 4mm came from the company’s core business—and remember, most of that must have come from the auto segment. There’s some doubt that the company will be able to keep its costs in check, sonsidering how much it may have to spend to provide adequate coverage of the political events coming up. In general, I am not impressed with the underlying fundamentals of the company and think there isn’t an adequate downside risk for me to make a trade.

I have gone into the “going private scenario” fairly in depth in the document below on HTV’s situation.
Hearst-Argyle’s Situation—

Hearst-Argyle Television (HTV ), spun off in 1997 by media giant Hearst, has been on the ropes since January, when it traded at 29. Now at 23, guess who is buying stock? Hearst itself. Since April, Hearst has bought -- through its Hearst Broadcasting unit -- 1 million shares of Hearst-Argyle in the open market, bringing its total stake of Class A shares to 35%. Hearst-Argyle owns 25 TV stations and manages three others, reaching 18% of U.S. households. The company also manages two radio stations. "It's one of the largest non-network-owned groups," notes Amy Glynn of Standard & Poor's (MHP ). Hearst Broadcasting already owns 100% of Hearst-Argyle's Class B shares, whose holders pick most of the board. These buys have led some hedge funds to buy, too, in the belief Hearst will take Hearst-Argyle private. They note that Cox Broadcasting recently said it would go private at a 16% premium to its stock price. Meanwhile, Hearst-Argyle has also repurchased 180,000 shares this year. Sean Butson of Legg Mason (LM ), who rates the stock a buy with a 12-month target of 35, sees earnings of $1.30 in 2004, up from 2003's $1. Both companies declined comment.

Analysis—
So this company is also on the ropes, as was MWY. The buyer, media giant Hearst, has an obvious direct relation to HTV, which probably increases its chances of being taken private because the integration would probably be more smooth. It would definitely be an odd move for Hearst, because it would be essentially reversing its decision in 1997. It does call the shots on votes though, because of its large ownership of the class B shares. HTV’s share repurchases also shine favorably on the probability of HTV’s being taken private. MWY didn’t have share repurchases, to the best of my knowledge. MWY might have been trading at relatively “cheap” valuation before, but not really. It was cash flow negative with negative earnings and negative EBITDA. HTV seems to be in a better position financially, which I think would be a plus. It has an EV/EBITDA of 8.45 and a P/E of 21.13. It has huge margins. Profit margin is 15%, and EBITDA margin is around 50%. It trades at from 5.5 to 8 times free cash flow, which (at the lower end) is good considering that this company has a market cap of 2.3B.

The underlying situation is better than it was, and the company is currently doing well, but seasonality and cyclicality make this a risky investment from a value standpoint. I’d like a better idea of the environmental situation.

I think this situation looks more similar to what is currently happening at TROY. TROY, a family run company, has owners controlling 67% of the outstanding shares. The stock had been languishing (what a strong common trend!). It is alleged that management is purposefully keeping the price down so that it can take the company private at a low valuation. TROY is a good value, attracting the likes of value investor Whitney Tilson. However it seems like HTV has a stronger financial position than both TROY and MWY. HTV has very wide margins; EBITDA, and profit. It trades between 5.5 and 8 times free cash flow. TROY has a trailing P/E above 40 and an EV/EBITDA of 19. TROY and

HTV diverge on the cash trend also. TROY used up a great deal of its cash four quarters ago, dropping their cash position from 8mm to 1.7mm. HTV’s cash position went from 4.5mm to 138mm. It was a debt laden, low cash, lower margin company with lower revenue. Over the past seven years, it has knocked off some debt (I believe), increased its cash position, expanded margins and brought the top line to a level it has maintained for five years. It indicated in its conference call that this is most likely because it intends to acquire another company, or do a major deal, in the near future. Regulatory issues are holding up HTV’s ability to do anything at the moment, so if those problems get squared away, then HTV will go and act. I don’t like the sound of that. That will knock away their cash position once again, and they will probably be at a higher leverage ratio like before. Also, I’m not sure how this factors into the probability of HTV’s being taken private.

TROY tells a different story though. It wants to take itself private (management is taking the company private, not an external company). Secondly, it is being pursued by Westar Capital, which wants to acquire the company and pay a premium on the price. Cash is well-known to make a company an attractive target of an acquisition, so a logical reason for the diminishing cash position could be as an acquisition deterrent. MWY is interesting. A year ago it had around 60mm. It then burned around 20mm, and had its cash hand at around 40mm until last quarter when it added 100mm to its cash position. It was truly against the ropes. It wasn’t talking about acquisitions, it was simply floundering.

Finally, Hearst filed with the SEC its intention to buy up to 20mm shares of HTV in the open market. As of the end of 2003, it had purchased 19.3mm of the 20mm. After the recent spate of buying, it has 20.58mm. In other words, it has gone above the limit. I am not sure what this could mean, but I would tend to think it means something. This and the industry consolidation trend interest me. The rest of the situation doesn’t. It seems to me like making a trade on this would expose me to too much risk so I’m staying on the sidelines.

A Few Thoughts on the Art of Forecasting, Reference to 'Fooled By Randomness

2005-07-14T07:10:00.000-07:00

To sum up the relevant facts from Nassim Taleb's 'Fooled By Randomness'--
-Traditional statistical inference is severely marred by the presence, the prevalence, and the staggerring effect of rare events, aka black swans. The example I used was an individual picking balls out of a container one by one to determine the underlying distribution of the balls. Knowledge acquisition creeps up so slowly as to be almost useless.
-Try to avoid biases. Trick yourself if necessary. If we get too caught up in "noise" we diminish our ability to see what really matters-- the bigger picture. Try not to take action until you feel you have done your utmost to gather all the relevant information you deem necessary. Once you are finished, take note of what you know, and most importantly, what you don't know. Try not to get 'married to a position'-- if information presents itself later which goes contrary to your thesis, be prepared to toss away your position entirely. Avoid the temptation when in a bad trade to wait until you break even-- admit your mistake and move on. Try to avoid getting sucked into herd-like behavior. Try to acknowledge the randomness in trades which ultimately work out.
-Watch out for deceiving statistics. The example I gave was that risk taking is not in and of itself a good thing.
-When analyzing historical information, look to the generator, not the result. The two are completely distinct. Try to get a feel for what the possible outcomes were and the probabilities and payoffs associated with those outcomes. If there is a terminal probability, a probability that you will lose or die or something equally bad, stop. It will inevitably happen.
So in this context I believe we are in a reasonable positition to make a rational forecast of the future. I believe forecasting is a two part game.
The first part of the game is gathering as much historical information as is possible from as diverse a library of knowledge as you can find, then distilling that information into a usable form and forming an opinion based on that. It is a large scale inference, basically, which can be decomposed into qualitative and quantitative elements. Essentially what one has done is look backwards to project forwards. This is powerful of its own right, and can probably do well over most short time horizons, but it is still very vulnerable to possibly lethal rare events.
The second part to my general methodology attempts to correct for the vulnerability of the first. I would try, to the best of my ability, to flush all historical information out of my head. I would look very hard at the company's business model, industry, and upper management and simply throw out all scenarios I can think of related to anything affecting all three. I would not constrain myself to the realm of possibility or rationality. The whole point is that black swans cannot be thought of rationally beforehand. Once all ideas have been thrown on the table, I would analyze the repercussions of the underlying scenarios. Basically, what are the payoffs associated with those scenarios? I then sort the list of scenarios by increasing payoff and go through the list one by one. I would then look for weaknesses-- how can the bad scenarios actually happen? Then I would find ways to counter the plug the dam-- to counter the weaknesses.
As an example, consider 9-11. It was a most tragic black swan. However because it has never happened to us in the past, it was pretty much out of the scope of normal possibility at the time-- I doubt almost anyone thought such a thing was possible. The whole point is that the ideas are almost by definition deemed crazy ex ante. The second step might be vague, but something along those lines seems to be the only way we can expect the unexpected.
If anyone has any thoughts I'd love to hear them.

Thoughts on a Variant of Information Cascades; Examples; Relationships to Hedge Funds

2005-07-14T07:06:00.001-07:00

When disaster strikes, all correlations go to one. Why look at information cascades only? Situations may cascade as well. For example, private equity firms and many hedge funds frequently use large amounts of leverage. The pain inflicted on them by losses and by rising interest rates is not really a linear one. Once a hedge fund, for example, loses a certain amount of money it will probably need to de-lever to hold steady its target leverage ratio. Where will it get that money from? From its existing portfolio. Should the company need money enough, it doesn’t matter whether the stocks in its portfolio are good or not. The fund will need to liquidate in either case, because it needs the money. Stock sales by one fund will have little impact on the overall market, but if the market turns on a wide enough base of funds (it has a tendency to do that), and a decent number of funds induce selling pressure in an effort to de-lever, the market will decline further. As the market declines further, all those people out there with long positions will again take a beating, and once again the funds are faced with rising leverage ratios—a potential vicious cycle has been created.

Putting on the forensic hat, one can notice similarities between cascading situations. Most notably, cascades form when the support which has fueled your gain will not be there to support you in loss-- cascades typically have weak support. Leverage is a weak support. And there is widespread leverage in the marketplace today, especially due to the prevalence of hedge fund on funds, which have multiple layers of leverage.

It would probably be very profitable to identify situations in which cascades have formed, because these are the situations which have the potential to spiral out of control in a logical, pseudo-predictable manner. They are usually situations which over shorter increments follow a linear pattern, but past a certain threshold, become non-linear.

Adding distinctions to the ACF

2005-07-14T07:06:00.000-07:00

The ACF we use assumes a form of linearity. For example, we can look at the lag one autocorrelation of a stock, assuming that the stock follows an AR(1). The return today in this model equals rho times yesterday’s return plus some epsilon. To calculate that rho we use the method of moments, multiplying the above equation through by yesterday’s return. When we take the expected value, the epsilon term goes to zero. We are then left with the expected value of yesterday’s return times today’s return is equal to rho times the expected value of yesterday’s return squared. For example, for the expected value of the return at time (t-1) times the return at time t, simply take the time t demeaned returns times the time t-1 demeaned returns, sum up over all T data points and divide by T. The ACF doesn’t make any distinction between large price fluctuations and small ones—all data in the return series is weighted by T; no more no less.

Perhaps there is some significant autocorrelation conditional on there being a large price movement, but when the price movement is no longer significant, the autocorrelation subsides, hiding the true value of the autocorrelation under the typical weighing scheme.

It would be interesting to be able to construct some sort of ANOVA test, where one divides up return data into 5 deciles, and compares the autocorrelation function in each case. If the data were truly as it should be, there shouldn’t be much if any statistically significant difference between the various deciles. It would be interesting to see if the 5th is the same as the first. What one can look for—if there is any sort of trend in the autocorrelations as one moves from one decile to the next for a single stock, that would be interesting. Then one could potentially reduce the forecasting error inherent in the prediction. One could also create an EMA, weighted by returns, in the same way that the ‘typical’ EMA weighs by time.

One could look at a large number of stocks and determine if there is any return-size-dependency on the ACF inherent in all stocks in general. If that data provides any sort of information, perhaps it too can be used to augment single stock predictions in a univariate sense. Or it could be used in an ordered univariate sense by looking at all stocks in the market, ordering them by absolute returns in descending order, and making bets on the top few.

I would further distinguish between industries, and determine if there are any sector-specific relationships which differ from those of the industry.

The catch? Cutting up your price series into smaller bits means you’re dealing with a smaller sample size, which means your results aren’t nearly as robust.

Generalized Methodology

In general, to properly gain more insight into the ACF, one should focus on all the various data points which we typically (dumbly) categorize into lags and then average together to form an autocovariance. Instead of averaging them all together blindly, I would take a step back and decide why I am using the damn thing. In the end, what I am looking for is a methodology which generates situations for me in which the autocorrelation between tomorrow’s return and that of one of its prior lags is high. That way, I can make a directional bet for which the probability of my guessing the future return on the stock is higher.

In essence, the ACF should be one of many functions which forecasts future returns using lag dependency as its starting point (certain lags are important while others aren’t) in past return data. The ACF is a one dimensional test in that it makes no other distinctions beyond that of lag dependency.

I would modify the ACF. I would also begin my endeavors by calculating a massive pool of data points-- returns multiplied by lagged returns, as the ACF does. Some data points will be high will others will be low. However rather than simply adding together all the data points which have the same lag relationship, I would add a vector of characteristics to each data point. For example, one cell I would add is volume data. To do so, for each data point, I would calculate the volume which occurred on the lag’s date. I would also add the absolute return on the lag. Perhaps industry makes a difference, so add an entry for industry classification. I would also add what the lag is, which is basically at the heart of what the ACF does. And so on.

I would then take an econometric standpoint. Finding a “good” autocorrelation value from an ACF involves nothing more than taking all of the above data points, indexing them by what the lag is, throwing all data points with the same lag into distinct piles, and averaging the predictive power of the lagged return on the non-lag return for each pile. Good piles have high average values for predictive power. Along the same lines, perhaps returns data has different properties when the volume is high rather than low. Or perhaps tech stocks which experience sharp return shocks on high volume tend to have higher predictive power than non-tech stocks moving on low volume. So I would run tests to find out if such relationships exist. I would first look at each variable in isolation and determine whether or not, all else being equal, variations in that variable have an impact on autocorrelation. I would also look at combinations, for example determining whether or not sharp returns in conjunction with variations in volume have an impact on average predictive power. I ultimately end up with many more piles. Instead of dividing all of the returns into piles categorized simply by their lag, I would aim to divide up all of the returns into piles, categorized by all variables which have a statistically significant effect on the predictive power of a lagged return on its non-lag counterpart. Thus, I would aim to make more distinctions on the data than the typical ACF function, without throwing out the ACF methodology entirely.

Market Dynamics

2005-07-14T07:04:00.000-07:00

Question: What does the issuance of put options to the open market do to the dynamic of the overall marketplace?
Hypothesis: The issuance of puts (1) exerts a stabilizing, mean reverting dynamic on the stock process, and (2) decreases the market implied volatility.
Basis Behind (1): all else equal, the market has bought and absorbed the put options. This decreases the market delta, and increases market gamma, and vega, all else equal. Why? Because the general public is buying from the company and not from other investors. If the company were buying from other investors, the net effect would theoretically be zero. However the general market is buying from the company, bringing those options into existence for the first time. Furthermore it is easy enough to track whether or not the put issuance has been hedged-- without loss of generality we can assume that it is not (or else we would simply retract all hypotheses). Finally assuming that the market absorbed the options with no problems, while it is theoretically possible that they fully hedged their position by longing the underlying, this is highly unlikely. The residual market delta is most likely to be negative.
So now let's think about what happens when the stock goes up. If the stock goes up, the puts get more out of the money, and the delta, which was negative, becomes less so. Assuming that at least some of these investors track their market exposure, some of the bigger investors will see this happen and attempt to re-establish their market exposure. They would do so by shorting off the delta increase. If the market goes down, they would conversely long off their delta decrease. Thus, the market stabilizes.
Basis Behind (2): Let us assume the issuer of the options is MSFT. MSFT has sold put options, so it's vega is negative. The market has bough the put options, so their vega is positive. We know MSFT won't hedge its vega. It should also be a reasonable assumption that the market will not hedge its vega either. We need to look at this from a supply/demand point of view. The supply of options has increased, while the demand can be assumed to be fairly constant- MSFT was the issuer after all. Supply and demand would imply that IV would have to go down as a result.
Also, we can think of MSFT as the initiator of the transaction in this case. The market in this case is nothing more than a counterparty. If that's the case, then even if you make the argument that on a net-net basis the change in vol should be zero (because you have a buyer and a seller at the same time), the fact that it was MSFT who initiated the trade indicates something.
Structured Products, I believe, are part of the reason why implied volatility is down so markedly.

Take-Aways from Blink

2005-07-14T06:48:00.001-07:00

-That initial gut reaction we have when making decisions is a very real phenomenon which merits thought. We tend to not always trust our hunches for a few reasons, one of which is that we've been taught to value caution and thoroughness to avoid making hasty mistakes-- how could it be that we gain more insight from our 2 second initial hunch than the following month's worth of rigorous analysis? The second reason I can think of is the fact that even if we believe in our ability to come to very insightful conclusions through nothign more than a hunch, it can be difficult if not impossible to know whether our hunch is a good one or a bad one-- that split second hunch can be decomposed into good analysis and bad biases, and it can be very, very difficult to differentiate one from the other. There is a partial solution to this. (1) Premeditatively create an environment for yourself miniizing the probability that you'll be reinforcing any biases you may have-- and make sure you only trust your hunches when you are in the proper environment (remember policemen in car chases; high heart rate --> change of psyche). (2) Refine and hone very specific processes-- when situations arise which are a direct application of those processes, it's far more likely that you'll respond with a "good blink." (Remember bodyguards training their reactions to vicious dogs and getting shot-- these are situations in which you can train yourself to remain in control).
-To be truly the best, it may be of value to refine and practice a particular process or drill to the point that it becomes a part of your second nature-- so that when you need do perform or make a decision which requires that skillset, you will be able to rise to the challenge.
-It is very important to classify the decisions you make on the job or elsewhere into how time dependent those decisions need to be. Take for example a cop or a soldier. They don't know if or when they will get tied up in a life or death situation they don't want to be involved in; what they do know is that in that very short time interval, they won't have the time to think things over. One may contrast that to a professional historian or philosopher-- while this sort of person will also have those majorly time dependent situations as well, it isn't nearly as crucial. Another comparison could be made between a quant trader and a cash trader/market maker. Identify those very time dependent situations and hone yourself to perform if/when they arise.
-Make your best attempt to identify your subconscious biases so that you can fix them-- the deeper they are, the harder they will be to identify.
-Many times, insightful analysis comes not from being an expert at already established techniques, but from adopting a whole new schema altogether. Gladwell brought up a couple cool examples of this, like the marriage inspector and the face readers. These are people making inferences based on very off the beaten path, subtle things. Just imagine how helpful it could be to read a person's face when in a face-to-face interview with them!
Face Reading 101:
Paul Ekman: Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage
Fritz Strack: Inhibiting and Facilitating Conditions of the Human Smile: A Nonobtrusive Test of the Facial Feedback Hypothesis, Journal of Personality and Social Psychology
Robert Schultz: Abnormal Ventral Temporal Cortical Activity During Face Discrimination Among Individuals with Autism and Asperger's Syndrome, Archives of General Psychiatry
Nancy L. Etcoff and Paul Ekman: Lie Detection and Language Comprehension, Nature 405
Simply Google Paul Ekman and Silvan Tompkins
Silvan Tompkins: Affect, Imagery, and Consciousness

On Catalysts and Quant Trading

2005-07-14T06:47:00.000-07:00

-The resolution of an investment catalyst should be a carefully monitored function of the time horizon of the investment. You may have a high resolution catalyst or set of catalysts on stocks with a one year time horizon, but when the horizon drops to six months, extremely specific catalysts become all the more important because the time dependency is much higher.
-There may be hope yet for deep value investing with a quantitative overlay. Even though quant thrives in high resolution data over a large universe of liquid stocks, that doesn't mean it can't help you infer a first guess, comparables style, on how much a company should be worth. Think of volatility surfaces-- option writers don't necessarily have super specific valuation models to determine in a vacuum exactly what an option should be priced at in implied vol terms. Rather, they interpolate out the vol surface as a multi-dimensional function and see where the new options fit on that surface-- a glorified interpolation.
Quant, then, can essentially be applied as a more in-depth screening technique which may point you, a person with scarce resources and time, to stocks with the highest probability of being potentially interesting (just remember the market maker and his 'eval': this is more or less the same thing, except we are working for more than a few basis points). The quant gives you the scale benefits that you couldn't have possibly detected as a human, because it's doing calculations based on an arbitrary number of variables over an arbitrarily large number of stocks. And yet at the same time, you get the qualitative ability to wade through and infer based on all the idiosyncratic information which makes each stock so unique.
Deep value, then, becomes a multi-tiered analysis where the resolution of the different phases of the analysis are proper functions of (1) the number of companies being looked at and (2) the nature of the process being observed and the time interval being examined. That makes sense.
So then the question becomes what model to create...