The Art of Streetplay

Tuesday, June 27, 2006

Poking Holes in Bogle's Pro-Cap Weighting Rationale

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.

Tuesday, June 20, 2006

WisdomTree Update, June 20th 2006

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:
  1. 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?
  2. 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.

Sunday, April 30, 2006

WisdomTree Update

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.

  • 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.

Sunday, January 29, 2006

Proactive Forecasting

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.

Wednesday, December 28, 2005

Randomness Kills Simplicity, But Hey, That's Reality

“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.

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.

Sunday, December 25, 2005

Taking Another Look at Arnott (Why Not?)

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.

Sunday, December 18, 2005

Responding to a comment; model building thoughts

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.

Response to Gavekal's Indexation Article

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

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.