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

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.

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


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


3. 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).


4. 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:

1. Hedge fund returns have abnormally high positive serial autocorrelation.


2. “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.


3. Hedge fund performance is indeed inversely proportional to size.


4. 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:

1. 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!).


2. 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%.


3. 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.