Tectonic Shifts in the Quantitative Management World

I have several friends who manage quantitative portfolios across a range of asset classes, and they have pretty much *all* been having a very difficult time over the past few weeks as the subprime mortgage crisis provokes a crisis of leverage and in turn provokes a liquidity crisis which then provokes an asset crisis. It seems that we are in the midst of one of those 100+ year events that come by every 5 to 10 years or so. Some managers are reporting “20+ sigma” events which are events that - under normal circumstances - shouldn’t happen more than once in the lifetime of the universe (if that). Rumors circulate that Goldman Sach’s Global Alpha fund is shutting down, or at least in major liquidation, and other managers are running scared, although no doubt comforted that this is a strategy-wide crisis, rather than any one manager’s mistake. So obviously “something’s afoot,” but what is it?

There are two interesting (in the Chinese sense of the world) things happening right now. One is to see how trouble in a relatively small sector of the credit market is triggering a major shift that could have 1929-style repercussions across many asset classes. The other is an observation that despite the very large number of quantitative funds, many established only in the last few years, there is not a great deal of diversification in the strategies employed. At first we had optimization as a method to control risk within a portfolio; then risk managers developed enterprise risk management as a method to protect enterprises from loading up on the same risk factors across portfolios; now we may be faced with an economy-wide risk management problem, where multiple financial enterprises are all exposed to the same risk factors, and get caught in a cycle of self-eliminating value, even if the fundamentals of underlying companies in the economy are relatively strong.

In 1929, the pancaking collapse of the stock market was linked to public belief that stocks simply could not lose for long, combined with excessive use of leveraging to increase returns. There was rampant speculation by the lay population, the use of techniques like astrology to predict market moves, and a number of purely behavioral activities which ultimately resulted in a substantial proportion of investments supporting companies with questionable economic value but whose stock was bid up by waves of fashion and euphoria. Markets usually have a self correcting mechanism to control for this type of irrationality after a while, but the crash was exacerbated by the fact that these stocks were highly leveraged, increasing risk. They could be bought with only about 10% margin, so that a large enough drop in stock price could trigger forced selling, which would then lower the price further, triggering more margin calls, more selling, etc.. With a 10-to-1 leveraging, this process could happen extremely quickly. One of the results of October 1929 is that - at least in the US - margin requirements were tightened to allow only 2-1 leveraging, rather than 10-1. In theory, this means that the chain reaction of margin calls lowering prices and provoking more margin calls, lowering prices further should not happen, or at least happen more slowly.

The development of modern derivative products has offered investors a way to get around the 2-1 leveraging requirement. Futures contracts typically require only 5% margin, theoretically allowing up to 20-1 leveraging, and making more cash or collateral available for additional investments. In turn, this helped keep borrowing rates low, which prompted more borrowing to extend leverage further. In addition, options allow for non-linear exposure to the market, also highly leveraged. Derivatives of derivatives allow nonlinear dependence on nonlinear products which sets the stage for deterministic chaotic behavior that can provoke behavioral panics. It is very easy to point to derivative products and blame them for a variety of financial ills, but it is not the product itself, merely the way they are used that has primed the system for a vicious cycle of value destruction.

The remarkable thing about the present situation is how small the real trouble in the subprime market sector really is, and yet how large an effect it is having. Mortgage defaults are up, but not enormously, and most market observers knew instinctively that there were a lot of low-quality loans being issued and that one should take these with healthy dose of skepticism.

The issue is that many of the subprime loans were then securitized, and rolled up into Collateralized Debt Obligations (CDOs) that divide up the payments into tranches and magically transform a large proportion of BB rated debt into AA rated debt that many institutional investors would be able to purchase. As long as mortgage defaults were relatively uncorrelated in time, the AA rated debt was protected by the fact that lower-rated tranches would absorb any losses from problematic loans and that a large number of defaults would have to occur before any real capital losses happened.

Now however, there are larger numbers of defaults; they seem more correlated than expected, and people are worried that the AA tranches may not be as safe as they thought. As a result, credit spreads have widened because managers want to be compensated for extra risks, and, as a result, many funds cannot afford to be as leveraged as they are. Therefore, they sell assets and cover positions to compensate for higher costs of leveraging debt. These sales in turn lower the price of assets, triggering more margin calls, forcing more selling, now across many asset classes. Sound familiar?

Now, what is the challenge in the quantitative world?

One big advantage of the quantitative model in normal times is that it can evaluate thousands of securities quickly and identify overvalued and undervalued assets, generating buy and sell orders. What this means is that one can make a large number of small-profit bets on relatively tiny mispricings rather than have to wait for substantial mispricings before it makes sense to act. It is akin to scooping up nickles and dimes with a vacuum cleaner rather than hunting for five and ten dollar bills by hand - provided you can scoop fast enough, it can be less risky and more profitable, since there are more likely to be nickles and dimes lying around than five and ten dollar bills. For funds that allow both long and short positions, there is an additional advantage is that a quantitative strategy can make money both by purchasing underpriced securities and by selling short overpriced ones.

The problem is - what to do when there is a major contraction of liquidity and computers in charge of substantial sums need to sell and cover positions. If the quantitative models are similar enough, then the computers from a number of funds are all likely to be instructing traders to be buying and selling the same sets of securities. Even so-called market-neutral funds are likely to be hit hard, because the underpriced securities are being sold to reduce leverage, and the overpriced securities are being bought to protect against (and simultaneously cause) a short squeeze. Therefore the prices of the underpriced securities drop, eliminating profit, and the prices of overpriced securities rise, eliminating profit; investors may decide to redeem funds, necessitating further selling and covering of short positions.

In finance programs across the world, the Capital Asset Pricing Model (CAPM) is taught because it is relatively simple to implement, easy to understand, and forms a basis for understanding other models of security pricing. The reason why CAPM is minimally plausible as something that reduces asset pricing to one single factor is that it does appear to explain roughly 85% of the variation in asset prices. The Fama-French (FF) three factor APT model, which includes CAPM’s market factor, plus two others, only improves explained variance by 5-10%, which means that most of the FF’s explanatory power results from the fact that its specification is very similar to CAPM.

Global Alpha and other quantitative models may have discovered new sets of paid risk factors that give them competitive advantages in normal times, but approximately 85% of predicted variations are still explained by the single CAPM factor, and a great many of Global Alpha’s competitors are using models that include this factor too. As a result, when liquidity contracts, these models are still generating buy and sell orders for roughly 85% of the same securities. After that, index funds need to change their adjustments, and the remaining 15% are affected. A similar logic may be argued for the quantitative optimization process, where the degree of liquidation required means that small differences in portfolio holdings are irrelevant to the net effect of having to sell or cover bits of everything in the portfolio.

What to do? It is hard to tell at this point. The fund managers that have large stores of cash are probably best positioned, and indeed, the low-leveraged funds might be able to increase their leverage at a later point to pick through the ashes for bargains. Given the tendency to overreact, there will be a bargain picking stage eventually, but the real question is how long to wait before looking.

Another thought is to try to construct what one might call a “Cassandra portfolio,” which generates unremarkable (possibly cash-like) returns in normal times, but expands enormously during market crises like this. Trying to figure out how to balance a regular portfolio and a Cassandra portfolio is an intriguing optimization problem.

A third thought is to introduce random elements to the securities that are bought and sold to reduce the correlation among buy and sell orders. This might work only for smaller sized portfolios, however, and also runs against a portfolio managers’ instinct to maximize the power of his or her active decisions. It would be difficult to explain to a supervisor that one decided what securities to buy and sell at random, but if every manager did this, the correlation between bought and sold securities would be reduced.

One question we will no doubt learn soon is which of the global macro and directional funds had swing positions set to take advantage of a liquidity shift. Most people knew that housing and mortgages were due for a correction, and many may have felt that the housing correction was already working its way through. People with bets on increased volatility are likely to be the biggest winners, provided those bets are big enough to offset the damage done to other more traditional strategies. Fundamental funds are also likely to be hit by the systematic effects, but perhaps not as quickly or intensely, since they are more likely to have a long-term perspective on their positions and a wider margin of error for the investment decisions they have committed to.

As the saying goes: “to err is human; to totally foul things up requires a computer.” But at least we can watch it all in color.