Editor's note: The following column is the 14th part of an ongoing series of articles by Aaron Brown examining the claims made in The Physics of Wall Street: A Brief History of Predicting the Unpredictable, a new book by James Owen Weatherall. Click here to read Part 1.
Last week we looked at Physicist James Weatherall’s The Physics of Wall Street
and his wacky solutions to problems like the quant equity crash of August 2007. This week we’ll look at the real problems.
Quant long-short equity is a simple strategy in principle. If you buy a large random basket of stocks, you can expect volatility around 20% per year in average times. If you buy a large random basket of stocks and go short a random bucket of other stocks in the same amount, your volatility drops to about 8% because the short positions offset much of the gains and losses from long positions. If you select your baskets carefully, you can get the volatility down to something like 2%. A typical quant long-short equity manager might try to find such a 2% volatility portfolio with an expected return of 2% per year, net of financing costs. This would give her a Sharpe ratio (return net of financing costs divided by volatility) of 1 and a low or zero correlation to the overall direction of equities, which would be quite valuable to institutional investors like pension funds and endowments.
However, the strategy would be difficult to market in this form as institutions are generally looking for higher returns. So our manager might lever her strategy 3 to 1 (6 to 1 gross leverage) to get an expected return and volatility of 6% per year. If she had $100 million to invest, she would buy $300 million of stock and short another $300 million of stock.
Leverage becomes the principal market risk of this strategy because so much effort has been made to squeeze out all the other risks. The portfolio has offset long and short positions in every country, industry, and type of company. It is balanced carefully to moderate risk from macro events, and diversified so broadly as to be protected against most micro events. It is monitored closely and uses only very liquid securities, so it can reposition quickly when necessary. Other than leverage problems, most of the market events that could damage this portfolio would result in slow bleeding rather than dramatic crisis losses (of course, there are still credit and operational risks that could cause sudden disasters).
Leverage imposes costs as well as risks. The direct financing costs are obvious. Less obvious is a mathematical disadvantage. Suppose one month your long positions go up 8% while your short positions go up 10%, resulting in a 1% loss to the unlevered portfolio. The next month the long positions go down 8% and the short positions go down 10%. Net, your long positions have gone down 0.64% (the square of the 8% move) while your short positions have gone down 1% (the square of the 10% move). The unlevered portfolio has a profit of 0.18%.
It’s a different story for the levered portfolio. The 1% unlevered loss in the first month translates to a 6% loss with 6 to 1 gross leverage. This requires a 13% reduction in the long positions and a 15% reduction in the short positions in order to keep the legs in balance and the leverage at 6 to 1 (a real portfolio would rebalance more frequently than monthly and at price movements smaller than 10%, and would have more sophisticated algorithms to reduce the leverage drag, this is just a toy example). As a result, the levered portfolio cannot take full advantage of the second month. Instead of getting six times the 0.18% return of the unlevered portfolio, a positive 1.08% return, it actually loses 0.36%.
This routine drag can become dangerous when the cost of leverage goes up due to less willingness of lenders to extend credit and increased cross-sectional volatility in the market. Cross-sectional volatility, also called “dispersion,” refers to the differences in performance of different stocks on the same day, as opposed to time series volatility (usually just called “volatility”) that refers to the performance of a single stock or index over successive days. The increased costs lead to reduced leverage as well as fund losses that require further leverage reductions. This trading soaks up liquidity in the stock market, which results in even higher trading costs, hence bigger losses, hence more position reductions. Lenders get nervous at the illiquidity, volatility, and losses, and become less willing to lend, driving up lending costs and exacerbating the spiral. If investors start to pull money out of the strategy, the crisis intensifies further. Opportunistic traders spot the situation and try to jump in ahead of the quant equity fund managers, throwing gasoline on the fire.
This is common knowledge among quant managers. It’s not an abstract theory; the effect is always present and has often risen to significant proportions. It is measured and managed in the normal course of business. Surviving traders are quite sophisticated at managing the risk. The version that occurred in August 2007 was the largest ever observed, but it was not fundamentally surprising.
An important point that most commentators including Weatherall miss is that the leverage-induced crash described above does not depend on the quant hedge funds all holding the same positions. All that is necessary is that there be enough overlap that some securities have significantly unbalanced holdings with quant funds as a group mostly long or mostly short. Unfortunately, there is no way to avoid this situation. There are stocks that will go up tomorrow, and stocks that will go down. Anyone who makes consistent money will have to be overrepresented with long positions in the stocks that go up, and overrepresented with short positions in the stocks that go down. If there were a small group of small hedge funds, they could avoid overlap by using different approaches. But with the large number of large funds that we have, significant overlap is inevitable. The only strategies that can be safe from this effect are the ones that don’t make consistent money.
Thus the seeds for the quant equity crisis were sown by the success of the strategy. This was recognized decades ago, and people have taken countermeasures. One thing you can do is develop new signals constantly through original research, to find profitable spread trades outside the set of popular quant stocks. Another is to monitor activity in your stocks and news about competitors to detect quant overcrowding. Or you can refine your execution capability to take advantage of liquidity pockets during large stock moves. Funds do all these things and more.
Unfortunately, all these efforts of funds to protect themselves increase the systemic risk. Lots of people trying new strategies means there’s more chance for error, plus those new strategies tend to be less profitable than the time-tested published strategies, so they require more leverage. Watching your stocks and competitors to buy or sell before others do makes markets unstable, like everyone trying to get out through a small door at the same time. More sophisticated execution can also lead to errors or instabilities. There is a more direct link as well. Your success and apparent safety encourage new entrants, and allow them to survive for a while without good risk management. These are often the largest players, hoping to make up in size for their late start, who run the riskiest businesses, both because they have to distinguish themselves from established funds and because they have not yet learned the harsh lessons of experience.
Attempts to reduce local risk create systemic risk. This is a general point, not just applicable to quant equity. The reason it is so obvious in quant equity is that many of the other risks have been reduced, and also that the kind of people who run quant equity strategies are more interested in truth and less interested in rewriting history to defend their actions so it’s easier to get at the truth. Next week we’ll discuss why instability introduced by efforts to make things safer is inherent to financial markets.
Links to previous stories in this series: Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7, Part 8, Part 9, Part 10, Part 11, Part 12, Part 13.
No positions in stocks mentioned.
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