Institutional Investor's Alpha Magazine - March 2008 - (Page 24) Quant Investing sions that drive trading activity. Other firms are pinning their hopes on machine learning — statistical methods that allow computers to identify relationships in financial data and make predictions from them. But regardless of the approach, managers agree that quant funds have been far too focused on equities and need to find ways to apply their strategies to a broader range of asset classes. “It’s important to cast your net as wide as possible, because you never know what you’re going to find,” says Dimitri Sogoloff, president and CEO of New York–based quant shop Horton Point. “And if you find something, rest assured that sooner or later it’s going to stop working.” Most quantitative strategies are designed to be market neutral — that is, to deliver positive returns irrespective of what happens to the broader market. Given the amount of borrowed capital that such strategies typically use — before August it was common for a statistical arbitrage fund to be ten times leveraged — the residual damage from last summer could have been worse. Most of the big quant firms have, in fact, bounced back. According to Chicagobased Hedge Fund Research, equity market-neutral and statistical arbitrage strategies finished 2007 up 5.8 percent and 9.1 percent, respectively. Meanwhile, the HFRI fund weighted composite index climbed 10.4 percent. The recovery masks the fact that equity marketneutral hedge funds have suffered a decadelong decline in performance. From 1999 to 2007 they had an annualized return of 6.1 percent. That compares with 11.8 percent from 1990 to 1998, according to HFR. The drop-off for statistical arbitrage funds is worse. They were up an annualized 4.6 percent from 1999 to 2007, versus 13.5 percent from 1990 to 1998. Too much money trying to exploit the same market inefficiencies accounts for some of the problem. HFR estimates that investors had $225 billion in quant hedge funds at the end of last year, more than the entire hedge fund industry had in early 1996. Overcrowding can be especially trying for quants, whose portfolios typically hold thousands of positions, making overlap among managers inevitable. WHEN MAREK FLUDZINSKI was studying physics at Princeton, he liked to get together with fellow graduate students to discuss market economics. The conversation would always come back to the fact that finance has no equivalent to the law of gravity or any other unifying principle of the physical world. After earning his Ph.D. in 1982, Fludzinski decided to apply his skills in the real world and went to work for Metron, a scientific consulting firm in suburban Washington set up to solve problems of national defense; there he helped develop satellite systems that could detect submarines under the surface of the ocean. But unanswered questions about finance nagged him. So in 1988 he joined Chicago’s Hull Trading Co., a proprietary trading firm later acquired by Goldman, Sachs & Co. He created options models at Hull before D.E. Shaw recruited him in 1990. At the time, quantitative trading was a small corner of Photo by Chris Hornbecker for Alpha “A lot of market behavior is somewhat predictable,” says John Moody, founder of J E Moody & Co. University of California, Berkeley, and once worked as a code breaker for the U.S. Department of Defense, was caught unawares by the August downdraft. In hindsight, last summer’s series of unfortunate events should not have been completely unexpected. Nine years earlier a global credit squeeze, which began when Russia defaulted on its ruble-denominated bonds, felled hedge fund Long-Term Capital Management, whose star-studded quant team included Nobel Prize–winning economists Robert Merton and Myron Scholes. But even the smartest managers seem to have underestimated the magnitude and speed of last August’s meltdown. “They’re all looking at their models and trying to get an understanding of which ones did worse and which ones did well,” says Andrew Lo, a fi nance professor at the MIT Sloan School of Management. “And they’re probably looking at various alternatives to try to forecast these kinds of dislocations in the future.” Lo is cofounder of Alpha Simplex Group, a Cambridge, Massachusetts–based quant fi rm with some $550 million in assets (see box). Quant shops aren’t sitting around idly. They are pressing into new realms of computational finance, applying concepts from molecular physics, mathematical linguistics, artificial intelligence and other scientific disciplines. Thales, for example, is using computer simulations to replicate human behavior to try to predict the myriad deci- 24 • INSTITUTIONAL INVESTOR’S ALPHA • MARCH 2008
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