Institutional Investor's Alpha Magazine - March 2009 - (Page 36) D.E. Shaw that had driven the firm’s success during its first decade. Investors, for their part, say that they are relatively happy with the 2008 performance — D.E. Shaw’s losses paled in comparison with the 20.3 percent decline in the HFRI multistrategy index and the 55 percent drop in Chicagobased Citadel Investment Group’s multistrategy funds. “They did pretty well in a very difficult set of markets,” says the Getty Trust’s Algert. Still, like almost every hedge fund firm, D.E. Shaw suffered redemptions — probably around $2 billion, based on its 2008 returns and January 1 asset totals, although the firm won’t comment. But even if the mass exodus of money from hedge funds is largely over — and there’s no reason to think it is — investors are likely to demand greater transparency from D.E. Shaw, which, given the firm’s history, may not come easily. “D.E. Shaw will tell you which strategies made money, but they won’t disclose their holdings,” says one large fund-ofhedge-funds investor with money in the firm’s multistrategy and macro funds. “For people who require real transparency, D.E. Shaw might not be the right investment.” avid Shaw envisioned D.E. Shaw “as essentially a research lab that happened to invest, and not as a financial firm that happened to have a few people playing with equations.” He considers himself a scientist rather than a financier and knew next to nothing about finance when he arrived on Wall Street. His vision began to take shape in 1986, when he left Columbia to join the automated proprietary trading group at Morgan Stanley & Co. Led by renowned quant Nunzio Tartaglia and trader Gregory van Kipnis, the APT group used statistical models to identify and profit from small, typically very short-term, anomalies in securities prices. To be successful, statistical arbitrageurs must be able to process and analyze an enormous amount of data. Shaw’s big contribution at Morgan Stanley was to introduce distributed computing — using multiple computer processors in parallel to crunch data — to create what was in effect a supercomputer. In 1988, Shaw left Morgan Stanley to strike out on his own. He began by looking for people with backgrounds like his — scientists, rather than financial professionals, who could use their expertise to develop trading strategies. “David has done it right,” says Marek Fludzinski, who was a researcher at D.E. Shaw from 1990 to 1992 and now manages Thales Asset Management, a $350 million New York–based statistical arbitrage hedge fund firm. “He would hire the smartest people who could also communicate with others and have a clear point of view.” Lou Salkind was the second employee hired. (The first, Peter Laventhol, left D.E. Shaw in January 1994 to start his own firm, Spark Management.) A mathematics prodigy and Manhattan native, Salkind was finishing up his Ph.D. in computer science and robotics at New York University’s D D DAVID SHAW JOINS THE WAR ON CANCER avid Shaw had established one of the leading quantitative hedge fund firms by the late ’90s, with a decadelong record of investing excellence, but still the former computer science professor felt unfulfilled. “From a scientific point of view, I felt like I was getting stupider with every passing year,” he says. “I was forgetting things I’d learned in undergraduate calculus. I found it really depressing.” Shaw’s epiphany came in 2001, at his 50th birthday party, when he spoke with the late William Golden, then 91 and a leading figure in U.S. science policy since the 1950s. Golden, whom Shaw had met shortly after being appointed to the President’s Council of Advisors on Science and Technology in 1994 by Bill Clinton, told the hedge fund manager to follow his heart. Has he ever. Over the past seven years, Shaw has assembled a team of 75 biologists, chemists, computer scientists, engineers, mathematicians and physicists and built D.E. Shaw Research into a leading innovator in the field of molecular dynamics, which uses computers to create 3-D images of biological structures. The inspiration for the research arm harks back to Shaw’s days at Columbia University in the mid-1980s, when he was developing a massively parallel supercomputer for use in artificialintelligence systems. Cyrus Levinthal, then chairman of the university’s biological sciences department and a pioneer in molecular dynamics, had designed a software program that tried to predict how proteins assemble themselves, a process called folding, but he needed a specialized supercomputer to run it. Shaw couldn’t come up with a solution at the time, but he found the problem interesting — and, ultimately, irresistible. When he decided to step back from the day-to-day grind of the hedge fund business and return to the world of scientific research, he chose to tackle the protein-folding problem. “At a certain point I started thinking it might be possible to build a supercomputer that could shed light on the folding process and on other bio- 36 • INSTITUTIONAL INVESTOR’S ALPHA • MARCH 2009
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