Sunday, April 6, 2008

the new math, as used by quant funds

an interesting article, from Alpha Magazine and Institutional Investor Magazine:

The New Math

by Nick Rockel

Smarting from last summer’s huge losses, quantitative hedge funds are pressing into new realms of science in an effort to prosper during the ongoing credit crisis.

For a man whose flagship hedge fund is running on fumes, Marek Fludzinski couldn’t be calmer. The founder and CEO of New York–based Thales Fund Management has watched his firm’s assets plummet by more than $1 billion during the past year, as Thales, like most quantitative managers, has suffered as a result of the global credit crisis that began last summer. But Fludzinski, who has a Ph.D. in theoretical physics from Princeton University and was one of the first two dozen employees at famed quant shop D.E. Shaw Group, is on a mission that means far more to him than profit and loss. He believes science is the key to unlocking the inner workings of the markets, and he intends to devote significant resources to prove it.

“I think there is a law tying everything together,” says Fludzinski, 52.

Science, however, didn’t much help Fludzinski last summer, when the onset of the credit crunch shook Thales and other quantitative hedge funds. Many of these firms specialize in a computer-powered strategy called statistical arbitrage, which uses mathematical models to profit from tiny mispricings of stocks and other assets. But in early August their models faltered after a large manager decided to liquidate its equity portfolio, most likely to meet margin calls on its credit positions. Partly because their strategies are based on many of the same academic theories, quant firms like AQR Capital Management, Renaissance Technologies Corp., D.E. Shaw and Thales held some of the same positions and began racking up huge losses. Sucker punched by the market, the quants didn’t know whether to cash out or stay in. Fludzinski and his peers came off like a bunch of propeller heads who had naively tried to bend reality to their models.

This isn’t the first time Fludzinski has run into trouble. In 2002, Thales fell about 10 percent, say investors, prompting some to flee. This go-around, in a more difficult market, Fludzinski found ways to stem the losses. “We had enough of a risk control program in place that we weren’t forced to liquidate for margin calls despite our leverage,” he says. “We had layers of option strategies on top of our stat arb strategies to protect them from this catastrophic risk.”

Yet even those risk controls didn’t save Thales from finishing 2007 down some 10 percent, according to investors, some of whom pulled their money. The fund, which despite its struggles has delivered an annualized return of 10 percent since its 1999 inception, began this year with just $400 million in assets.

This calamity wasn’t supposed to befall the quants, who are among the brightest people working in finance. During the past several years, managers and investors have flocked to their strategies, drawn by the promise of outsize returns and undersize risks. The dean of the quants is James Simons, founder of Renaissance, a publicity-shy firm based in East Setauket, New York. Renaissance’s $7.5 billion Medallion Fund has posted a 39 percent annualized return — after its hefty 5 percent management fee and 44 percent performance fee — since its 1988 inception. But even Simons, who has a Ph.D. in mathematics from the 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 finance 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 co-founder of Alpha­Simplex Group, a Cambridge, Massachusetts–based quant firm 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 decisions 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 Chicago-based 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 market-neutral 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 the investment world. D.E. Shaw had been founded two years earlier by former Columbia University computer science professor David Shaw, who came from Morgan Stanley, where he worked under renowned trader Nunzio Tartaglia. A onetime Jesuit with a Ph.D. in astrophysics, Tartaglia was an early proponent of pairs trading. He would find the stocks of two related companies — say, General Motors Corp. and Ford Motor Co. — whose prices had diverged from their historical relationship, and then buy the cheaper stock while shorting the overpriced one.

In this precursor to statistical arbitrage, Shaw — who launched his firm with $28 million from Greenwich, Connecticut–based Paloma Partners Management Co. and New York’s Tisch family — saw the potential for computerized trading on a grand scale. When Fludzinski arrived at D.E. Shaw, the now 1,300-person firm had about 20 employees. He ran one of the fledgling shop’s four quantitative strategies and began developing an options market-making system that would be able to price options on the fly. Fludzinski, who was born in Stafford, England, and grew up in Buffalo, New York, remembers D.E. Shaw as being extremely secretive.

Fludzinski quit D.E. Shaw in 1992 to build a statistical arbitrage system for Swiss Bank Corp. in New York. Two years later he opened Thales Financial Group, the predecessor of his current firm. Fludzinski named it after the ancient philosopher Thales of Miletus, who, in addition to introducing geometry to Greece, is known as one of the first people to corner a commodities market — in his case, olives, more than 2,500 years ago.

For the first four years, Fludzinski was bankrolled entirely by Paloma. In January 1999 he struck out on his own and launched the Thales Fund with $50 million. By the end of 2001, Thales had grown to $1.4 billion, mostly from funds of hedge funds, which were attracted to its market-neutral strategy. But the firm’s 10 percent drop in 2002 prompted the funds of funds to leave as quickly as they had come in, shrinking assets to $500 million. Fludzinski set about refurbishing his battered franchise: “We hired people and expanded our research department — by digging into my own retained capital — and it paid off.”

According to Fludzinski, quant shops generally fall into one of two main camps: those that strongly emphasize statistics and those that show more interest in fundamentals. He says he has always tried to find the middle ground. D.E. Shaw alumni like Fludzinski tend to be comfortable looking at such factors as price movement and volume, which may be statistically significant to performance but are not easily explainable by pure fundamental analysis.

Goldman Sachs quants have typically fallen more in the fundamentals camp, going back to the mid-1980s, when the firm hired MIT finance professor Fischer Black, known best for the options-pricing model he devised with Scholes and Merton. More-recent recruits include Clifford Asness, the University of Chicago finance Ph.D. who headed up Goldman Sachs Asset Management’s quantitative research group in the mid-1990s before leaving in 1998 to start AQR. In late 1995, Asness and his team launched Goldman’s famed market-neutral Global Alpha fund, which had an annualized return of 74 percent its first two years.

Quant investing has changed dramatically since the pairs trading of the 1980s. Today’s traders have to deal with many more potential combinations and relationships. One source of this added complexity is quick access to information, as market news spreads much faster than in the past. “Now statistical arbitrage is a much higher dimension of problem,” Flud­zinski says. “It’s almost like a chess game.”

Sanford Grossman, president and CEO of Greenwich-based quantitative hedge fund firm QFS Investment Management, notes that the quant connection with academia has led to the widespread sharing of ideas. “Instead of inventing things themselves from scratch, many firms are reading academic papers, talking with academics and coming up with strategies that for some reason work in a long historical simulation,” says Grossman, who continued to teach finance at the University of Pennsylvania’s Wharton School for more than a decade after founding QFS in 1988. “And then they do it. They don’t really know why it works.”

According to Grossman, that was part of the problem last summer. He is surprised that so few hedge funds protected themselves by purchasing options when they were cheap in late 2006 and early 2007. In his opinion, investors are largely to blame because they don’t reward managers for being conservative. “They penalize them because those firms are underperforming in the period before a crisis occurs,” says Grossman, whose firm manages about $3 billion in assets. “One thing that would help the system is if investors in funds become more cognizant of risk management strategies and how they are being implemented.”

Options protection helped Thales avoid the really big losses suffered last year by several large quant funds, including Goldman’s Global Alpha, which fell more than 14 percent the week of August 7 and 28.5 percent for the month. (For the year Global Alpha was down 38 percent.) Still, many Thales investors fled. Fludzinski says the reduced capital base gives Thales an opportunity to focus on “lower capacity — but ultimately higher return — high-frequency strategies.”

Statistical arbitrage funds like Thales employ strategies that play out over different timescales, ranging from minutes to months. Fludzinski says there’s a lot of action in the middle, where holding periods can range from a few days to a few weeks. At the long end, stock movements are driven largely by fundamental macroeconomic factors like corporate earnings. At the short end, investors’ immediate need to buy or sell can move a particular stock irrespective of the fundamentals. “The real question is, can such relationships be exploited?” Fludzinski says.

Thales’s 35-person research team, which includes ten Ph.D.s, is looking for ways to forecast investor behavior on different timescales. (In total, the firm employs about 50 people.) Although Thales focuses most of its effort on improving current trading systems and strategies, Fludzinski says the firm devotes as much as 20 percent of its resources to finding a unifying theory of markets. Like physics, he says, finance lends itself to mathematical models based on observations. Through experiments that replicate what they see in the natural world, physicists have described its underlying principles.

Thales is taking a similar approach with finance, using computers to model so-called agent simulations of thousands of traders. In one experiment Thales created a simulation with 10,000 traders, each owning the same portfolio of 15 stocks. After hearing news about one of these stocks, half of the simulated traders might buy it and the other half might sell. Fludzinski says such computer models help explain why a stock typically trades higher than average for a few days after a positive earnings announcement and then trades lower before returning to normal. By modeling such herding behavior, he hopes to better understand how securities prices move in relation to one another.

“Maybe we need to build a computer simulation that has 50 million people, with complicated rules for each,” Fludzinski says. “It’s very difficult to explain why people behave irrationally.”

Fludzinski is quick to point out that Thales’s agent simulations are different from much of the work being done in behavioral finance, which applies cognitive psychology to analyzing markets by using models in which people don’t always act rationally, are prone to overconfidence and are more risk-averse when they are losing money. “This is not behavioral finance like you read about that’s kind of long term, where everyone doesn’t know their own objective function and the fear of regret is really high,” he explains. “That’s sort of macroscopic. What we’re doing is microscopic behavioral finance.”

MIT’s Lo, whose adaptive markets hypothesis views the market as an evolving biological system, says behavioral finance has only recently started to gain credibility. “For the most part, the mainstream of financial theory has rejected psychology and behavior,” he says. “Even now they’re viewed with a great deal of skepticism.”

Tanya Styblo Beder, chairwoman of New York consulting firm SBCC Group and a longtime quant, says behavioral finance is a vital aspect of any good trading operation. “I think it’s going to be huge,” says the former CEO of Tribeca Global Management, Citigroup’s now-defunct hedge fund division. “It will be one of the most critical ways for people to discover things about supply-demand flows in the marketplace. If you can figure out where the money’s going and what it’s coming out of, then you should be able to make trades and make some pretty decent dough.”

Judith Posnikoff, a managing director at Irvine, California–based fund-of-hedge-funds firm Pacific Alternative Asset Management Co., thinks that Fludzinski and his team are well positioned for the current market. “They come up with interesting things on a regular basis and then actually implement them in the portfolio,” says Posnikoff, whose firm has invested in Thales since 1999. “And if something’s not working, they stop using it.”

JOHN MOODY HAS KEPT HIS HAND in both finance and academia for more than two decades. Like Fludzinski, Moody, the founder of quantitative hedge fund firm J E Moody & Co. in Portland, Oregon, has a Ph.D. in theoretical physics from Princeton. He got into trading as a postdoctoral student at the Institute for Theoretical Physics at the University of California, Santa Barbara, in the early ’80s. From 1987 to 1992 the Portland native taught computer science at Yale University, where he was also a member of the neuroscience program. Moody then founded and directed the Computational Finance Program at Portland’s Oregon Graduate Institute. In the meantime, he had started consulting for clients ranging from New York–based J.P. Morgan Securities to the U.S. government’s Defense Advanced Research Projects Agency.

The author of some 65 academic papers, Moody was among the first scientists to think about applying machine learning to finance. Machine learning is a branch of artificial intelligence whose proponents design computer programs that can recognize patterns and learn by trial and error. In the physical world, machine learning is used to help intelligent robots — for example, it enables the Mars rover to decide which of several routes to choose when moving through rough terrain. Financial markets, Moody says, are much less predictable and can be even tougher to navigate.

“You’re trying to build programs that are able to identify relationships in data and draw inferences and make predictions from those relationships,” he explains.

In 2003, Moody left full-time academic life to concentrate on his hedge fund firm. He also resigned from OGI to join the Algorithms Group, part of the International Computer Science Institute at the University of California, Berkeley. Moody, 50, has witnessed the financial world’s appetite for computer science, mathematics, physics, engineering and statistics graduates with knowledge of machine learning. He has hired several of his former students to work as traders and researchers at his firm and says that more than half of his Ph.D. and postdoctoral students have gone into finance. “A lot of my colleagues in computer science and engineering departments have had the same experience,” he adds.

Like an increasing number of quants, including Renaissance Technologies’ Simons, Moody is applying his expertise well beyond the equity markets. The $114 million JEM Commodity Relative Value Program, which he launched in May 2006, invests solely in commodity futures. JEM stands out from other commodity hedge funds because it uses statistical arbitrage and has a diversified portfolio. The fund trades in five sectors — energy, grains, livestock, metals and soft commodities — and balances long and short positions. JEM returned 13.9 percent last year, according to the Barclay Hedge database. From inception through February, it has a compounded average annual return of 22.27 percent.

Moody, who has used machine-learning algorithms to forecast commodity price movements, says the biggest challenge with financial data is that it’s so noisy. The more flexible and complex a quantitative model is, the greater its chances of finding spurious patterns with no forecasting value. But Moody and other machine-learning researchers have found ways to build flexible models that work with a limited number of parameters. As a result, they minimize the overfitting that can come from using too many parameters while still capturing subtle relationships in the data. “The techniques that have been developed in machine learning are especially good at maximizing predictive power,” he says.

One common style of machine learning is reinforcement, whereby an algorithm makes sequential decisions — while playing a board game like backgammon or checkers, for example. In finance, Moody says, reinforcement learning can be applied to portfolio rebalancing, where calls about what stock to drop or add depend on the success or failure of past choices.

Another application is making trading decisions. Moody says he and his colleagues have created reinforcement-
learning techniques that maximize the risk-adjusted returns of a trading system while taking into account transaction costs. “We’ll come up with a much different structure for a model than we would if we were to simply try to predict whether the market’s going to go up or down and then do trading and risk management as an afterthought,” he says.

Anyone can see this and other work on trading strategies in academic papers that Moody has co-authored with colleagues and students. He admits that he and others in the machine-learning community regret sharing some of their discoveries. “I think a few of us wish that we hadn’t published them,” he says.

Because markets are dynamic, machine-learning and other statistical models need constant updating. The key is feeding them the right mix of older and newer data. “You’re trying to find a sweet spot between using data that is stale, and therefore misleading, versus having enough recent data so that you can estimate a model reliably,” Moody explains.

Incorporating unprecedented events like last August into machine-learning models may be difficult, but Moody says it is possible to simulate how a model will behave under some extreme circumstances. One approach is to take, say, eight years of daily market data and do computer-based Monte Carlo simulations — which use a random sampling of numbers to create potential outcomes — to generate a hypothetical 800-year trading history. If this is done properly, the synthesized data will reflect previously observed major disruptions. Investors can use such an approach to estimate the frequency and magnitude of extreme market events, as well as the associated trading risks. Moody notes that most simulation methods can’t capture disruptions or changes in market behavior that have no historical precedent. Still, they can be used to create a model that generates trading signals — and takes fewer risks.

“By using these kinds of techniques, you’re informing the model of the possibility of extreme events,” Moody explains. “And that will give rise to a more conservative strategy.”

Moody says the advantage of a systematic method like machine learning is that it removes the emotion from trading decisions. But at the same time, it’s capable of taking market psychology into account. “A lot of market behavior is actually somewhat predictable based upon human emotion,” Moody notes. “An appropriate statistical model should be able to capture that.”

DIMITRI SOGOLOFF’S QUANT START-UP, Horton Point, has its roots in an act of generosity. In 2004, Sogoloff and Yuri Kuperin co-founded an academic program at St. Petersburg State University, in his native Russia, that trains science Ph.D.s to work in financial services. (Kuperin is head of the econophysics program there.) Sogoloff began wondering about the relationship between finance and pure science — and whether there was a systematic way to capture it without using just statistics.

According to Sogoloff, 46, the main flaw of most quant strategies is their heavy reliance on historical data. Even though no strategy works all the time, he explains, this backward-looking approach makes statistical arbitrage especially vulnerable to unforeseen market moves. “If there is an event that is not captured in the prices today, the statistical arbitrage box will completely fall apart,” he says. “It’s a fantastic tool, but that’s all it is. It’s a tool in what needs to be a larger toolbox.”

At Horton Point, Sogoloff and co-founder Vladimir Finkelstein are building that toolbox in the form of the firm’s Gallery QMS Fund. The multistrategy fund seeks to trade well in different market conditions by applying an evolving suite of strategies to many asset classes. Sogoloff and Finkelstein are currently researching four main investment areas — credit and capital-structure arbitrage, equities, fixed-income arbitrage and reinsurance.

Gallery was scheduled to launch in January, but general market risk has led to a delay. Although Horton Point has been trading with its own capital, the opening is on hold pending final talks with investors. Sogoloff says that to achieve “complete diversification,” the portfolio must have several hundred million dollars in assets. “We are finalizing an initial commitment from a strategic investor and hopefully will be able to begin trading shortly,” he adds.

Born in Kharkov, Ukraine, Sogoloff emigrated to the U.S. in 1980. After earning an engineering degree and an MBA from Columbia, he began his career as a market maker and convertible bond trader with London- and New York–based Quadrex Securities Corp. He later traded convertible bonds at the New York office of London’s Baring Securities and for Chicago-based LIT America.

In 1993, Sogoloff co-founded Alexandra Investment Management with Mikhail Filimonov. The New York hedge fund firm focused on convertible arbitrage before branching out into other strategies early this decade. Sogoloff says he left Alexandra in the fall of 2006 because he wanted to pursue something completely new. He started Horton Point that October with Finkelstein. Finkelstein, who also grew up in Kharkov, has a Ph.D. in theoretical physics from New York University and a master’s degree in the same discipline from the Moscow Institute of Physics and Technology. Before joining Horton Point as chief science officer, he was head of quantitative credit research at Citadel Investment Group in Chicago.

Most of the 12 Ph.D.s at Horton Point’s Manhattan office are researching investment strategies and ways to apply scientific principles to finance. The firm runs what Finkelstein, 54, describes as a factory of strategies, with new models coming on line all the time. “It’s not like we plan to build ten strategies and sit on them,” he says. “The challenge is to keep it going, to keep this factory functioning.”

Along with his reservations about statistical arbitrage, Sogoloff is wary of quants who believe the real world is obliged to conform to a mathematical model. He acknowledges the difficulty of applying scientific disciplines like genetics or chaos theory — which purports to find patterns in seemingly random data — to finance. “Quantitative work will be much more rewarding to the scientist if one concentrates on those theories or areas that attempt to describe nonstable relationships,” he says.

Sogoloff sees promise in disciplines that deal with causal relationships rather than historical ones — like mathematical linguistics, which uses models to analyze the structure of language. “These sciences did not exist five or ten years ago,” he says. “They became possible because of humongous computational improvements.”

However, most quant shops aren’t exploring such fields because it means throwing considerable resources at uncertain results, Sogoloff says. Horton Point has found a solution by assembling a global network of academics whose research could be useful to the firm. So far the group includes specialists in everything from psychology to data mining, at such schools as the Beijing Institute of Technology, the California Institute of Technology and Technion, the Israel Institute of Technology.

Sogoloff tells the academics that the goal is to create the Bell Labs of finance. To align both parties’ interests, Horton Point offers them a share of the profits should their work lead to an investment strategy. Scientists like collaborating with Horton Point because it combines intellectual freedom with the opportunity to test their theories using real data, Sogoloff says. “You have experiments that can be set up in a matter of seconds because it’s a live market, and you have the potential for an amazing economic benefit.”

As he seeks to right his faltering hedge fund, Thales’s Fludzinski could stand to reap such rewards sooner rather than later. But he’s not losing sight of his quest to explain why markets do what they do — a goal that may require a leap of imagination. Just as quantum mechanics and the theory of relativity shook up the world of physics, Fludzinski says, “finance needs a similar out-of-the-box insight — something that changes the way we look at things.”

Summer Solstice: How the Quants Fell Back to Earth

When quantitative hedge funds trading long-short equity strategies racked up big losses in August, Andrew Lo wasted no time weighing in. Lo, 47, is Harris & Harris Group Professor of Finance at MIT Sloan School of Management, where he directs the Laboratory for Financial Engineering. He is also co-founder of AlphaSimplex Group, a multistrategy quantitative hedge fund firm based in MIT’s hometown of Cambridge, Massachusetts, and has a long-standing research interest in the hedge fund industry’s vulnerability to systemic shocks.

A month after the blowup, Lo and his student Amir Khandani released the first draft of a paper called “What Happened to the Quants in August 2007?” Their hypothesis: Last summer’s losses resulted from the unwinding of one or more quantitative equity market-neutral portfolios. Because the price impact was so swift and dramatic, the unwinding probably began with a sudden liquidation by a large multistrategy fund or proprietary trading desk, perhaps to cut risk or meet margin calls on a credit portfolio. Most of the damage occurred from August 7 to August 9, when many funds started selling to reduce their risk. By deleveraging, these firms missed the bounce back in equities on August 10.

Lo and Khandani had no data from the quant shops themselves, so they created a simulated long-short equity strategy and then compared it against the individual and aggregate performance of hedge funds in the Lipper TASS database. Using their test strategy’s performance as a “microscope,” Lo says, they also compared August 2007 with August 1998, when Greenwich, Connecticut–based Long-Term Capital Management and other fixed-income relative-value hedge funds suffered a similar meltdown.

Like the sequence of events in 1998, which were triggered when Russia defaulted on its bond payments, last summer’s troubles were precipitated by a credit crunch. But this time around, Lo says, a shock in one area — subprime mortgages and credit portfolios — infected a completely unrelated sector of the market.

The events of August 2007 had a big impact on equity prices; those of August 1998 had none at all. (In 1998 the Standard & Poor’s 500 index was up 28.6 percent; last year the S&P was up just 5.5 percent.) “What that tells us is that the hedge fund space is very, very crowded,” Lo says. “The economy is much more sensitive to shocks in the hedge fund sector than ever before. This is proof positive that systemic risk has increased.”

In 1999, Lo launched AlphaSimplex after developing quantitative models as a consultant to institutional and private investors. The firm’s first offering was a U.S. long-short equity fund for Greenwich-based Paloma Partners Management Co. In late 2003, AlphaSimplex opened a global long-short fund to multiple investors. Last September, Paris-based Natixis Global Asset Management acquired the firm, which has 20 employees and manages $550 million in assets. Lo has stayed on as chief scientific officer and chairman.

Lo’s dual career as an academic and a hedge fund manager grew out of his pioneering research in fields as disparate as statistics and psychology. A partnership with industry, his MIT laboratory receives funding from several investment firms, including Boston’s State Street Global Advisors and Newport Beach, California–based Pacific Investment Man­agement Co., both of which offer absolute-return strategies.

Lo says he’s not surprised that statistical arbitrage and other quant strategies got slammed last summer. “Quant funds by their nature are going to be invested in mostly liquid strategies, and so when we have massive liquidations, they’re going to be first in line to take their licks,” he explains.

To protect themselves, Lo says, quant funds must understand their exposure to future sell-offs. That means paying close attention to the relationship between liquidity and their strategies’ expected returns. “We can’t forecast with any degree of accuracy when the next LTCM is going to hit,” Lo says. “But we can tell when certain market conditions are ripe for a dislocation and gradually try to adjust our risk exposures to take that into account.”

Lo believes that forecasting such disruptions will remain a problem — at least until hedge fund firms surrender more-detailed information. “The hope is that if we can get more data for both investors and managers to use, we will be able to avoid this kind of mad rush to the exits in the future,” he says. — N.R.

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