The New Math: Quantitative hedge funds are pressing into new realms of science

Discussion in 'Wall St. News' started by makloda, Apr 7, 2008.

  1. 27 Mar 2008

    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.

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    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 Alpha_Simplex 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.

  2. And what do you think are the chances of that happening?

    Before these guys set their sights on Mars, they should first prove to themselves that they can land on the moon. Stated differently, they should first try to predict female behavior with their models. If they can achieve that, then they may have a sporting chance at figuring out the markets.

    “I think there is a law tying everything together.”
    You can't make this stuff up. :D
  3. maxpi


    There's nothing like some losses to send a feller back to the old drawing board... and searching for the unified field theory of finance to boot....
  4. There IS a law tying it all together. The Law of Unintended Consequences.
  5. Must have something confused with GRAVITY!
  6. You know that ain't gonna happen.
  7. Just 10 years ago we had problems with "highly leveraged, complex and convoluted bets on credit instruments".

    And now we have it all over again. Doesn't anybody learn?

    Regulators should have made it impossible to occur a second time.... but I guess greed got in the way of common sense yet again.
  8. No because the government is hell bent on keepping this delusional economic system going
  9. Well, I am behaving exactly like the ppl in the article. I am a quant trader, and while my p&l stayed positive since the subprime crisis hit (thanks to quite stringent risk mgmt and decision to reduce exposure over all), overall return has dropped significantly.

    In all honesty, from a pure scientific point of view, I enjoy losses much more than gains. Losses usually exposes certain weakness in the overall model, necessitating either adjustment of models, or additions to the models that would attempt to make the model more complete. Making money, while financially interesting, really makes one lethargic (yes, kinda of "boring"), and less inclined to make fundamental changes to the underlying model.

    So as a researcher, the recent period are really very exciting, it gives one new data and new phenomenons that need to be modeled.
    #10     Apr 7, 2008