The mathematical models involved here are used to value bundles of mortgages or other debt, including corporate "junk" (high yield) bonds. Most importantly, they predict probabilities or rates of default based on historical data and the characteristics of the overall economy, the borrowers, etc. One problem is that rating agencies such as Moody's and S&P were willing to rate senior tranches of subprime debt as AAA (safe), based on the model predictions. In other words, their models predicted that only the riskier tranches would take significant losses and sufficiently senior tranches were as safe as, well, T-bills.
Now, the failure of default models based on historical data might have something to do with loosening of credit standards and outright fraud at the mortgage broker (mainstreet) level. That has little to do with eggheads and math, although perhaps the eggheads should have realized the frailty of human nature in advance :-) Also, there is some question as to whether S&P and Moody's were happy to nudge the ratings higher in order to drum up business. It is an inherent conflict of interest that ratings agencies are paid to generate ratings!
The second model problem is more subtle and plays a role in hedge fund strategies. The models predict relative changes in valuation in different tranches. If interest rates spike, or spreads change, the effect on the senior tranches might be very different from that on junior tranches or on equities. Hedge funds made bets on the correlations predicted by their models, but at least in the short term got into trouble because the market was indiscriminate in marking down all forms of credit, independent of quality. These trades may, in the long run, be big winners if the hedgies have sufficient liquidity to ride them out. Goldman (and some smart co-investors like Hank Greenberg and Eli Broad) and Citadel may have the brains, guts and liquidity to ride this out.
The credit industry is in the early stages of building a system to redistribute risk. This works quite well for us in, e.g., the insurance industry. But it would be naive to think that there won't be hiccups and crises along the way. At the moment, much of the problem is fear and contagion: the system is new and untested, and the participants are afraid.
Final comment: I doubt the typical market neutral quant long-short fund is directly involved with credit products. They lost money recently simply because the market moved in a very unpredictable way -- certain funds that did have credit exposure had to sell whatever liquid positions they had to make margin calls. That means stocks that quant models tended to favor suddenly and unexpectedly went way down...
For Wall Street's Math Brains, Miscalculations
Complex Formulas Used by 'Quant' Funds Didn't Add Up in Market Downturn
By Frank Ahrens
Tuesday, August 21, 2007; A01
They are the powerful, cerebral and offstage actors of Wall Street, but the recent turmoil in the financial markets has yanked them into the light.
They are the math geniuses of the quant funds.
Short for "quantitative equity," a quant fund is a hedge fund that relies on complex and sophisticated mathematical algorithms to search for anomalies and non-obvious patterns in the markets. These glitches, often too small for the human eye, can present opportunities for short-and long-term trades that yield high-profit returns.
The models replace instinct. They try to turn historical trends into predictive science, using elegant mathematics seemingly above the comprehension of your average 401(k) participant or Wall Street fund manager.
Instead of veteran, market-savvy traders waving fistfuls of sell slips, the elite quant funds employ Nobel nerds with math PhDs, often divorced from the real world. It's not for nothing that they are called "black-box" funds -- opaque to outsiders, the boxes contain investment magic understood by only the wizards who conjured it up.
But the 387-point drop in the Dow Jones industrial average Aug. 9 and the continuing turmoil in the markets, in part attributed to massive sell-offs by the quant funds, have tarnished some of the quants' glimmering intellectual credentials and shown that, when push comes to shove, they can rush toward the exits as fast as a novice investor.
Last week, Goldman Sachs said its Global Alpha quant fund had lost 27 percent of its value this year because its computers failed to anticipate what the firm called "25 percent standard deviation moves" or events so rare Goldman had seen them only twice before in the firm's history. On the same day Goldman revealed the bad news, the firm said it would lead a group of big-money investors, including philanthropist Eli Broad, in pouring $3.6 billion into another Goldman quant fund, aiming to shore up confidence in the quants.
Barclays Global Investors, with $450 billion of its $2 trillion in assets under quant management, began applying mathematical tools to its funds in 1978. Last week, Barclays spokesman Lance Berg said the firm was "maintaining its investment process" despite the recent troubles. He would not say how much the Barclays quant funds had fluctuated during the period of turmoil.
The acknowledged quant king is James Simons, 69, an M.I.T.-trained mathematician with a groundbreaking theory that physicists are using to plumb the mysteries of superstring study and get at the very nature of
existence itself. Simons turned his big brain on investing after his math career, founding Renaissance Technologies quant shop. The firm pocketed $1.7 billion in investor fees last year, among the highest in the industry. In return, his clients can reap annual returns of more than 30 percent, according to news reports.
As elegant as the models are, they cannot predict unpredictable events, or human panic, some traders say. Further, some say, too many quant funds are full of myopic brainiacs, overly reliant on their tools.
"Most are idiot savants brought to industrial proportion," Nassim Nicholas Taleb, former quant-jock and bestselling contrarian author, said by phone from Scotland, where he is promoting his new book on improbability, "The Black Swan."
"They are very smart in front of a textbook but not smart enough to understand very elementary things in reality," he said.
Taleb believes in monkey-wrench events that shatter the models of the quant-jocks. He says their algorithms don't adequately account for huge, rare anomalies, such as the current surprise credit crunch. Or the Russian credit crisis in 1998 that nearly put the superstar quant fund of the time, Long-Term Capital Management, out of business in a matter of days, saved by cash infusion organized by the Federal Reserve.
The sentiment is reminiscent of the demise of Enron, a company said to have been designed by geniuses but run by idiots. The oil-and-gas trader used next-generation financial tools designed by brilliant mathematicians. But they couldn't overcome the inept and criminal actions of the management.
The allure of a unifying, perfect mathematical formula is powerful; it is an alchemy for the enlightened age. Math's universal principles underlie and suffuse everyday life and the workings of the cosmos, offering a glimpse of the eternal. In the frequently irrational financial markets, mathematic models offer the hope of cool reason and certitude, a sort of godlike wisdom.
In the 1998 film "Pi," a troubled math genius who sees patterns in the newspaper stock tables tries to create the Algorithm for Everything. He and his work are simultaneously hunted by a Wall Street firm that seeks its predictive powers, and by orthodox Jews, who believe it could unlock the mind of God.
The quant funds thrive on volatility -- it's how they make their profit margins. But recent weeks have proved too volatile for some of the funds, many of them highly leveraged, which seemingly all at once got spooked into seeking liquidity. When they ended up seeking liquidity by selling the same stocks, the Aug. 9 plunge happened, analysts speculate, resulting in the Dow's second-largest one-day slump of the year.
"It became increasingly transparent that many of the highly sophisticated quant funds employed similar investment approaches and held similar core holdings," Thomson Financial wrote in an analysis of the role of the 25 largest quant funds in the market meltdown. "This resulted in the funds selling similar long stocks and covering similar short positions."
For instance, the most broadly held stock among the top quant shops, Thomson reported, is Exxon Mobil. Shares of the oil company dropped 2.4 percent in heavy trading during the Aug. 9 sell-off.
"If you ask the question, 'Did the smart guys blow it or get it right?' I think the answer is, if they knew it, it wouldn't have happened," said David Levine, a vice president in corporate advisory services at Thomson.
"I occasionally hear broad statements like, 'This just shows computer models don't always work,' " Clifford S. Asness, founding principal of the quant-fund firm AQR Capital Management, wrote to his clients after the sell-off. "That's true, of course, they don't, nothing always works. However, this isn't about models, this is about a strategy getting too crowded, as other successful strategies both quantitative and non-quantitative have gotten many times in the past, and then suffering when too many try to get out the same door."
The value of Simons's $29 billion Renaissance Institutional Equities Fund fell by nearly 9 percent from the beginning of the month through the Aug. 9 drop, Bloomberg News reported. It was less of a hit than many of the other quants took, possibly reinforcing Simons's status as the Dumbledore of the quants.
A mathematician and cryptanalyst, Simons headed the math department of the State University of New York at Stony Brook, pushing the program into the nation's elite.
Simons and his colleagues work in a form of high math decipherable to a handful of humans on the planet. As such, practitioners of the rare mathematic arts can become the powerful priests of investing, thanks to their strange and obscure language, much the way the medieval church trafficked in Latin, which required the translation of a learned cleric.
In 1978, Simons began to apply his predictive models to investing and set up his investment shop on the north shore of Long Island near his old school, virtually insulated from Manhattan's financial district. He generally recruits mathematicians and programmers, not MBAs and traders.
The press-shy Simons would not comment for this article, and a Renaissance spokesman could not be reached for a comment.