Listen to the podcast:
After weeks of skittishness and fear, investors showed signs on Tuesday of settling down. “Yesterday was one of the dullest days in the market that we’ve had in a while, and that’s good in many ways,” says Wharton finance professor Jeremy Siegel.
Around the world, investors have been reeling from widespread problems in the subprime sector of the U.S. mortgage business. Stocks have fallen, especially among financial firms. Yields on Treasury securities have dropped, as people pile into an investment that’s seen as a safe harbor in times of tumult. And companies – lumped together with the big crowd of homeowners who have defaulted on their loans – are finding it hard to borrow money.
Yet Siegel remains upbeat. “It’s my belief that the basic economy is strong and that credits outside of the subprime mortgage industry are also strong,” Siegel says. “The economy will weather this and equity markets will recover nicely. So I’m optimistic but chastened because I didn’t think this hysteria would become as widespread as it did.”
Some commentators have criticized the U.S. Federal Reserve, the nation’s central bank, for not acting to reassure investors by more aggressively cutting interest rates. The Fed last week reduced the rate that it charges banks for short-term loans but left alone the one that banks charge each other, called the federal funds rate. Cuts in the federal funds rate can jumpstart a stalled economy but overheat a healthy one.
“I personally think that the Fed deserves an A for its effort so far,” Siegel says. The bank, under chairman Ben Bernanke, has proceeded cautiously out of fear of igniting inflation and also out of a desire not to backstop lenders who made bad loans, according Siegel. “If you always bail out bad behavior, you encourage it. But what the Fed is saying is that it doesn’t want the market for good credits to stop working.”
He interprets Tuesday’s calm as an indication that investors are properly reading the central bank’s signals. Tuesday also brought news that billionaire investor Warren Buffett might make a bid for Countrywide Financial, a mortgage lender grappling with subprime problems. “Buffett is astute and conservative, so that will give some confidence to the market,” Siegel says. That reassurance matters because fear, far more than any market fundamental, seems to have propelled the recent zigzags. “A lot of the contagion is that people don’t know who owns what,” Siegel adds. “XYZ corporation may be fine, but people are wondering, ‘What if it bought some of those subprimes?'”
Fear and panicked selling can create opportunities for bargain-hunting investors like Buffett, whose investment holding company, Berkshire Hathaway, is sitting on about $50 billion in cash. Average investors might consider following Buffett’s lead, Siegel says. “If you have cash, now would not be a bad time to move into stocks. Junk bond funds have sunk dramatically, so they are looking attractive, too.”
The Role of the “Quants”
A symptom of Wall Street’s recent woes can be found in the gyrations of several high-profile quantitative — more commonly known as “quant” — hedge funds. Quant funds use computer programs to screen stocks and other securities and to assemble their portfolios. Some allow little human intervention once the computer code has been written, buying and selling solely based on their programs’ recommendations. Other funds use software to narrow their list of possible investments but then let money managers make the final calls. Like many hedge funds, quant funds also tend to take on significant debt, which magnifies their ability to make money but also increases their losses when they falter.
Last week, Goldman Sachs, a publicly traded investment bank, announced that it was rescuing one of its quant funds that had racked up big losses. The firm teamed up with two private investors — Eli Broad and Maurice Greenberg — to inject $3 billion into the foundering fund, which had lost 30% of its value in the prior week. Several other leading quant managers, including Renaissance Technologies in New York and AQR in Connecticut, have also endured big drops in their portfolios. These outfits, which are privately held, haven’t said exactly how much money they have lost.
Quant funds have long worn a halo on Wall Street. Although they have existed for years, they have lately gained much greater notice because of the success of firms like Renaissance and AQR. They are often run by people with doctorates in disciplines like finance, physics and math, and their managers have therefore acquired reputations as savants. Success has brought riches, too. Jim Simons, the math professor turned money manager who runs Renaissance, has collected more than $1 billion a year thanks to strong returns and high fees paid by his clients.
The upheaval so far this August has shown that quant funds, despite their computer power, aren’t immune to mistakes and market downturns. Even so, their stumbles shouldn’t automatically make investors turn away from firms that employ quant strategies, says Wharton finance professor Rob Stambaugh. “Even the best model is going to sometimes be wrong. That’s what risk means. Sometimes you’re going to win, and sometimes you’re going to lose even when you take everything you can into account and do everything right. The best available forecast can still be wrong.”
Lately, the stakes for all investors have been higher than usual because the stock market has been showing greater than average volatility, Stambaugh notes. A measure of U.S. expected stock volatility — the Chicago Board Option Exchange’s Volatility Index, or VIX — has shot up this summer, hitting its five-year high last week. In other words, returns have roller-coastered, and investors expect them to continue to do so. “If you take a thousand funds and lever them up [with debt] and you have high volatility, some of them are going to do very poorly, and some are going to do very well.”
That doesn’t mean that their performance is simply distributed randomly. The poor performers could well be pursuing similar strategies. Hedge funds, for example, often tend to invest in less liquid stocks of small-capitalization firms, while selling short the more liquid shares of big, popular companies, like Microsoft and Procter & Gamble. When liquidity dries up, prices of the smaller, less liquid stocks often suffer more than blue chips, contributing to losses on this particular investing strategy.
“Yet quantitative models often neglect liquidity as a risk factor,” Stambaugh says. “As a result, managers who screen on quantitative factors like earnings momentum or valuation metrics may not realize the extent to which they are also making liquidity plays. Fluctuations in liquidity can then induce common outcomes across strategies.”
Hedge funds’ use of debt can exacerbate problems when their strategies go awry. “They borrow a lot of money, and then their broker starts making margin calls,” says Wharton finance professor Alex Edmans. “To meet the margin calls, they will often have to sell their stocks. But if they are all pursuing the same strategy, they are all facing the same margin calls, and they are all trying to sell the same thing at the same time. So you have a thousand people trying to get through one tiny exit door.”
At a theater, they end up trampling each other. On Wall Street, they crush each other’s portfolios.
On top of this problem, quantitative models can also have difficulty taking into account unusual events and rapid, hefty changes in market conditions. “Quants will even say that market shocks mess with their models,” notes Russ Kinnel, director of mutual fund research at Morningstar in Chicago. Computers can crunch through reams of data in a short time, but that data sometimes lags the market by days or even weeks.
Consider, for example, the subprime mess. As defaults on these loans mounted, “traders were probably rapidly doing back-of-the-envelope calculations” to see which lenders would be affected and by how much, Kinnel says. But the problems wouldn’t immediately show up in company earnings, where quantitative programs would detect them. As a result, traders might have begun to dump stocks with exposure to the subprime market while quantitative models were still recommending them.
Managers of quant funds probably knew of the subprime problems but might not have believed that they could come to affect the stock market as broadly as they have, adds Wharton finance professor Gary Gorton. Market crises like the subprime defaults and subsequent credit crunch occur rarely and thus can’t be readily factored into models. “Events are going to happen which are inconsistent with a model or never considered by a model,” he notes.
For Gorton, the inability of quant models to sense a rare event and make appropriate recommendations doesn’t call into question their durability or long-term value. “You assess a model by saying, ‘Does this make sense?’ and ‘Does it make money over long periods?’ All the funds we’re talking about have made money. So the issue is that, given that this is a special event, do we think it warrants giving up on these strategies?
“We build buildings in Tokyo based on earthquake-proof engineering standards,” says Gorton. “Yet when there’s an earthquake, some of them still fall down. Should we then give up on engineering?”