by Nate Silver
In today’s stock market, most trades are made with someone else’s money (in Druckenmiller’s case, mostly George Soros’s). The 1990s and 2000s are sometimes thought of as the age of the day trader. But holdings by institutional investors like mutual funds, hedge funds, and pensions have increased at a much faster rate (figure 11-9). When Fama drafted his thesis in the 1960s, only about 15 percent of stocks were held by institutions rather than individuals.67 By 2007, the percentage had risen to 68 percent.68, 69
These statistics represent a potential complication for efficient-market hypothesis: when it’s not your own money on the line but someone else’s, your incentives may change. Under some circumstances, in fact, it may be quite rational for traders to take positions that lose money for their firms and their investors if it allows them to stay with the herd and reduces their chance of getting fired.70 There is significant theoretical and empirical evidence71 for herding behavior among mutual funds and other institutional investors.72 “The answer as to why bubbles form,” Blodget told me, “is that it’s in everybody’s interest to keep markets going up.”
FIGURE 11-9: INDIVIDUAL AND INSTITUTIONAL INVESTOR TOTAL EQUITY HOLDINGS, UNITED STATES (ADJUSTED FOR INFLATION)73
Everything I’ve described up to this point could result from perfectly rational behavior on the part of individual participants in the market. It is almost as though the investors are responding hyper-rationally to their career incentives, but not necessarily seeking to maximize their firm’s trading profits. One conceit of economics is that markets as a whole can perform fairly rationally, even if many of the participants within them are irrational. But irrational behavior in the markets may result precisely because individuals are responding rationally according to their incentives. So long as most traders are judged on the basis of short-term performance, bubbles involving large deviations of stock prices from their long-term values are possible—and perhaps even inevitable.
Why We Herd
Herding can also result from deeper psychological reasons. Most of the time when we are making a major life decision, we’re going to want some input from our family, neighbors, colleagues, and friends—and even from our competitors if they are willing to give it.
If I have a forecast that says Rafael Nadal has a 30 percent chance of winning Wimbledon and all the tennis fans I encounter say he has a 50 percent chance instead, I’d need to be very, very sure of myself to stick to my original position. Unless I had some unique information that they weren’t privy to, or I were truly convinced that I had spent much more time analyzing the problem than they had, the odds are that my iconoclastic view is just going to lose money. The heuristic of “follow the crowd, especially when you don’t know any better” usually works pretty well.
And yet, there are those times when we become too trusting of our neighbors—like in the 1980s “Just Say No” commercials, we do something because Everyone Else Is Doing it Too. Instead of our mistakes canceling one another out, which is the idea behind the wisdom of crowds,74 they instead begin to reinforce one another and spiral out of control. The blind lead the blind and everyone falls off a cliff. This phenomenon occurs fairly rarely, but it can be quite disastrous when it does.
Sometimes we may also infer that the most confident-acting neighbor must be the best forecaster and follow his lead—whether he knows what he’s doing. In 2008, for reasons that are still somewhat unclear, a rogue trader on Intrade started buying huge volumes of John McCain stock in the middle of the night when there was absolutely no news, while dumping huge volumes of Barack Obama stock.75 Eventually the anomalies were corrected, but it took some time—often four to six hours—before prices fully rebounded to their previous values. Many traders were convinced that the rogue trader knew something they didn’t—perhaps he had inside information about some impending scandal?
This is herding. And there’s evidence that it’s becoming more and more common in markets. The correlations in the price movements between different stocks and different types of assets are becoming sharper and sharper,76 suggesting that everybody is investing in a little bit of everything and trying to exploit many of the same strategies. This is another of those Information Age risks: we share so much information that our independence is reduced. Instead, we seek out others who think just like us and brag about how many “friends” and “followers” we have.
In the market, prices may occasionally follow the lead of the worst investors. They are the ones making most of the trades.
Overconfidence and the Winner’s Curse
A common experiment in economics classrooms, usually employed when the professor needs some extra lunch money, is to hold an auction wherein students submit bids on the number of pennies in a jar.77 The student with the highest bid pays the professor and wins the pennies (or an equivalent amount in paper money if he doesn’t like loose change). Almost invariably, the winning student will find that he has paid too much. Although some of the students’ bids are too low and some are about right, it’s the student who most overestimates the value of the coins in the jar who is obligated to pay for them; the worst forecaster takes the “prize.” This is known as the “winner’s curse.”
The stock market has some of the same properties. Every now and then, the trader most willing to buy a stock will be the one who really does have some unique or proprietary insight about a company. But most traders are about average, and they’re using mostly the same models with most of the same data. If they decide a stock is substantially undervalued when their peers disagree, most of the time it will be because they’ve placed too much confidence in their forecasting abilities and mistaken the noise in their model for a signal.
There is reason to suspect that of the various cognitive biases that investors suffer from, overconfidence is the most pernicious. Perhaps the central finding of behavioral economics is that most of us are overconfident when we make predictions. The stock market is no exception; a Duke University survey of corporate CFOs,78 whom you might expect to be fairly sophisticated investors, found that they radically overestimated their ability to forecast the price of the S&P 500. They were constantly surprised by large movements in stock prices, despite the stock market’s long history of behaving erratically over short time periods.
The economist Terrance Odean of the University of California at Berkeley constructed a model in which traders had this flaw and this flaw only: they were overconfident in estimating the value of their information. Otherwise, they were perfectly rational.79 What Odean found was that overconfidence alone was enough to upset an otherwise rational market. Markets with overconfident traders will produce extremely high trading volumes, increased volatility, strange correlations in stock prices from day to day, and below-average returns for active traders—all the things that we observe in the real world.
Why It’s Hard to Bust Bubbles
And yet, if the market is trending toward a bubble, efficient-market hypothesis would imply that some traders should step in to stop it, expecting to make enormous profits by selling the stock short. Eventually, the theory will be right: all bubbles burst. However, they can take a long time to pop.
The way to bet against an overvalued stock is to short it: you borrow shares at their current price with the promise of returning them at some point in the future based on their price at that time. If the stock goes down in value, you will make money on this trade. The problem comes if the stock goes up in value, in which case you will owe more money than you had borrowed originally. Say, for instance, that you had borrowed five hundred shares of the company InfoSpace on March 2, 1999, when they cost $27, promising to return them one year later. Borrowing these shares would have cost you about $13,400. One year later, however, InfoSpace was trading at $482 per share, meaning that you would be obligated to return about $240,000—almost twenty times the initial value of your investment. Although this bet would have turned out to be brilliant in the end—InfoSpace later traded for as little as $1.40 per share—you would hav
e taken a bath and your ability to make future investments would be crippled. In fact, the losses from shorting a stock are theoretically unlimited.
In practice, the investor loaning you the shares can demand them back anytime she wants, as she assuredly will if she thinks you are a credit risk. But this also means she can quit anytime she’s ahead, an enormous problem since overvalued stocks often become even more overvalued before reverting back to fairer prices. Moreover, since the investor loaning you the stocks knows that you may have to dig into your savings to pay her back, she will charge you a steep interest rate for the privilege. Bubbles can take months or years to deflate. As John Maynard Keynes said, “The market can stay irrational longer than you can stay solvent.”
The Price Isn’t Right
At other times, investors may not have the opportunity to short stocks at all. One somewhat infamous example, documented by the University of Chicago economists Richard Thaler and Owen Lamont,80 is when the company 3Com spun off shares of its mobile phone subsidiary Palm into a separate stock offering. 3Com kept most of Palm’s shares for itself, however, so a trader could also invest in Palm simply by buying 3Com stock. In particular, 3Com stockholders were guaranteed to receive three shares in Palm for every two shares in 3Com that they held. This seemed to imply Palm shares could trade at an absolute maximum of two-thirds the value of 3Com shares.
Palm, however, was a sexy stock at the time, whereas 3Com, although it had consistently earned a profit, had a stodgy reputation. Rather than being worth less than 3Com shares, Palm shares instead traded at a higher price for a period of several months. This should have allowed an investor, regardless of what he thought about Palm and 3Com, to make a guaranteed profit by buying 3Com shares and shorting Palm. On paper, it was a virtually no-risk arbitrage opportunity,81 the equivalent of exchanging $1,000 for £600 British pounds at Heathrow Airport in London knowing you could exchange the £600 for $1,500 when you got off the flight in New York.
But shorting Palm proved to be very difficult. Few holders of Palm stock were willing to loan their shares out, and they had come to expect quite a premium for doing so: an interest rate of well over 100 percent per year.82 This pattern was common during the dot-com bubble:83 shorting dot-com stocks was prohibitively expensive when it wasn’t literally impossible.
I met with Thaler after we both spoke at a conference in Las Vegas, where we ate an overpriced sushi dinner and observed the action on the Strip. Thaler, although a friend and colleague of Fama’s, has been at the forefront of a discipline called behavioral economics that has been a thorn in the side of efficient-market hypothesis. Behavioral economics points out all the ways in which traders in the real-world are not as well-behaved as in the model.
“Efficient-market hypothesis has two components,” Thaler told me between bites of toro. “One I call the No Free Lunch component, which is that you can’t beat the market. Eugene Fama and I mostly agree about this component. The part he doesn’t like to talk about is the Price Is Right component.”
There is reasonably strong evidence for what Thaler calls No Free Lunch—it is difficult (although not literally impossible) for any investor to beat the market over the long-term. Theoretically appealing opportunities may be challenging to exploit in practice because of transaction costs, risks, and other constraints on trading. Statistical patterns that have been reliable in the past may prove to be ephemeral once investors discover them.
The second claim of efficient-market hypothesis, what Thaler refers to as the Price Is Right component, is more dubious. Examples like the discrepancy in pricing between Palm and 3Com stock simply could not have arisen if the price were right. You had the same commodity (the value of an interest in Palm) trading at two different and wildly divergent prices: at least one of them must have been wrong.
There are asymmetries in the market: bubbles are easier to detect than to burst. What this means is that the ultimatum we face in Bayesland—if you really think the market is going to crash, why aren’t you willing to bet on it?—does not necessarily hold in the real world, where there are constraints on trading and on capital.
Noise in Financial Markets
There is a kind of symbiosis between the irrational traders and the skilled ones—just as, in a poker game, good players need some fish at the table to make the game profitable to play in. In the financial literature, these irrational traders are known as “noise traders.” As the economist Fisher Black wrote in a 1986 essay simply called “Noise”:
Noise makes trading in financial markets possible, and thus allows us to observe prices for financial assets. [But] noise also causes markets to be somewhat inefficient. . . . Most generally, noise makes it very difficult to test either practical or academic theories about the way that financial or economic markets work. We are forced to act largely in the dark.84
Imagine there were no noise traders in the market. Everyone is betting on real information—signal. Prices are rational pretty much all the time, and the market is efficient.
But, if you think a market is efficient—efficient enough that you can’t really beat it for a profit—then it would be irrational for you to place any trades. In fact, efficient-market hypothesis is intrinsically somewhat self-defeating. If all investors believed the theory—that they can’t make any money from trading since the stock market is unbeatable—there would be no one left to make trades and therefore no market at all.
The paradox reminds me of an old joke among economists. One economist sees a $100 bill sitting on the street and reaches to grab it. “Don’t bother,” the other economist says. “If it were a real $100 bill, someone would already have picked it up.” If everyone thought this way, of course, nobody would bother to pick up $100 bills until a naïve young lad who had never taken an economics course went about town scooping them up, then found out they were perfectly good and exchanged them for a new car.
The most workable solution to the paradox, identified by the Nobel Prize–winning economist Joseph Stiglitz and his colleague Sanford Grossman many years ago,85 is to allow some investors to make just a little bit of profit: just enough to adequately compensate them for the effort they put in. This would not actually be all that difficult to accomplish in the real world. Although it might seem objectionable to you that securities analysts on Wall Street are compensated at $75 billion per year, this pales in comparison to the roughly $17 trillion in trades86 that are made at the New York Stock Exchange alone. So long as they beat the market by 0.5 percent on their trades, they would be revenue-positive for their firms.
The equilibrium proposed by Stiglitz is one in which some minimal profits are available to some investors. Efficient-market hypothesis can’t literally be true. Although some studies (like mine of mutual funds on E*Trade) seem to provide evidence for Fama’s view that no investor can beat the market at all, others are more equivocal,87 and a few88 identify fairly tangible evidence of trading skill and excess profits. It probably isn’t the mutual funds that are beating Wall Street; they follow too conventional a strategy and sink or swim together. But some hedge funds (not most) very probably beat the market,89 and some proprietary trading desks at elite firms like Goldman Sachs almost certainly do. There also seems to be rather clear evidence of trading skill among options traders,90 people who make bets on probabilistic assessments of how much a share price might move.* And while most individual, retail-level investors make common mistakes like trading too often and do worse than the market average, a select handful probably do beat the street.91
Buy High, Sell Low
You should not rush out and become an options trader. As the legendary investor Benjamin Graham advises, a little bit of knowledge can be a dangerous thing in the stock market.92 After all, any investor can do as well as the average investor with almost no effort. All he needs to do is buy an index fund that tracks the average of the S&P 500.93 In so doing he will come extremely close to replicating the average portfolio of every other trader, from Harvard MBAs to noise tr
aders to George Soros’s hedge fund manager. You have to be really good—or foolhardy—to turn that proposition down. In the stock market, the competition is fierce. The average trader, particularly in today’s market, in which trading is dominated by institutional investors, is someone who will have ample credentials, a high IQ, and a fair amount of experience.
“Everybody thinks they have this supersmart mutual fund manager,” Henry Blodget told me. “He went to Harvard and has been doing it for twenty-five years. How can he not be smart enough to beat the market? The answer is: Because there are nine million of him and they all have a fifty-million-dollar budget and computers that are collocated in the New York Stock Exchange. How could you possibly beat that?”
In practice, most everyday investors do not do even that well. Gallup and other polling organizations periodically survey Americans94 on whether they think it is a good time to buy stocks. Historically, there has been a strong relationship between these numbers and stock market performance—but the relationship runs in the exact opposite direction of what sound investment strategy would dictate. Americans tend to think it’s a good time to buy when P/E ratios are inflated and stocks are overpriced. The highest figure that Gallup ever recorded in their survey was in January 2000, when a record high of 67 percent of Americans thought it was a good time to invest. Just two months later, the NASDAQ and other stock indices began to crash. Conversely, only 26 percent of Americans thought it was a good time to buy stocks in February 1990—but the S&P 500 almost quadrupled in value over the next ten years (figure 11-10).
Most of us will have to fight these instincts. “Investors need to learn how to do exactly the reverse of what their fight-or-flight mechanism is telling them to do,” Blodget told me. “When the market crashes, that is the time to get excited and put your money into it. It’s not the time to get scared and pull money out. What you see instead is the more the market drops, the more money comes out of it. Normal investors are obliterated, because they continuously do exactly the wrong thing.”