by Nate Silver
The other catch is that the pattern has since reversed itself. During the 2000s, the stock market changed direction from day to day about 54 percent of the time, just the opposite of the pattern from earlier decades. Had the investor pursued his Manic Momentum strategy for ten years beginning in January 2000, his $10,000 investment would have been whittled down to $4,000 by the end of the decade even before considering transaction costs.40 If you do consider transaction costs, the investor would have had just $141 left over by the end of the decade, having lost almost 99 percent of his capital.
In other words: do not try this stuff at home. Strategies like these resemble a high-stakes game of rock-paper-scissors at best,* and the high transaction costs they entail will deprive you of any profit and eat into much of your principal. As Fama and his professor had discovered, stock-market strategies that seem too good to be true usually are. Like the historical patterns on the frequency of earthquakes, stock market data seems to occupy a sort of purgatory wherein it is not quite random but also not quite predictable. Here, however, matters are made worse because stock market data ultimately describes not some natural phenomenon but the collective actions of human beings. If you do detect a pattern, particularly an obvious-seeming one, the odds are that other investors will have found it as well, and the signal will begin to cancel out or even reverse itself.
Efficient Markets Meet Irrational Exuberance
A more significant challenge to the theory comes from a sustained increase in stock prices, such as occurred in technology stocks during the late 1990s and early 2000s. From late 1998 through early 2000, the NASDAQ composite index more than tripled in value before all those gains (and then some) were wiped out over the two years that followed.
Some of the prices listed on the NADSAQ seemed to be plainly irrational. At one point during the dot-com boom, the market value of technology companies accounted for about 35 percent of the value of all stocks in the United States,41 implying they would soon come to represent more than a third of private-sector profits. What’s interesting is that the technology itself has in some ways exceeded our expectations. Can you imagine what an investor in 2000 would have done if you had shown her an iPad? And told her that, within ten years, she could use it to browse the Internet on an airplane flying 35,000 feet over Missouri and make a Skype call* to her family in Hong Kong? She would have bid Apple stock up to infinity.
Nevertheless, ten years later, in 2010, technology companies accounted for only about 7 percent of economic activity.42 For every Apple, there were dozens of companies like Pets.com that went broke. Investors were behaving as though every company would be a winner, that they wouldn’t have to outcompete each other, leading to an utterly unrealistic assumption about the potential profits available to the industry as a whole.
Still, some proponents of efficient-market hypothesis continue to resist the notion of bubbles. Fama, in what was otherwise a very friendly conversation, recoiled when I so much as mentioned the b-word. “That term has totally lost its meaning,” he told me emphatically. “A bubble is something that has a predictable ending. If you can’t tell you’re in a bubble, it’s not a bubble.”
In order for a bubble to violate efficient-market hypothesis, it needs to be predictable in real-time. Some investors need to identify it as it is happening and then exploit it for a profit.
Identifying a bubble is of course much easier with the benefit of hindsight—but frankly, it does not seem all that challenging to do so in advance, as many economists did while the housing bubble was underway. Simply looking at periods when the stock market has increased at a rate much faster than its historical average can give you some inkling of a bubble. Of the eight times in which the S&P 500 increased in value by twice its long-term average over a five-year period,43 five cases were followed by a severe and notorious crash, such as the Great Depression, the dot-com bust, or the Black Monday crash of 1987.44
A more accurate and sophisticated bubble-detection method is proposed by the Yale economist Robert J. Shiller, whose prescient work on the housing bubble I discussed in chapter 1. Shiller is best known for his book Irrational Exuberance. Published right as the NASDAQ achieved its all-time high during the dot-com bubble, the book served as an antidote to others, such as Dow 36,000, Dow 40,000 and Dow 100,00045 that promised prices would keep going up, instead warning investors that stocks were badly overpriced on the basis of the fundamentals.
In theory, the value of a stock is a prediction of a company’s future earnings and dividends. Although earnings may be hard to predict, you can look at what a company has made in the recent past (Shiller’s formula uses the past ten years of earnings) and compare it with the value of the stock. This calculation—known as the P/E or price-to-earnings ratio—has gravitated toward a value of about 15 over the long run, meaning that the market price per share is generally about fifteen times larger than a company’s annual profits.
There are exceptions in individual stocks, and sometimes they are justified. A company in an emerging industry (say, Facebook) might reasonably expect to make more in future years than in past ones. It therefore deserves a higher P/E ratio than a company in a declining industry (say, Blockbuster Video). Shiller, however, looked at the P/E ratio averaged across all companies in the S&P 500. In theory, over this broad average of businesses, the high P/E ratios for companies in emerging industries should be balanced out by those in declining ones and the market P/E ratio should be fairly constant across time.
But Shiller found that this had not been the case. At various times, the P/E ratio for all companies in the S&P 500 ranged everywhere from about 5 (in 1921) to 44 (when Shiller published his book in 2000). Shiller found that these anomalies had predictable-seeming consequences for investors. When the P/E ratio is 10, meaning that stocks are cheap compared with earnings, they have historically produced a real return46 of about 9 percent per year, meaning that a $10,000 investment would be worth $22,000 ten years later. When the P/E ratio is 25, on the other hand, a $10,000 investment in the stock market has historically been worth just $12,000 ten years later. And when they are very high, above about 30—as they were in 1929 or 2000—the expected return has been negative.
However, these pricing patterns would not have been very easy to profit from unless you were very patient. They’ve become meaningful only in the long term, telling you almost nothing about what the market will be worth one month or one year later. Even looking several years in advance, they have only limited predictive power. Alan Greenspan first used the phrase “irrational exuberance” to describe technology stocks in December 1996,47 at which point the P/E ratio of the S&P 500 was 28—not far from the previous record of 33 in 1929 in advance of Black Tuesday and the Great Depression. The NASDAQ was more richly valued still. But the peak of the bubble was still more than three years away. An investor with perfect foresight, who had bought the NASDAQ on the day that Greenspan made his speech, could have nearly quadrupled his money if he sold out at exactly the right time. Instead, it’s really only at time horizons ten or twenty years out that these P/E ratios have allowed investors to make reliable predictions.
There is very little that is truly certain about the stock market,* and even this pattern could reflect some combination of signal and noise.48 Still, Shiller’s findings are backed by strong theory as well as strong empirical evidence, since his focus on P/E ratios ties back to the fundamentals of stock market valuation, making it more likely that they have evinced something real.
So how could stock prices be so predictable in the long run if they are so unpredictable in the short run? The answer lies in how traders behave in the competitive pressures that traders face—both from their rival firms and from their clients and their bosses.
Much of the theoretical appeal of efficient-market hypothesis is that errors in stock prices (like Bayesian beliefs) should correct themselves. Suppose you’ve observed that the stock price of MGM Resorts International, a large gambling company, increases by 10 percen
t every Friday, perhaps because traders are subconsciously looking forward to blowing their profits in Atlantic City over the weekend. On a particular Friday, MGM starts out priced at $100, so you expect it to rise to $110 by the end of the trading day. What should you do? You should buy the stock, of course, expecting to make a quick profit. But when you buy the stock, its price goes up. A large enough trade49 might send the price up to $102 from $100. There’s still some profit there, so you buy the stock again and its price rises to $104. You keep doing this until the stock reaches its fair price of $110 and there are no more profits left. But look what happened: in the act of detecting this pricing anomaly, you have managed to eliminate it.
In the real world, the patterns will be nowhere near this obvious. There are millions of traders out there, including hundreds of analysts who concentrate on the gambling industry alone. How likely is it that you will have been the only one to have noticed that this stock always rises by 10 percent on Friday? Instead, you’re usually left fighting over some scraps: a statistical pattern that might or might not be meaningful, that might or might not continue into the future and that might or might not be profitable enough to cover your transaction costs—and that other investors are competing with you to exploit. Nevertheless, all that competition means that the market should quickly adjust to large pricing errors and that the small ones may not be worth worrying about. At least that’s how the theory goes.
But most traders, and especially the most active traders, are very short-term focused. They will arbitrage any profit opportunity that involves thinking a day, a month, or perhaps a year ahead, but they may not care very much about what happens beyond that. There may still be some predictability out there; it is just not within their job description to exploit it.
FIGURE 11-7: TRAILING P/E RATIOS AND STOCK MARKET RETURNS
The Stampeding Herd
Henry Blodget first came to the nation’s attention in 1998. After some meandering years split between freelance journalism and teaching English in Japan,50 he had settled in at a job analyzing Internet stocks for the company CIBC Oppenheimer. As attention to Internet stocks increased, attention to Blodget’s analysis did as well, and in December 1998 he issued a particularly bold call51 in which he predicted that the shares of Amazon.com, then valued at $243, would rise to $400 within a year. In fact, they broke the $400 barrier barely two weeks later.52
Such was the mania of the time that this may even have been a self-fulfilling prophecy: Amazon stock jumped nearly 25 percent53 within a few hours on the basis of Blodget’s recommendation. The call helped catapult Blodget to fame and he took a multimillion-dollar job as an analyst for Merrill Lynch. Blodget has a particular gift54 for distilling the zeitgeist of the market into coherent sentences. “What investors are buying,” Blodget said of Internet stocks in 1998,55 “is a particular vision of the future.” His way with words and reputation for success led to ubiquitous television and radio appearances.
Blodget’s call on Amazon still looks pretty good today: the shares he recommended at a price of $243 in 1998 would have traded as high as $1,300 by 2011 if measured on the same scale.56 He urged investors to pay for value, concentrating on industry leaders like Amazon, Yahoo!, and eBay and noting that most of the smaller companies would “merge, go bankrupt, or quietly fade away.”57 In private correspondence, he trashed small companies with dubious business strategies—LifeMinders, Inc., 24/7 Media, and InfoSpace—all of which turned out to be pretty much worthless and lost 95 to 100 percent of their value.
The problem, the big problem, is that despite criticizing them privately, Blodget had recommended stocks like LifeMinders publicly, maintaining buy ratings on them and defending them on TV. Moreover, the discrepancies seemed to favor companies with whom Merrill did banking business.58 Later charged with fraud by the Securities and Exchange Commission,59 Blodget disputed some details of the case but eventually settled for a $4 million fine60 and a lifetime ban from stock trading.
Blodget knows that whatever he says about Wall Street will be taken skeptically; a piece he once wrote for Slate magazine on the Martha Stewart trial had a 1,021-word disclosure statement attached to it.61 He has had time, however, to digest the work of economists like Fama and Shiller and compare it with his actual experience as a Wall Street insider. He has also embarked on a new career as a journalist—Blodget is now the CEO of the highly successful blogging empire Business Insider. All this has given him a mature if somewhat jaded perspective on the lives of analysts and traders.
“If you talk to a lot of investment managers,” Blodget told me, “the practical reality is they’re thinking about the next week, possibly the next month or quarter. There isn’t a time horizon; it’s how you’re doing now, relative to your competitors. You really only have ninety days to be right, and if you’re wrong within ninety days, your clients begin to fire you. You get shamed in the media, and your performance goes to hell. Fundamentals do not help you with that.”
Consider what would happen if a trader had read Shiller’s book and accepted its basic premise that high P/E ratios signal an overvalued market. However, the trader cared only about the next ninety days. Historically, even when the P/E ratio in the market has been above 30—meaning that stock valuations are twice as high as they are ordinarily—the odds of a crash62 over the next ninety days have been only about 4 percent.
If the trader had an unusually patient boss and got to look a whole year ahead, he’d find that the odds of a crash rose to about 19 percent (figure 11-8). This is about the same as the odds of losing a game of Russian roulette. The trader knows that he cannot play this game too many times before he gets burned. But what, realistically, are his alternatives?
FIGURE 11-8: HISTORICAL ODDS OF STOCK MARKET CRASH WITHIN ONE YEAR
This trader must make a call—buy or sell. Then the market will crash or it will not. So there are four basic scenarios to consider. First, there are the two cases in which he turns out to have made the right bet:
The trader buys and the market rises. In this case, it’s business as usual. Everyone is happy when the stock market makes money. The trader gets a six-figure bonus and uses it to buy a new Lexus.
The trader sells and the market crashes. If the trader anticipates a crash and a crash occurs, he will look like a genius for betting on it when few others did. There’s a chance that he’ll get a significantly better job—as a partner at a hedge fund, for instance. Still, even geniuses aren’t always in demand after the market crashes and capital is tight. More likely, this will translate into something along the lines of increased media exposure: a favorable write-up in the Wall Street Journal, a book deal, a couple of invitations to cool conferences, and so forth.
Which of these outcomes you prefer will depend significantly on your personality. The first is great for someone who enjoys the Wall Street life and likes to fit in with the crowd; the second for someone who enjoys being an iconoclast. It may be no coincidence that many of the successful investors profiled in Michael Lewis’s The Big Short, who made money betting against mortgage-backed securities and other bubbly investments of the late 2000s, were social misfits to one degree or another.
But now consider what happens when the investor gets his bet wrong. This choice is much clearer.
The trader buys but the market crashes. This is no fun: he’s lost his firm a lot of money and there will be no big bonus and no new Lexus. But since he’s stayed with the herd, most of his colleagues will have made the same mistake. Following the last three big crashes on Wall Street, employment at securities firms decreased by about 20 percent.63 That means there is an 80 percent chance the trader keeps his job and comes out okay; the Lexus can wait until the next bull market.
The trader sells but the market rises. This scenario, however, is a disaster. Not only will the trader have significantly underperformed his peers—he’ll have done so after having stuck his neck out and screaming that they were fools. It is extremely likely that he will be fired. And he will
not be well-liked, so his prospects for future employment will be dim. His career earnings potential will have been dramatically reduced.
If I’m this trader, a 20 percent chance of a crash would be nowhere near enough for me to take the sell side of the bet. Nor would a 50 percent chance. I’d want a crash to be a near-certainty before I’d be ready to take the plunge, and I’d want everyone else to be in the sinking ship with me.
Indeed, the big brokerage firms tend to avoid standing out from the crowd, downgrading a stock only after its problems have become obvious.64 In October 2001, fifteen of the seventeen analysts following Enron still had a “buy” or “strong buy” recommendation on the stock65 even though it had already lost 50 percent of its value in the midst of the company’s accounting scandal. Even if these firms know that the party is coming to an end, it may nevertheless be in their best interest to prolong it for as long as they can. “We thought it was the eighth inning, and it was the ninth,” the hedge fund manager Stanley Druckenmiller told the New York Times66 in April 2000 after his Quantum Fund had lost 22 percent of its value in just a few months. Druckenmiller knew that technology stocks were overvalued and were bound to decline—he just did not expect it to happen so soon.