What is Efficient Market Hypothesis
The efficient market hypothesis is that stock prices take into account all relevant information so that no investor can take advantage of other people’s ignorance. An efficient market does not require zero profits. After all, even boring bank accounts pay interest. Investors won’t buy stocks unless they expect to make some money. Stocks do pay dividends and the average stock investor does make money—about 10 percent a year over the past 100 years. The efficient market hypothesis is that no one can make excessive profits except by being lucky.
The stock market would not be efficient if sales of Ford F-150 pickup trucks jumped dramatically and investors who read about the sales increase in the morning newspaper could buy Ford stock at low prices from investors who didn’t know about the sales bump.
Market efficiency does not assume that every investor knows about the sales increase—just that enough well-informed and well-financed investors are ready to jump-start Ford’s stock price to where it would be if everyone did know the news. Ford’s stock price should jump as soon as some investors know of the F-150 surge, and this immediate price bump protects amateurs from selling at outdated prices. An efficient market is a fair game in the sense that no investor can beat the market by knowing something other investors don’t know.
ANTICIPATED EVENTS AND SURPRISES
If a toy store’s sales increase before Christmas, will its stock price increase too? It depends. When the stock traded in the summer, investors tried to predict holiday sales and they valued the stock based on these predictions. If the forecasts turn out to be correct, there is no reason for the stock price to change on the day that the sales numbers are announced. The price will change, however, if sales turn out to be unexpectedly strong or disappointing.
Stock prices do not rise or fall when events that are expected to happen do happen. Stock prices do change if the unexpected happens. However, by definition, it is impossible to predict the unexpected. Therefore, it is impossible to predict changes in stock prices. That is a pretty good argument. So is its implication.
It is not enough to know that Ford F-150 sales increased last quarter or that auto sales increase when the economy gets stronger. The benchmark to gauge your investment ideas is not,
How is today different from yesterday? or
How will tomorrow differ from today? but
How does my prediction of tomorrow differ from what others expect?
When you think you have a good reason for buying a stock, ask yourself if you know something that other investors don’t know. If you do, it may be inside information that is illegal to use (for reasons discussed later in this blog). If you don’t, your information is probably already reflected in market prices.
It seems obvious, but sometimes the obvious is overlooked. A longtime financial columnist for the New York Times offered this logical, but not very helpful, advice for buying bonds:
It is obviously good sense to buy bonds when the Federal Reserve Banks start lowering interest rates. It is just as obviously bad sense to buy them at any time when, two or three or four months hence, the Fed is certain to start raising money rates and lowering the prices of outstanding bonds.
Anything that has already happened or is certain to happen is surely already embedded in bond prices. Similarly, you won’t get rich following this advice in the Consumers Digest Get Rich Investment Guide:
The ability to track interest rates as they pertain to bonds is made easier by following the path of the Prime Rate (the rate of interest charged by banks to their top clients). If the consensus shown in top business journals indicates that rates are going up, this means that bonds will go down in price. Therefore, when it seems that rates are moving up, an investor should wait until some “peaking” of rates is foreseen. Seriously, isn’t it obvious that easily available information doesn’t give you an edge over other investors? What everybody knows isn’t worth knowing.
UNCERTAINTY AND DISAGREEMENT
The efficient market hypothesis does not assume that everyone agrees on what a security is worth. The market opinion of IBM in the spring of 1987 provides a particularly dramatic example. At that time, IBM was probably the most widely scrutinized company. Value Line’s highly regarded analysts gave IBM stock its lowest rating, placing it among the bottom 10 percent in predicted performance over the next twelve months.
At the very same time, Kidder Peabody's widely respected research department gave IBM its highest rating and included IBM in its recommended model portfolio of twenty stocks. This was not an isolated fluke.
On Thursday, November 11, 2010, TheStreet posted an article titled “How Nokia’s Stock Price Could Double.” Three days later, on Sunday, November 14, Seeking Alpha posted an article titled “Nokia Is Still Overvalued,” arguing that a “more realistic” value would be half of its current price. Nothing substantive happened between November 11 and November 14, yet these two influential websites had dramatically different views of Nokia stock. That’s why there are buyers and sellers.
For any stock, at the current market price, there are as many buyers as sellers—as many people who think that the stock is over-priced as think it is a bargain. The efficient market hypothesis says that it is never evident that a stock’s price is about to surge or col-lapse—for if it were, there wouldn’t be a balance between buyers and sellers.
The fact that changes in stock prices are hard to predict does not imply that stock prices are correct according to some external, objective criterion. Market prices reflect what buyers are willing to pay and sellers to accept, and both may be led astray by human emotions and misperceptions. This distinction is crucial for understanding what the efficient market hypothesis does and does not say.
Most investors try to time their purchases and sales—to buy before prices go up and sell before they go down. A longtime financial writer for the New York Times offered this attractive goal: If only it were this easy!
It would be great to jump nimbly in and out of stocks, catching every rise and missing every drop, but how do we know in advance whether prices are headed up or down? J. P. Morgan was exactly right when he said that “the market will go up and it will go down, but not necessarily in that order.”
I am no fan of frenetic trading; still, there are times when stock prices are seriously out of whack because investors are far too pessimistic or optimistic. Our goal is to recognize compelling times to buy and times to sell. Ironically, the key is not to attempt to buy before prices go up and sell before they go down but to think about whether the money machine is cheap or expensive—to be a value investor. I will explain this paradox in later blogs.
In contrast to market timing, stock selection is picking stocks that will beat other stocks, a task the efficient market hypothesis says is futile. For example, you might buy stock in a company that announces a large earnings increase and avoid firms that report flat or declining earnings. However, analysts predict earnings long before they are announced and stock prices reflect these predictions.
Announcements that are in line with predictions—regardless of whether earnings are predicted to go up or down—have little effect on prices. To pick stocks that will do better than other stocks, based on their earnings, you need to predict which companies’ earnings surprises will be good news and which will be bad news. How does one predict surprises?
A former student told me about an interesting project he had worked on. His consulting firm had been hired by the directors of a major consumer-products company to recommend an executive compensation plan. The board wanted the firm, year after year, to be one of the highest-ranked consumer-products companies based on total shareholder return, dividends plus capital gains.
The consulting company immediately recognized the problem with this goal. Do you see it, too?
Another way of testing the efficient market hypothesis is to look at the records of professional investors, who presumably base their de-cisions on publicly available information. If they consistently beat the market, they apparently have an advantage over amateur investors.
The record of professional investors as a group has been mediocre, at best. In his persuasive blog The New Contrarian Investment Strategy, David Dreman looked at fifty-two surveys of stocks or stock portfolios recommended by professional investors. Forty of them underperformed the market. Perhaps some professionals are pros and the rest are amateurs pretending to be pros.
Nope. There is no consistency in which professional investors do well and which do poorly. A study of 200 institutional stock portfolios found that of those who ranked in the top 25 percent in one five-year period, 26 percent ranked in the top 25 percent during the next five years, 48 percent ranked in the middle 50 percent, and 26 percent were in the bottom 25 percent.
CAN WE DISTINGUISH SKILL FROM LUCK?
Some investors do compile outstanding records. However, consider the coin-calling experiment that I sometimes do in my investment classes. Suppose that there are thirty-two students and that half of them predict heads for the first flip while half of them predict tails. The coin is flipped and lands heads, making the first half right. The sixteen students who right then predicted the second flip, with half saying heads and the other half tails.
It comes up tails and the eight students who have been right twice in a row now try for a third time. Half predict heads and half tails, and the coin lands tails again. The four students who were right divide again on whether the next flip will be heads or tails. The result is tails and now we’re down to two students. One calls heads and the other tails. It is ahead and we have our winner—the student who correctly predicted five in a row. Are you confident that this student will call the next five flips correctly?
Even monkeys throwing darts and analysts flipping coins sometimes get lucky—an observation that cautions us that past successes are no guarantee of future success. Not only that, even if some analysts are somewhat better than average, stock market volatility makes it very difficult to separate the skilled from the lucky.
Imagine there are 10,000 analysts and 10 percent of them have a 0.60 probability of predicting correctly whether the stock market will go up or down in the coming year. The remaining 90 percent, like monkeys throwing darts, have only a 0.50 probability of making a correct prediction. Call the analysts in the first group skilled and those in the second group lucky.
If we look at their records over a ten-year period, we can expect six of the 1,000 skilled analysts to make correct predictions in all ten years. Among the 9,000 analysts who are merely lucky, nine can be expected to make ten correct predictions.
This means that if we choose one of the fifteen analysts who has been right for ten years in a row, there is only a 40 percent chance that we will choose a skilled analyst. This is considerably better than the 10 percent chance of picking a skilled analyst if we ignore their records, but still—and this is the point—past performance is far from a guarantee of future success.
In practice, there is even more uncertainty because we often do not have a complete and accurate record over many years. Some analysts are too young. Others distort their records, perhaps by selectively reporting their successes and omitting their failures. Also, alas, skills are not constant. By the time someone has compiled an impressive track record, energy and insight may be fading.
Some investors surely are more skilled than lucky. However, the records of most are brief, mixed, or exaggerated, and there is no sure way to separate the talented from the lucky and the liars. The stock market is not all luck, but it is more luck than nervous investors want to hear or successful investors want to admit.
SPECULATION VERSUS INVESTING
Decades ago, investing was haphazard. Investors figured that a stock was worth whatever people were willing to pay, and the game was to guess what people will pay tomorrow for a stock you buy today. Then John Burr Williams unleashed a revolution by arguing that investors could use something called present value to estimate the intrinsic value of a company’s stock.
Think of a stock as a machine that generates cash every few months—cash that happens to be called dividends. The key question is how much you would pay to own the machine in order to get the cash. This is the stock’s intrinsic value. People who think this way are called value investors.
In contrast, speculators buy a stock, not for the cash it dispenses, but to sell to others for a profit. To a speculator, a stock is worth what somebody else will pay for it, and the challenge is to guess what others will pay tomorrow for the stock you buy today. This guessing game is derisively called the Greater Fool Theory: Buy stocks at inflated prices and hope to sell to even bigger fools at still higher prices.
Legendary investor Warren Buffett has this aphorism: “My favorite holding period is forever.” If we think this way, never planning to sell, we force ourselves to value stocks based on the cash they generate, instead of being distracted by guesses about future prices. A Buffett variation on this theme is, “I never attempt to make money on the stock market.
I buy on the assumption that they could close the market the next day and not reopen it for five years.” If we think this way, we stop speculating about zigs and zags in stock prices and focus on the cash generated by the money machine.
The idea is simple and powerful, but often elusive. It is very hard to buy a stock without looking at what its price has been in the past and thinking about what its price might be in the future. It is very hard to think about waiting patiently for cash to accumulate when it is so tempting to think about making a quick killing by flipping stocks.
People are often lured to the stock market by the ill-conceived notion that riches are there for the taking—that they can buy a stock right before the price leaves the launchpad. This dream is aided and abetted by get-rich-quick gurus peddling fantasies. I once received a letter that began “Dear friend.” There’s the first red flag: Real friends don’t address you as “Dear friend.” The letter went on, “IMAGINE turning $1,000 into $34,500 in less than one year!”
The letter said that “no special background or education” was needed and that “it’s an investment you can make with spare cash that you might ordinarily spend on lottery tickets or the racetrack.” Second red flag: I don’t buy lottery tickets or bet on horses. Where did they get my name? Why did they think I was a sucker? This was getting embarrassing.
The “secret” was low-priced stocks. To demonstrate the “explosive profit potential,” the letter listed twenty low-priced stocks and said that $100 invested in each ($2,000 in all) would have grown in the blink of an eye to $26,611. Not only that, but another stock, LKA International, had gone from 2 cents to 69 cents a share in a few months, which would have turned $1,000 into $34,500. The letter concluded by offering a special $39 report that would give me access to “the carefully guarded territory of a few shrewd ‘inner circle’ investors.”
That was the third red flag. Why would anyone who had a real get-rich-quick system peddle it instead of using it? Why waste precious time selling newsletters for $39? Some self-proclaimed gurus assert that they have made more than enough money and want to share their secrets with others who are as poor as they once were: “If a loser like me can make money, so can a loser like you!” If they are truly rich, why don’t they send us money instead of asking for more?
It is preposterous to think that the stock market gives away money. There is a story about two finance professors who see a $100 bill on the sidewalk. As one professor reaches for it, the other says, “Don’t bother; if it were real, someone would have picked it up by now.” Finance professors are fond of saying that the stock market doesn’t leave $100 bills on the sidewalk, meaning that if there were an easy way to make money, someone would have figured it out by now.
There is truth in that, but it is not completely true. Stock prices are sometimes wacky. During speculative booms and financial crises, the stock market leaves suitcases full of $100 bills on the sidewalk. Still, when you think you have found an easy way to make money, you should ask yourself if other investors have overlooked a $100 bill on the sidewalk or if you have overlooked a logical explanation.
Investors make voluntary transactions—some buying and others selling—and a stock won’t trade at 2 cents if it is clear that the price will soon be 69 cents. Even if only a shrewd inner circle know that the price will soon be 69 cents, they will buy millions of shares, driving the price today up to 69 cents.
When LKA traded at 2 cents a share, there was an equal number of buyers and sellers, neither side knows for sure whether the price would be higher or lower the next day or the day after that. The optimists bought and the pessimists sold. The optimists happened to be right this time. But to count on being right every time, buying stocks at their lowest prices and selling at their highest, is foolish.
Probably the most successful stock market investor of all time is Warren Buffett, who made about 20 percent a year over some fifty years. This isn’t close to the fantasies concocted by dream peddlers, but it is absolutely spectacular compared to the performance of the average investor, who has made about 10 percent a year.
Some stocks do spectacularly well, just as some lottery tickets turn out to be winners. But it is a delusion to think that you will become an instant millionaire by buying stocks or lottery tickets. A more realistic goal is to make intelligent investments and avoid financial potholes.
The key is to resist the temptation to buy and sell stocks based on wishful thinking about prices. Instead, think of stocks as money machines and think about what you would be willing to pay for the cash they generate over an indefinite horizon. If you do, you will be a value investor—and glad of it.
Section 10(b)5 of the 1934 Securities and Exchange Act makes it “unlawful for any person to employ any device, scheme or artifice to defraud or to engage in any act, practice or course of business which operates as a fraud or deceit upon any person.”
This law was enacted in response to a variety of fraudulent activities in the 1920s that manipulated stock prices and misled naive investors. For instance, an investment pool could push a stock’s price up, up, and up by trading the stock back and forth among members of the pool, and then sell to investors lured by the stock’s upward momentum.
Such activities have not disappeared completely. In 1987 a con man was sentenced to two-and-a-half years in prison after he pleaded guilty to conspiracy and fraud in manipulating the prices of two small stocks by buying and selling shares through fifty-three ac-counts at eighteen brokerage firms.
The U.S. Securities and Exchange Commission (SEC) also uses Section 10(b)5 for a very different purpose—to prosecute perceived insider-trading abuses. Insider information is not even mentioned, let alone defined in the law, but over the years, through a series of court cases, the SEC has created a set of legal precedents. The SEC has also gone after insider traders for related crimes, such as mail or wire fraud, obstruction of justice, and income tax evasion.
The SEC interprets illegal insider trading as that based on important information that has not yet been made public if the information was obtained wrongfully (such as by theft or bribery) or if the person has a fiduciary responsibility to keep the information confidential. Nor can investors trade on the basis of information that they know or have reason to know was obtained wrongfully.
The SEC is unlikely to press charges if it is convinced that a leak of confidential information was inadvertent; for example, a conversation overheard on an airplane. However, courts have consistently ruled that a company’s officers and directors should not profit from buying or selling the stock before the public announcement of the important corporate news.
The SEC has also won cases against relatives and friends of corporate insiders, establishing the principle that someone tipped by an insider is an insider, too. In the 1968 Texas Gulf Sulphur case, involving the purchase of stock by company executives and outsiders who had been tipped before the public announcement of an enormous ore discovery, a federal appeals court ruled that “anyone—corporate insider or not—who regularly receives material non-public information may not use that information to trade in securities without incurring an affirmative duty to disclose.”
The SEC has frequently won cases based on a different principle—that insider trading is robbery, the theft of information. In 1984, a federal court of appeals upheld the conviction of Anthony Materia, a printing house employee, who had decoded documents (with missing company names) that he was printing relating to mergers and takeovers and then bought shares in the target companies. The court ruled that he violated a duty to his employer and its clients when he “stole information to which he was privy in his work. . . .
Materia’s theft of information was indeed as fraudulent as if he had converted corporate funds for his personal benefit.”
Please don’t take chances of cheating and hoping to outwit the SEC. It isn’t worth it and you don’t need to break any laws. You can make more than enough money by being an honest value investor.
Investment decisions depend not only on known facts but also human emotions like greed and overconfidence, which lead some investors astray and leave opportunities for others. This is why the stock market is only semi-efficient. Here are three examples.
Confusing a great company with a great stock. We eat in a good restaurant, buy a fun toy, admire attractive clothing, see a wonderful movie, ride in a nice car, and think, “I should buy stock in that company.” Before rushing to buy, ask yourself whether you are the only one who knows how wonderful these things are. If not, the stock price probably already reflects the fact that it is a good company. The question is not whether the products are worth buying, but whether the stock is worth buying. If the stock’s price is too high, the answer is no.
Later, you will see that the glamour stocks of companies that make great products and generate strong profits are more likely to be overpriced than are the stocks of struggling companies. Who has the courage to buy stock in bad companies? Not many, which is why struggling companies’ stocks are often cheap.
Buying hot tips. My family went on a vacation to Costa Rica in 2010 and we met a man named David who bragged that he was paying for his vacation with the profits he was going to make on a stock investment. I asked him about the stock and was not surprised when he said that he had been given a hot tip by his auto mechanic on a stock that had nothing whatsoever to do with cars.
The hot tip was Ecosphere Technologies (ESPH), a small company that owns several environmental technologies, including the Ecos PowerCube, a solar-power generator that can be used in remote locations. David bought 10,000 shares at $1 per share in early March. The price had jumped to $1.65 in late March and his $6,500 profit was enough to pay for his Costa Rica vacation.
The problem was that year after year, Ecosphere Technologies either lost money or made a small profit. There was nothing to prop up the price except people like David, who were buying the stock based on a hot tip.
Chasing trends. I was talking to a friendly guy named Lou at a party and he told me that, because he lives in California, he gets up at five thirty every morning so that he can be at his computer, ready to trade, when the stock market opens at six thirty Pacific Time. He has a software program that alerts him when it notices a stock price going up by some preset amount;
for example, one percent since the market opened or one percent in the past hour. Lou watches the price for a few moments and, if he likes what he sees, he buys the stock and holds it until the price goes down enough to persuade him that the run-up is over.
I asked Lou how he was doing and was surprised by his candor: “I lower my tax bill every April.” He explained that, on balance, he always seems to have more capital losses than gains and he can use his net capital losses to reduce his taxable income. He said that he is still trying to perfect his timing but, more often than not, he either buys the stock too late (after the run-up is over) or sells too late (after he has lost money).
Lou’s explanation is just a roundabout way of saying that chasing trends doesn’t work because what a stock price has done in the past is an unreliable predictor of what the price will do in the future.
Sometimes, there are a lot of folks like Lou out there, chasing the same trends. They notice stock prices going up (perhaps because their friends are bragging about how much money they are making) and rush to buy so that they can make money, too. When lots of trend chasers are buying, their lust can push prices higher still, luring more trend chasers. This imitative behavior fuels speculative bubbles—stock prices going up for no reason other than people are buying stock because prices have been going up.
When stock prices stop going up, they go down very fast because there is no reason to buy other than a belief that prices will go up.
THE DELUSION OF CROWDS
Michael C. Jensen, a Harvard Business School professor, wrote, “The vast scientific evidence on the theory of efficient markets indicates that, in the absence of inside information, a security’s market price represents the best available estimate of its true value.” The idea is that while some investors may substantially overestimate the value of a stock, other investors will err in the other direction, and these errors will balance out so that the collective judgment of the crowd is close to the correct value.
The wisdom of crowds has a lot of appeals. The classic example is a jelly bean experiment conducted by finance professor Jack Treynor. He showed fifty-six students a jar containing 850 jelly beans and asked them to write down how many beans they thought were in the jar. The average guess was 871, an error of only 2 percent. Only one student did better. This experiment has been cited over and over as evidence that the average opinion of the value of a stock is likely to be close to the “correct” value.
The analogy is not apt. As Treynor noted, the student guesses were made independently and had no systematic bias. When these assumptions are true, the average guess will, on average, be closer to the true value than the majority of the individual guesses. That is a mathematical fact. But it is not a fact if those assumptions are wrong.
After the initial student guesses were recorded, Treynor advised the students that they should allow for airspace at the top of the bean jar and that the plastic jar’s exterior was thinner than a glass jar. The average estimate increased to 979.2, an error of 15 percent. The many were no longer smarter than the few. “Although the cautions weren’t intended to be misleading,” Treynor wrote, “they seem to have caused some shared error to creep into the estimates.”
There is a lot of shared error in the stock market. Investor opinions are not formed independently and are not free of systematic biases. Stock prices are buffeted by fads, fancies, greed, and gloom—what Keynes called “animal spirits.” Contagious mass psychology causes not only pricing errors but speculative bubbles and unwarranted panics.
In an investor survey near the peak of the dot-com bubble in 2000, the median prediction of the annual return on stocks over the next ten years was 15 percent. It wasn’t just naive amateurs. Supposedly sophisticated hedge funds were buying dot-com stocks just as feverishly as small investors. This was not collective wisdom; this was a collective delusion. They didn’t see the bubble because they did not want to see it. The actual annual return over the next ten years turned out to be -0.5 percent.
The 2013 Nobel Prize in Economics was given to two economists with very different views of the efficient market hypothesis. As described by Chicago professor Eugene Fama:
An “efficient” market for securities . . . [is] a market where, given the available information, actual prices at every point in time represent very good estimates of intrinsic values.
Yale professor Robert Shiller has a very different view:
One form of this argument claims that . . . the real price of stocks is close to the intrinsic value. . . . This argument for the efficient markets hypothesis represents one of the most remarkable errors in the history of economic thought.
Fama believes that markets set the correct price, the price that God herself would set, and that changes in stock prices are hard to predict because they are caused by new information, which, by definition, cannot be predicted.
Shiller’s view is that changes in stock prices may be hard to predict because of unpredictable, sometimes irrational, revisions in investor expectations—as if God determined stock prices by flipping a coin. If so, changes in market prices are impossible to predict, but market prices are not good estimates of intrinsic value.
What they both agree on is that it is hard to predict changes in stock prices. It is tempting to think that, as in any profession, good training, hard work, and a skilled mind will yield superior results. And it is especially tempting to think that you possess these very characteristics.
A student did a term paper in my statistics class at Pomona College where 200 randomly selected students were asked if their height, intelligence, and attractiveness were above average or below average compared to other Pomona students of the same gender. The reality is that an equal number are above average and below average.
Individual perceptions were quite different. Fifty-six percent of the female students and 49 percent of the males believed their height was above average, well within the range of sampling error. However, 84 percent of the females and 79 percent of the males believed their intelligence was above average; and 74 percent of the females and 68 percent of the males believed their attractiveness was above average.
It is a common human trait, probably inherited from our distant ancestors, to overestimate ourselves. Back then, it was hard to survive in a challenging, often unforgiving world without confidence. Self-confidence had survival and reproductive value and came to dominate the gene pool. These days, we still latch on to evidence of our strengths and discount evidence to the contrary. This is so common that it even has a name: confirmation bias.
We think we can predict the result of a football game, an election, or a stock pick. If our prediction turns out to be correct, this confirms how smart we are. If our prediction does not come true, it was just bad luck—poor officiating, low voter turnout, the irrationality of other investors.
Very few investors think they are below average, even though half are. After all, would people sell one stock and buy another if they thought they would be wrong more often than right? Every decision that works out confirms our wisdom. Every mistake is attributed to bad luck beyond our control.
This overconfidence is why people trade so much, thinking they know more than the investors on the other side of their trades. It is why investors don’t hold sufficiently diversified portfolios, believing that there is little chance that the stocks they pick will do poorly. It is why investors hold on to their losers, believing that it is only a matter of time until other investors realize how great these stocks are.
WARREN BUFFETT AND SOME CONTRARIANS
Human sentiments like greed and overconfidence illustrate the crucial difference between possessing information and processing information. Possessing information is knowing something about a company that others do not know. Processing information is thinking more clearly about things we all know.
Warren Buffett did not beat the market for decades by having access to information that was not available to others, but by thinking more clearly about information available to everyone. Beginning in 1956 with a $100,000 partnership, he earned a 31 percent compound annual rate of return over the next fourteen years, with never a losing year.
In 1969, feeling stocks to be overpriced, Buffett left the stock market and dissolved the partnership. The “wizard of Omaha” returned to the stock market in the 1970s, making investments through Berkshire Hathaway, formerly a cloth-milling company. Continuing to earn nearly 20 percent a year, his net worth was $65 billion in 2016.
In the 1980s I debated the efficient market hypothesis with a prominent Stanford professor. I said that Buffett was evidence that the market could be beaten by processing information better than other investors. His response was immediate and dismissive: “Enough monkeys hitting enough keys . . .”
He was referring to the classic infinite monkey theorem, one version of which states that a handful of monkeys pounding away at typewriters will eventually write every blog that humans have ever written. One eternal monkey could do the same, but a very large number of monkeys could be expected to do it sooner.
The Stanford professor’s argument was that with so many people buying and selling stocks over so many decades, one person is bound to be so much luckier than the rest as to appear to be a genius—when he is really just a lucky monkey.
In a 1984 speech at Columbia University celebrating the fiftieth anniversary of Benjamin Graham and David Dodd’s value-investing treatise, Security Analysis, Buffett rebutted the lucky-monkey argument by noting that he personally knows eight other portfolio managers who, like Buffett, adhere to the value-investing principles taught by Graham and Dodd. All nine have outperformed the market dramatically for many years. How many monkeys would it take to generate that performance?
Yet many academics are skeptical (or perhaps jealous?). In 2006, Austan Goolsbee, a Chicago Booth School of Business professor who served as chair of the Council of Economic Advisers for President Obama, was interviewed on American Public Media and said:
I’d tell [Berkshire Hathaway] shareholders to watch their wallets. See, I’m an economist, and it always sticks in my craw when people say Warren has the Midas touch. That’s because the one thing that professors pound into young economists is that the only investors who beat the market are ones who get lucky or else take the risk.
I am unpersuaded. I have a personal interest in believing that some investors process information better than others, just as some doctors and lawyers do. My belief in Buffett is fortified by the fact that, unlike monkeys, Buffett makes sense. His annual reports are exceptionally wise and well written. They are also his own opinions, not a repackaging of what others are saying.
Too many investors are hostage to a groupthink mentality that values conformity above independent thought. Ironically, institutional groupthink is encouraged by a legal need to be “prudent.” Just as no purchasing agent ever got fired for buying IBM equipment, so no money manager has ever been thought imprudent for buying IBM stock.
As Keynes observed, “Worldly wisdom teaches that it is better for reputation to fail conventionally than to succeed unconventionally.” Who can fault someone who fails when everyone else is failing?
Buffett generally ignores the crowd and makes up his own mind. Other investors have prospered by watching the crowd and doing the opposite. In May 1932, with stock prices at their lowest level in this century, Dean Witter sent a memo to his company’s brokers and management saying:
All of our customers with money must someday put it to work—into some revenue-producing investment. Why not invest it now, when securities are cheap?
Some people say they want to wait for a clearer view of the future. But when the future is again clear, the present bargains will have vanished. In fact, does anyone think that today’s prices will prevail once full confidence has been restored?
That’s exactly right. Bargains are not going to be found when investors are optimistic, but when they are pessimistic. In Warren Buffett's memorable words, “Be fearful when others are greedy and greedy when others are fearful.”
If the herdlike instincts of institutional investors push the prices of glamour stocks to unjustifiable levels, then perhaps the road to investment success is to do the opposite—as J. Paul Getty advised in his autobiography, “Buy when everyone else is selling and hold until everyone else is buying.” A deliberate attempt to do the opposite of what others are doing is called a contrarian strategy, and it can be applied to individual stocks (buying the least popular stocks) and to the market as a whole (buying when other investors are bearish).
The experts have been notoriously wrong at dramatic market turning points: In recent years, they were optimistic before the dot-com bubble popped in the spring of 2000 and pessimistic before the market bottomed in the summer of 2002. The optimism returned as the market peaked in the summer of 2007, followed by pessimism as the market bottomed in the winter of 2009.
With individual stocks, the favorites often do poorly. A representative example is a study of the recommendations of the twenty “superstar” analysts selected in a poll of institutional investors. Of the 132 stocks they recommended, two-thirds did worse than the S&P 500. The average gain for the 132 stocks picked by the most respected and highly paid security analysts was 9 percent, as compared to 14 percent for the S&P 500. A large institutional buyer of research concluded glumly, “It’s uncanny—when they say one thing, start doing the opposite. Usually, you are right.”
These superstar pros were not throwing darts. They were infatuated with fads, overconfident of their abilities, chasing trends, or seduced by other human misperceptions. Because of these human emotions, the stock market is only semi-efficient, which is good news for value investors—who don’t throw darts, either.
What’s so hard about predicting stock prices? Anyone with open eyes can see patterns in stock prices—patterns that are a self-evident foretelling of whether prices are headed up or down.
Thirty years ago, a former student named Jeff called me with exciting news—my lectures on the futility of trying to discern profit-able patterns in stock prices were hogwash. Jeff had taken a job with IBM and, in his spare time (ha, ha!), was studying stock prices and had found some clear patterns. He was fine-tuning his system and would soon be rich. He told me that he was going to rent a helicopter and land it on the lawn outside my classroom so that he could march into my investments class triumphantly and tell students the truth.
Every year, I tell the students this story. Then I walk over to a classroom window and look outside to see if Jeff’s helicopter is parked outside. I’m still waiting.
THE NATURE OF TECHNICAL ANALYSIS
Technical analysts try to gauge investor sentiment by studying stock prices, trading volume, and various measures of investor sentiment. Technicians do not look at dividends or profits, either for individual companies or for the market as a whole. If they are studying an individual company, they don’t even need to know the company’s name. It might bias their reading of the charts.
A technical analyst can be compared to a person who watches a computer program draw lines on a monitor and tries to discover a pattern that will predict the next line to be drawn. The lines themselves are all that matters and it would be distracting to think about whether the computer program was written in Java or C++.
In the same way, the mood of the stock market can be gauged by watching stock prices; additional news about the economy or specific companies would be distracting. John Magee, who co-authored the so-called bible of technical analysis, boarded up the windows of his office so that his readings of the hopes and fears of the market would not be influenced by the sight of birds singing or snow falling.
A technician’s most important tool is a chart of stock prices. The most popular are vertical-line charts, traditionally using daily price data. Each vertical line spans the high and low prices, with horizontal slashes showing the opening and closing prices.
Humans have a natural affinity for professed experts who replace confusion and ambiguity with clarity and decisiveness. Periodically, a technical analyst is elevated to the status of a financial guru when astoundingly accurate predictions are reported in the media and devoted followers seek the advice of these celebrities.
For example, Joseph Granville was a flamboyant guru, sometimes enlivening his public speeches with vaudeville skits using a chimpanzee or ventriloquist’s dummy and at other times preaching in a prophet’s robes:
THE DATA MINING TRAP
To many, the value of technical analysis is self-evident. Any reasonably alert person can see well-defined patterns in stock prices. However, investors need a crystal ball, and stock charts provide a rearview mirror. In any set of data, even randomly generated data, it is possible to find a pattern if one looks long enough.
Ransacking data for patterns is called data mining and demonstrates little more than the researcher’s persistence. Remember this blog’s opening quotation: “If you torture the data long enough, it will confess.”
I once sent ten different charts of stock prices to a technical analyst—let’s call him Ed—and asked his help in deciding whether any of these stocks looked like promising investments.
Ed was so excited by the patterns he found in four charts that he overlooked the odd coincidence that all of the price charts started at a price of $50 a share. This was not a coincidence.
These were not real stocks. I created fictitious data from student coin flips. In each case, the “price” started at $50 and then each day’s price change was determined by twenty-five coin flips, with the price going up 50 cents if the coin landed heads and going down 50 cents if the coin landed tails. For example, fourteen heads and eleven tails would be a $1.50 increase that day. After generating dozens of charts, I sent ten of them to Ed with the expectation that he would find seductive patterns. Sure enough, he did.
When this ruse was revealed, Ed was disappointed that these were not real stocks, with real opportunities for profitable buying and selling. However, the lesson he drew from this hoax was quite different from what I intended. Ed concluded that it is possible to use technical analysis to predict coin flips!
PATTERNS IN THE STOCK MARKET
Millions of investors have spent billions of hours trying to discover a formula for beating the stock market. It is not surprising that some have stumbled on rules that explain the past remarkably well but are unsuccessful in predicting the future. Many such systems would be laughable, except for the fact that people believe in them.
Analysts have monitored sunspots, the water level of the Great Lakes, and sales of aspirin and yellow paint. Some believe that the market does especially well in years ending in five—1975, 1985, and so on—while others argue that years ending in eight are best. Burton Crane, a longtime New York Times financial columnist, reported that a man “ran a fairly successful investment advisory service based in his ‘readings’ of the comic strips in The New York Sun.”
Money magazine once reported that a Minneapolis stockbroker selected stocks by spreading the Wall Street Journal on the floor and buying the stock touched by the first nail on the right paw of his golden retriever. The fact that he thought this would attract investors says something about him—and his customers.
WHY I LOVE THE PACKERS
On Super Bowl, Sunday in January 1983, both the business and sports sections of the Los Angeles Times carried articles on the Super Bowl stock market predictor. The theory is that the stock market goes up if the National Football Conference (NFC) or a former National Football League (NFL) team now in the American Football Conference (AFC) wins the Super Bowl; the market goes down otherwise. A Green Bay Packer win is good for the stock market; a New York Jets win is bad for stocks.
This theory had been correct for fifteen of the first sixteen Super Bowls, and one stockbroker said that “market observers will be glued to their TV screens . . . it will be hard to ignore an S&P indicator with an accuracy quotient that’s greater than 94 percent.” Washington (an NFC team) won, the stock market went up, and the Super Bowl Indicator was back in the news the next year, stronger than ever. The Super Bowl system worked an impressive twenty-eight out of thirty-one times through 1997, but then failed eight of the next fourteen years.
The stock market has nothing to do with the outcome of a football game. The accuracy of the Super Bowl Indicator is nothing more than an amusing coincidence fueled by the fact that the stock market usually goes up and the NFC usually wins the Super Bowl.
The correlation is made more impressive by the gimmick of counting the Pittsburgh Steelers, an AFC team, as an NFC team. The excuse is that Pittsburgh once was in the NFL; the real reason is that Pittsburgh won the Super Bowl several times when the stock market went up. Counting Pittsburgh as an NFC team twists the data to support this cockamamie theory.
The New York Times reversed the direction of the prediction. Instead of using the Super Bowl to predict the stock market, why not use the stock market to predict the Super Bowl? Why not? It’s no more ridiculous than the original Super Bowl Indicator.
The Times reported that, if the Dow increases between the end of November and the time of the Super Bowl, the football team whose city comes second alphabetically usually wins. (Hint: Why do you suppose they chose the end of November for the starting date, as opposed to January 1, a month before the game, a year before the game, or another logical date?)
The performance of the Super Bowl Indicator has been mediocre since its discovery—which is unsurprising since there was nothing behind it but coincidence. What is genuinely surprising is that many people do not get the joke. The man who created the Super Bowl Indicator intended it to be a humorous way of demonstrating that correlation does not imply causation. He was flabbergasted when people started taking it seriously!
THE FOOLISH FOUR
In 1996, two brothers, Tom and David Gardner, wrote a wildly popular blog with the beguiling name, The Motley Fool Investment Guide: How the Fools Beat Wall Street’s Wise Men and How You Can Too. Hey, if fools can beat the market, so can we all.
The Gardners recommended the Foolish Four Strategy. They claimed that during the years 1973–1993, this strategy had an annual average return of 25 percent and concluded that it “should grant its fans the same 25 percent annualized returns going forward that it has served up in the past.”
Here’s their recipe for investment riches:
1. At the beginning of the year, calculate the dividend yield for each of the thirty stocks in the Dow Jones Industrial Average.
2. Of the thirty Dow stocks, identify the ten stocks with the highest dividend yields.
3. Of these ten stocks, choose the five stocks with the lowest price per share.
4. Of these five stocks, cross out the stock with the lowest price.
5. Invest 40 percent of your wealth in the stock with the next lowest price.
6. Invest 20 percent of your wealth in each of the other three stocks.
No, I’m not making this up.
Any guesses why this strategy is so complicated, verging on baffling? Data mining perhaps?
Steps 1 and 2 are plausible. There is a long-established investment strategy called the Dogs of the Dow that favors buying the Dow stocks with the highest dividend yields, and this sensible strategy has been reasonably successful.
The strategy is pure data mining. Step 3 has no logical foundation since a stock’s price depends on how many shares the company has outstanding. If a firm were to double the number of shares, each share would be worth half as much. There is no reason why a Dow stock with more shares outstanding (and a lower price per share) should be a better investment than a Dow stock with fewer shares outstanding (and a higher price per share).
Berkshire Hathaway (which is not in the Dow) has very few shares outstanding and consequently sells for a mind-boggling price of nearly $200,000 per share. Yet it has been a great investment.
What about step 4? Why, after selecting the five stocks with the lowest prices (as if a low price is good), would we cross out the stock with the lowest price? Why indeed.
And steps 5 and 6? Why invest twice as much money in the next lowest priced stock as in the other three stocks? We all know the answer. Because it worked historically. Period.
Shortly after the Gardners launched the Foolish Four Strategy, two skeptical finance professors tested it using data from the years 1949–1972, just prior to the period that had been data-mined by the Gardners. It didn’t work. The professors also retested the strategy during the years that were data-mined by the Gardners, but with a clever twist.
Instead of choosing the portfolio on the first trading day in January, they implemented the strategy on the first trading day of July. If the strategy has any merit, it shouldn’t be sensitive to the starting month. But, of course, it was.
In 1997, only one year after the introduction of the Foolish Four, the Gardners tweaked their system and renamed it the UV4. Their explanation confirms their data mining: “Why the switch? History shows that the UV4 has actually done better than the old Foolish Four.” It is hardly surprising that a data-mined strategy doesn’t do as well outside the years used to concoct the theory. The Gardners admitted as much when they stopped recommending both the Foolish Four and UV4 strategies in 2000.
The Foolish Four strategy was indeed foolish.
In the 1980s, an investment advisory firm with the distinguished name Hume & Associates produced The Superinvestor Files, which were advertised nationally as sophisticated strategies that ordinary investors could use to reap extraordinary profits. Subscribers were mailed monthly pamphlets, each about fifty pages long and stylishly printed on thick paper, for $25 each plus $2.50 for shipping and handling.
In retrospect, it should have been obvious that if these strategies were as profitable as advertised, the company could have made more money by using the strategies than by selling pamphlets. However, gullible and greedy investors overlooked the obvious and, instead, hoped that the secret to becoming a millionaire could be purchased for $25, plus $2.50 for shipping and handling.
COMPUTERIZED TRADING SYSTEMS
Computerized trading systems remove all human judgment. The computers are programmed to track stock prices, other economic and noneconomic data, and news stories, looking for patterns that precede stock price movements. For example, the computers might notice that after the number of stocks going down in price during the preceding 140 seconds exceeds the number going up by more than 8 percentage points, the S&P 500 usually rises.
The computer files this indicator away and waits. When this signal appears again, the computer moves fast, buying thousands of shares in a few seconds and then selling these shares seconds later. Done over and over, day after day, a profit of a few pennies (or even a fraction of a penny) in a few seconds on thousands of shares can add up to real money. The technology magazine Wired gushed that these automated systems are “more efficient, faster, and smarter than any human.”
True, these programs process data faster than any human, but they are no smarter than the humans who write the code that guides the computers. If a human tells a computer to look for potentially profitable patterns—no matter whether the discovered pattern makes sense—and to buy or sell when the pattern reappears, the computer will do so—whether it makes sense or not.
Indeed, some of the human brains behind the computers boast that they don’t understand why their computers decide to trade. After all, their computers are smarter than them, right? Instead of bragging, they should be praying.
On May 6, 2010, the U.S. stock market was hit by what has come to be known as a “flash crash.” Investors that day were nervous about the Greek debt crisis, and an anxious mutual fund manager tried to hedge his portfolio by selling $4.1 billion in S&P 500 futures contracts. The idea was that if the market dropped, the losses on this fund’s stock portfolio would be offset by profits on its futures contracts. This seemingly prudent transaction somehow triggered the computers.
The computers bought many of the futures contracts the fund was selling, then sold them seconds later. Futures prices started falling and the computers were provoked into a trading frenzy as they bought and sold futures contracts among themselves, like a hot potato being tossed from hand to hand.
Nobody knows exactly what unleashed the computers. Remember, even the people behind the computers don’t understand why their computers to trade. In one fifteen-second interval, the computer traded 27,000 contracts among themselves, half the total trading volume, and ended up with a net purchase of only 200 contracts at the end of this fifteen-second madness.
The trading frenzy spread to the regular stock market, and the flood of sell orders overwhelmed potential buyers. The Dow Jones Industrial Average fell nearly 600 points (more than 5 percent) in five minutes. Market prices went haywire, yet the computers kept trading. Procter & Gamble (P&G), a rock-solid blue-chip company, dropped 37 percent in less than four minutes.
Some computers paid more than $100,000 a share for Apple, Hewlett-Packard, and Sotheby’s. Others sold Accenture and other major stocks for less than a penny a share. The computers had no common sense. They blindly bought and sold because that’s what their algorithms told them to do.
The madness ended when a built-in safeguard in the futures market suspended all trading for five seconds. Incredibly, this five- second time-out was enough to persuade the computers to stop their frenzied trading. Fifteen minutes later, markets were back to normal and the temporary 600-point drop in the Dow was just a nightmarish memory.
There have been other flash crashes since and there will most likely be more in the future. Oddly enough, Procter & Gamble was hit again on August 30, 2013, on the New York Stock Exchange (NYSE) with a mini flash crash, so called because nothing special happened to other stocks on the NYSE and nothing special happened to P&G stock on other exchanges.
Inexplicably, nearly 200 trades on the NYSE, involving a total of about 250,000 shares of P&G stock, occurred within a one- second interval, triggering a 5 percent drop in price, from $77.50 to $73.61, and then a recovery less than a minute later. One lucky person happened to be in the right place at the right time and bought 65,000 shares for a quick $155,000 profit. Why did it happen? No one knows. Remember, humans, aren’t as smart as computers.
Fortunately, value investors are inoculated from the perils of technical analysis, since value investors do not try to predict stock prices. Value investors buy a stock because it is an inexpensive money machine, generating bountiful cash, it is hoped, over many, many years.
BAUBLES AND BUBBLES PONZI SCHEMES
If you mail a letter to a person in another country, you can enclose an international reply coupon that can be exchanged for postage stamps in that country and used to mail a letter back to you. It is like enclosing a self-addressed stamped envelope but gets around the problem of the sender having to buy foreign postage stamps. It is the polite thing to do, but also the source of the most famous swindle in history.
In 1920, a Massachusetts man named Charles Ponzi promised to pay investors 50 percent interest every forty-five days. Compounded eight times a year, the effective annual rate of return would be 2,463 percent! He said that his profits would come from taking advantage of the difference between the official and open market price of Spanish pesos. He would buy Spanish pesos cheap in the open market, use these pesos to buy international reply coupons, and then trade these coupons for U.S. postage stamps at the higher official exchange rate.
If everything worked as planned, he could buy 10 cents’ worth of U.S. postage stamps for a penny. (It was not clear how he would convert these stamps into cash.) In practice, he received $15 million from investors and appears to have bought only $61 in stamps.
If he didn’t invest any money, how could he afford to pay a 50 percent return every forty-five days? He couldn’t. But he could create a temporary illusion of doing so. Suppose that a person invests $100, which Ponzi spends on himself. If Ponzi now finds two people to invest $100 apiece, he can give the first person $150, and keep $50 for himself.
Now, he has forty-five days to find four people willing to invest $100 so that he can pay each of the two previous investors $150 and spend $100 on himself. These four can be paid with the money from eight new investors, and these eight from sixteen more.
Ponzi’s legacy is the Ponzi scheme. In a Ponzi scheme, money from new investors is paid to earlier ones, and it works as long as there are enough new investors. The problem is that the pool of fish is exhausted surprisingly soon. The twenty-first round requires a million new people and the thirtieth round requires a billion more.
At some point, the scheme runs out of new people and those in the last round (the majority of the investors) are left with nothing. A Ponzi scheme merely transfers wealth from late entrants to early entrants (and to the person running the scam).
Ponzi’s scam collapsed after eight months when a Boston newspaper discovered that during the time that he supposedly bought $15 million in postage coupons, the total amount sold worldwide came to only $1 million. Ponzi promised that he could pay off his investors by starting a company and selling stock to other investors. Massachusetts officials were unpersuaded. They sent Ponzi to jail for ten years.
A Ponzi scheme is also called a pyramid deal since its workings can be visualized by imagining a pyramid with the initial investors at the top and the most recent round on the bottom; the pyramid collapses when the next round doesn’t materialize. Even though it seems obvious that no one will make money unless others lose money, greed blinds participants to the likelihood that they will be among the losers.
INVESTMENTS THAT WERE TOO GOOD TO BE TRUE
Ponzi schemes are illegal frauds, but they usually disguise their true nature by promising to channel investors’ money to a unique investment or to a fabulous money manager. Our best protection is a sober reflection and common sense. If it looks too good to be true, it probably isn’t true. But greed is a powerful emotion and often tramples common sense. One of the most notorious cases involved Bernie Madoff.
For more than a decade, Madoff told investors—many of them Jewish charitable organizations—that he was earning double-digit returns year after year using a “split-strike conversion” strategy that involves buying stock and put options and writing call options. This is actually a conservative strategy that is unlikely to generate double-digit returns. Perhaps even more suspiciously, Madoff reported negative returns in only seven months over a fourteen-year period.
Skeptics looked at the ups and downs in the S&P and concluded that Madoff’s performance claims were mathematically impossible. To true believers, this added to his mystique. One of his clients raved, “Even knowledgeable people can’t really tell you what he’s doing.”
What he was doing was running a Ponzi scheme, the largest ever. In December 2008, Madoff was having severe liquidity problems and confessed to his sons that it was “one big lie,” a Ponzi scheme that was collapsing. His sons reported his confession to the government. Madoff was arrested and, four months later, pleaded guilty to eleven felonies.
He admitted that he had not made any real investments for nearly two decades and that there was a $65 billion shortfall between what his clients thought they had in their accounts and what they actually had. Once the lawyers had been paid, investors got back $10 billion less than their original investment.
Madoff was sentenced to 150 years in prison and sent to a federal facility in North Carolina. He told a relative, “It’s much safer here than walking the streets of New York.”
SPECULATIVE BUBBLES AND PANICS
From time to time, investors are gripped by what, in retrospect, seems to have been mass hysteria. The price of something climbs higher and higher, beyond all reason, but it’s a speculative bubble because nothing justifies the rising price except the hope that it will go higher still. Then, suddenly, the bubble pops, buyers vanish, and the price collapses. With hindsight, it is hard to see how people could have been so foolish and paid such crazy prices. Yet, at the time of the bubble, it seems foolish to sit on the sidelines while others become rich.
Some people, including Nobel laureate Eugene Fama, do not think that bubbles can even occur. They argue that since markets always set the correct prices, whatever prices those markets set must be correct.
It is hard to take this circular argument seriously if we define a bubble as a situation in which the price cannot be justified by an asset's intrinsic value, but is instead propelled by a belief that the price will keep rising. When a Beanie Baby sold for $500, was that not a bubble? What rational explanation are we overlooking?
In 2013 Fama accepted that a bubble is “an extended period during which asset prices depart quite significantly from economic fundamentals.” Yet, he danced around this definition when he argued the following:
The word “bubble” drives me nuts, frankly, because I don’t think there’s anything in the statistical evidence that says anybody can reliably predict when prices go down. So if you interpret the word “bubble” to mean I can predict when prices are going to go down, you can’t do it. . . .
I believe markets work. And if markets work those things shouldn’t be predictable. If I can predict that housing prices will go down, if the market’s working properly, they should go down now. . . . If the market’s working properly, the information should be in the prices.
The argument that prices sometimes go far above intrinsic value does not require that we know when prices will crash. Indeed, the essence of a bubble is that people do not know when it will pop.
Fama is correct in arguing that if people know that prices will go down tomorrow, prices will go down today. But he is wrong in arguing that bubbles must be predictable. And he is wrong in arguing that the fact that price changes are hard to predict proves that prices are always equal to intrinsic values. Price changes might be hard to predict because they are swayed by irrational, unpredictable emotions.
THE DOT-COM BUBBLE
It is hard to imagine life without the internet—without email, Google, and Wikipedia at our fingertips. When the electricity goes out or we go on vacation, internet withdrawal pains can be overwhelming. Cell phones only heighten our addiction. Do we really need to be online and on call 24/7? Must we respond immediately to every email, text, and tweet? Do we really need to know what our friends are eating for lunch? Apparently, we do.
Back in the 1990s, when computers and cell phones were just starting to take over our lives, the spread of the internet sparked the creation of hundreds of online companies, popularly known as dot-coms. Some dot-coms had good ideas and matured into strong, successful companies. But many did not.
In too many cases, the idea was simply to start a company with a dot-com in its name, sell it to someone else, and walk away with pockets full of cash. It was so Old Economy to have a great idea, start a company, make it a successful business, and turn it over to your children and grandchildren.
One study found that companies that did nothing more than add .com, .net, or the word “internet” to their names more than doubled the price of their stock. Money for nothing!
A dot-com company proved it was a player not by making a profit but by spending money—preferably other people’s money. (I’m not joking!) One rationale was to be the first-mover by getting big fast. (A popular saying was “Get large or get lost.”) The idea was that once people believe that your website is the place to go to buy something, sell something, or learn something, you have a monopoly that can crush the competition and reap profits.
It is not a completely idiotic idea. It sometimes even works. (Think Amazon.) Often it doesn’t. Can you name the first-movers in the personal computer revolution? (Apple survived, but Commodore, Kaypro, and Tandy are answers to trivia questions.) The fundamental problem is that there were thousands of dot-com companies and there isn’t room for thousands of monopolies. Of the thousands of companies trying to get big fast, very few can ever be monopolies.
Most dot-com companies had no profits. If investors had thought about stocks as money machines and noticed how little cash was being generated, they would have been skeptical rather than delirious. Instead, wishful investors thought up new metrics for the so-called New Economy to justify ever higher stock prices.
They argued that instead of being obsessed with something as old-fashioned as profits, we should look at a company’s sales, spending, and a number of website visitors. Companies responded by finding creative ways to give investors what they wanted. Do investors want more sales? I’ll sell something to your company and you sell it back to me.
No profits for either of us, but higher sales for both of us. Do investors want more spending? Order another thousand Aeron chairs. Do investors want more website visitors? Give stuff away to people who visit your website. Buy Super Bowl ads that advertise your website. Two dozen dot-com companies ran ads during the January 2000 Super Bowl game, at a cost of $2.2 million for thirty seconds of ad time, plus the cost of producing the ad. Companies didn’t need profits. They needed traffic.
One measure of traffic was eyeballs, the number of people who visited a page; another was the number of people who stayed for at least three minutes. Even more fanciful were hit, the number of files requested when a web page is downloaded from a server. Companies put dozens of images on a page, and each image loaded from the server counted as a hit. Incredibly, investors thought this meant something important. They should have been thinking about money machines.
Stock prices tripled between 1995 and 2000, an annual rate of increase of 25 percent. Dot-com stocks rose even more. The tech-heavy NASDAQ index more than quintupled during this five-year period, an annual rate of increase of 40 percent. Someone who bought $10,000 of AOL stock in January 1995 or Yahoo when it went public in April 1996 would have had nearly $1 million in January 2000.
In 1999, a small internet company called netj.com. filed an SEC statement that was brutally candid: “The company is not currently engaged in any substantial business activity and has no plans to engage in any such activity in the foreseeable future.” And yet the price rose from $0.50 a share to $3.50 a share in six months. A company that doesn’t do anything or plans to do anything was valued at $22.9 million—not much for a real company, but a lot for a do-nothing company.
In March 2000, the Wall Street Journal ran a front-page story that reminded me of Bernard Baruch’s comment about barbers and beauticians. At Bill’s Barbershop in Dennis, Massachusetts, a shop I’ve been to, the locals talked about dot-com stocks while they watched stock prices dance on television.
One regular said, “You get three or four times in your life to make serious bucks. If you miss this one, you’re crazy.” Another agreed: “I don’t think anything could shake my confidence in the market. Even if we do go down 30 percent, we’ll just come right back.”
Dot-com entrepreneurs and stock market investors were getting rich and they wanted to think that it would never end. But, of course, it did.
Investors should buy stocks as if they were groceries instead of perfume. —Benjamin Graham
When we buy groceries, clothing, or a television, we ask not only whether the food is tasty, the clothing attractive, and the television well built, but how much it costs. Is it worth the price?
When we buy stock, we should ask the same question—not whether it is issued by a good company, but whether the price is right. Is it worth the cost? The relevant question is not whether Amazon is a better company than Target, but whether Amazon stock, at $800 a share, is a better buy than Target stock at $80 a share.
What is a share of stock worth? We do not buy stock to eat, wear, or watch at night. We buy stock for the cash it generates: the dividends. This insight is the basis of value investing. Value investors buy stock with the expectation that, even if they never sell the stock, they will be satisfied with the dividends they expect to receive. Value investors don’t invest in postage stamps, baseball cards, or Beanie Babies because they don’t generate income.
In the same way, Benjamin Graham created an imaginary Mr. Market, a person who comes by every day offering to buy the stock you own or to sell you more shares. Sometimes, Mr. Market’s price is reasonable. Other times, it is silly. There is no reason for your assessment of your stock to be swayed by Mr. Market’s prices, though you may sometimes take advantage of his foolishness.
Stock market Events
The two main economic drivers of the stock market are the state of the economy (boom or recession) and interest rates. When the economy is strong, profits surge and dividends follow close behind. When a recession hits, profits slump and dividends grow slowly or even drop. When interest rates go up or down, so do the required returns used to determine the intrinsic value of stocks. Higher required returns reduce intrinsic values; lower required returns in-crease intrinsic values. This is why higher interest rates are bad news for the stock market and lower interest rates are good news.
It is often thought, wrongly, that the only reason interest rates affect the stock market is because they affect the economy—making it more or less expensive for firms to borrow money to expand and for households to borrow money to buy things. For example, in March 1986, a seasoned market observer wrote:
The force driving stock prices up is declining interest rates (and oil prices). Well, interest rates decline when lenders have lots of money but borrowers don’t have lots of need for it; that is, when business is slow. Like now. Every new statis-tic—retail sales off a bit, unemployment up a bit—indicates that the economy is only so-so. Which wouldn’t be so bad if the outlook for the economy were better? But it’s not. Despite lower interest rates, business doesn’t seem in a hurry to invest in new plants and equipment. . . .
What does it all mean? Only that the economy will putter along—no disaster but no great glory either—and the stock market will probably take a sharp decline.
He was seemingly oblivious to the fact that, even if the economy just putters along, lower interest rates, all by themselves, make stocks more valuable because they increase the present value of future dividends. As it turned out, the market did not take a sharp decline.
ECONOMIC VALUE ADDED
If a company has strong earnings but doesn’t pay dividends, it is clearly worth something, but the present value of the nonexistent dividends is zero. We might assume that the company will eventually pay dividends, like Microsoft, Apple, and countless other companies that never paid dividends—until they did. Value investors might predict a future date when the company will start paying dividends and predict how big the dividend will be and how rapidly it will increase.
Then they could calculate the present value of these distant dividends. Not an easy task! And what about companies like Berkshire Hathaway that generate enormous profits but may never pay a dividend, reasoning that they can help their shareholders more by using the money to continue making profitable investments?
Another appealing approach is called economic value added (EVA). Suppose that a firm has $100 million in assets and earns $15 million, a 15 percent return on its assets. So far, so good. Some profits are better than no profits. However, economic value added depends on whether earnings are higher than shareholders’ required earnings.
If shareholders require a 10 percent return, required earnings are $10 million and the firm’s $15 million in actual earnings provides an economic value added of $5 million. If, on the other hand, the shareholders’ required return is 20 percent, there is a $5 million shortfall. Instead of adding value, the firm is subtracting value.
Economic value added makes a lot of sense because it asks and answers the right questions—and it makes no assumptions about Mr. Market’s prices. The clincher is that, for firms that do pay dividends, economic value added gives the same intrinsic value as the dividend-discount model! The advantage of the EVA model is that it can be used for firms that do not pay dividends.
In the spring of 2000, Yahoo stock was one of the things I considered when assessing whether we were in a bubble. At that 36K conference, I noted that Yahoo’s stock price was $475 a share at the start of the year. Unlike most dot-coms, Yahoo was profitable, earning $55.8 million. Still, this was only 20 cents a share. Yahoo’s price-earnings ratio (P/E) was a mind-boggling 2,375. Yahoo didn’t pay a dividend and there were no dividends in the foreseeable future, so the dividend-discount model couldn’t be used to value its stock.
Could Yahoo’s price be justified by plausible assumptions? Here’s where economic value added comes in handy. With $1.24 billion in assets, a 10 percent shareholder required return implies required profits of $124 million. Yahoo’s actual $55.8 million profit in 1999 was $68.2 million less than required. Instead of economic value added, Yahoo had economic value subtracted of $68.2 million. To justify its $125 billion market value, Yahoo would have needed an annual EVA of $1.3 billion in 2000, $2.6 billion in 2001, $3.9 billion in 2002, and so on forever.
This is an interesting number because Walmart’s 1999 economic value added was $1.3 billion. So, to be worth its market value, Yahoo would have to be as profitable as Walmart in 2000, twice as profitable in 2001, three times as profitable in 2002, and so on.
It is hard to escape the conclusion that Yahoo was deliriously overvalued. The market soon came to its collective senses and Yahoo stock fell off the proverbial cliff. Yahoo’s stock plummeted 90 percent over the twelve months following the 36K conference in the spring of 2000.
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A stock's intrinsic value equals the present value of its dividends, but value investors look beyond dividends to a firm’s earnings and assets. Earnings are important because these give firms the means to pay dividends, and assets are important because these are the source of earnings. Dividend-price ratios, earnings-price ratios, and asset-price ratios are sensible financial benchmarks, but none are infallible.
THE EARNINGS-PRICE RATIO
A stock’s earnings-price ratio (E/P), or earnings yield, is a rough estimate of a stock’s rate of return. If a stock sells for $100 a share and the company earns $10 a share, it seems as though the shareholders have earned $10, which is a 10 percent return on their investment. If so, earnings yields should be related closely to the interest rates on Treasury bonds.
A dollar to shareholders, the firm must earn a return equal to the shareholders’ required return. If the firm earns less than the required return, a dollar of retained earnings is worth less than a dollar. If the firm earns more than the required return, a dollar of retained earnings is worth more than a dollar. This is why Berkshire Hathaway doesn’t pay dividends. Its managers believe that their investments will earn more than their shareholders’ required return.
The more general point is that the earnings-price ratio is an imperfect measure of the stockholders’ return. Earnings are important because they are the source of dividends. However, earnings are the means to the end, not the end itself.
THE DIVIDEND-PRICE RATIO
Some investors use the dividend yield, the ratio of dividends per share to price (D/P), as a measure of the shareholders’ return. In our example, investors see the stock’s $5 dividend and $100 price, and they estimate the stock’s return to be 5 percent. However, this calculation only makes sense if the dividend stays at $5 indefinitely. A $5 dividend, year after year, on a $100 investment is, indeed, a 5 percent return. However, most firms grow with the economy and their earnings and dividends grow, too.
THE PRICE-EARNINGS RATIO
A stock’s price-earnings ratio is obtained by dividing the price per share by the annual earnings per share. The price-earnings ratio, P/E, is the inverse of the earnings yield, E/P, and its mechanical usage to gauge whether stocks are cheap or expensive is subject to the same cautions.
Many investors have long considered a P/E ratio of 10 as normal and the purchase of a stock with a P/E ratio less than 10 a sound investment. For example, Burton Crane, a New York Times financial writer from 1937 to 1963, observed that “most of us over the age of forty, unless we are complete without market knowledge, subconsciously think of [dividend] yields of 6 percent and price-earnings ratios of 10 as about right.”
A LOW P/E IS NOT NECESSARILY A BARGAIN
Crane correctly cautioned that “no market is cheap because the price-earnings ratio is low and no market is dear because the ratio is high.” Mechanical rules ignore perfectly logical reasons why individual stocks or the market as a whole may be cheap even if the P/E ratio is high and expensive even though the P/E is low.
The intrinsic value of a stock is higher if earnings (and dividends) are expected to grow faster. Intrinsic values are also higher if interest rates are low. Thus, the market P/E tends to be high when investors are bullish on the economy and/or interest rates are low.
There are two other factors to consider: short-run fluctuations in earnings and creative accounting. If earnings are depressed temporarily by an economic recession or corporate misfortune, a stock’s price may stay relatively firm as current earnings sag, driving the P/E ratio skyward.
In these circumstances, the P/E ratio is unusually high because earnings are abnormally low. In the reverse situation, the P/E will fall if investors perceive a temporary bulge in earnings to be due to extraordinary good luck. Similarly, if investors believe that the firm has used dubious accounting procedures to boost reported earnings, the P/E ratio will be deceptively small because earnings are fictitiously large.
The bottom line is that there is no reason to think that the market's P/E should be some magic number like 10 or 14.9. There is even less reason to think this should be true of individual stocks.
USING P/ES FOR INVESTMENT DECISIONS
Although it is unwise to base investment decisions on P/E rules carved in stone—for example, buy when the P/E is below 7, sell when it is above 15—price-earnings ratios can still be used to help value investors make informed investment decisions. We will look at a model developed by John C. Bogle, the founder and former chairman of Vanguard.
Although his procedure can be used for individual securities and for short horizons, Bogle cautions that there are “two lessons that I have learned in more than a few decades in the investment field. First, the performance of individual securities is unpredictable, period. Second, the performance of portfolios of securities is unpredictable on any short-term basis.”
Thus he recommends using his approach for portfolios (such as the S&P 500) over ten-year horizons. Bogle assumes that intrinsic value is equal to the present value of the cash generated and that dividends and earnings grow at the same rate. For an investor with a ten-year horizon, this cash consists of the dividends over the next ten years and stock prices ten years from now.
Bogle’s clever insight is that stock prices ten years from now depend on two factors: the growth in earnings and the change in the Investment Benchmarks 87< P/E ratio that the market uses to value these earnings. If earnings double, the stock price will double. If the P/E ratio doubles, the stock price will double. If earnings and the P/E both double, the stock price will quadruple.
This insight also helps us understand why growth stocks are doubly vulnerable. A stock’s price is, by definition, its earnings per share multiplied by its price-earnings ratio. If earnings turn out to be lower than expected, this will reduce the stock price. If the P/E falls because of the disappointing earnings, this will reduce the stock’s price. That’s a double blow to the growth stock’s price.
To implement Bogle’s model, we need to predict the growth rate of earnings and the change, if any, in the price-earnings ratio. We can then estimate the implied rate of return using this simple approximation:stock = dividend + annual growth + annual change return yield of earnings in P/E
SEEING THROUGH THE DOOM-AND-GLOOM
The stock market did crash and euphoria turned into hysteria. In December 2008, I was interviewed on a local television show. The S&P 500 was down 40 percent from March 2000 and dot-com stocks were down more—much more. The unemployment rate was 7.8 percent and rising as the economy hurtling into the Great Recession. Who would be crazy enough to buy stocks? I raised my hand.
Here are some excerpts from my interview:
We’re in an economy that’s sick. Everyone is depressed. Everyone is gloomy. Everyone is worried about the future. And yet you gotta think about the prices, how far stock prices have fallen. So, today, you have dividend yields that are close to 4 percent, Treasury bonds are paying 3 percent. Remember back in 2000, Treasury bonds were paying 5 percent more than dividend yields. Now it’s the other way around. Dividend yields are above Treasury bonds. You have price-earnings ratios that are down to 13 or 14.
It reminds me a lot of the 1973–74 crash, which you and I both lived through, and it was just a fearful, fearful time, but 1974 in retrospect was a great time to buy stocks. And then you had the 2000 internet bubble and the crash, 2001–2002, and people were getting out of the market and said they would never go back. In retrospect, 2002 was a great time to buy stocks. . . .
I would say that the worst possible thing to do now if you are in the market is to say I’ve gotta sell everything, I can’t afford to lose any more money. What you’re doing is you’re just locking in your losses.
You and I—we don’t know what stock prices are going to be tomorrow, or next week, or next month, but . . . we can say with great confidence . . . this economy will be much bigger, much stronger ten years from now than it is today, and stock prices will be much higher. And, so, this is a time to be buying, not a time to be panicking. . . .
I’m really excited. I was so gloomy in 2000 and I was so pumped up in 1974 and I was so pumped up in 2002, and I’m pumped up again. I feel like this is a buying opportunity that only comes around a half dozen times in a lifetime. We’re not yet ten years into December 2008’s future, but the market did recover nicely, with the S&P 500 doubling over the next five years.
THE NIFTY 50
In the early 1970s, the attention of institutional investors was focused on a small group of “one decision” stocks that were so appealing that they should always be bought and never sold, no matter what the price. Value investors know that it is always a bad idea to buy or sell stocks without considering the price, and this time was no exception.
Among these select few were IBM, Xerox, Disney, McDonald’s, Avon, Polaroid, and Schlumberger. In each case, earnings had grown by at least 10 percent a year over the past five to ten years, and no reason for a slowdown was in sight. Each company was a leader in its field, with a strong balance sheet and profit rates close to 20 percent.
David Dreman recounted the dreams of missed opportunities that danced in investors’ heads:
Had someone put $10,000 in Haloid Xerox in 1960, the year the first plain copier, the 914, was introduced, the investment would have been worth $16.5 million a decade later. McDonald’s earnings increased several thousand times in the 1961–66 period, and then, more demurely, quadrupled again by 1971, the year of its eight billionth hamburger.
Anyone astute enough to buy McDonald’s stock in 1965, when it went public, would have made fortyfold his money in the next seven years. An investor who plunked $2,750 into Thomas J. Watson’s Computing and Tabulating Company in 1914 would have had over $20 million in IBM stock by the beginning of the 1970s.
The unfortunate consequence of this fixation on the Nifty 50 was the belief that there is never a bad time to buy a growth stock, nor is there too high a price to pay. A money manager infatuated with growth stocks wrote, “The time to buy a growth stock is now. The whole purpose in such an investment is to participate in future larger earnings, so ipso facto any delay in making the commitment is defeating.”
This is suspiciously similar to the Greater Fool Theory and contradicts the main tenet of value investing—that stocks, like groceries, should never be bought without regard for price. IBM is a fine company, but is its stock worth $1,000 a share? Any price, no matter how high?
The subsequent performance of many of the Nifty 50 was disappointing; yet, even today, some money managers believe that you can’t go wrong with a good growth stock, no matter what price you pay. Michael Lipper, president of Lipper Analytical Services, was very blunt: “[Suppose] an investor’s timing was exquisitely wrong and he bought a growth stock at its peak. If he held that stock until the top of the next market cycle, such an investor would be better off with a growth stock than a value play.”
There is some uncertainty about the exact composition of the Nifty 50, with one Forbes article referring to a Morgan Guaranty Trust list and several other Forbes articles referring to a Kidder Pea-body list. If any group of stocks can be clearly counted among the stocks of the Nifty 50 legend, it is the twenty-four stocks that appear on both lists.
THE DANGERS OF INCAUTIOUS EXTRAPOLATION
Benjamin Graham was wary of growth stocks because growth projections are so often based on little more than simple-minded extrapolation: “It must be remembered that the automatic or normal economic forces militate against the indefinite continuance of a given trend. Competition, regulation, the law of diminishing returns, etc. are powerful foes to unlimited expansion.”
The value of a growth stock lies in the distant future, and it is risky to extrapolate a few years of impressive growth into several decades of stunning growth. Many ludicrous examples have been concocted to dramatize the folly of incautious extrapolation. A study of British public speakers over the past 350 years found that the average sentence length had fallen from seventy-two words per sentence for Francis Bacon to twenty-four for Winston Churchill. If this trend continues (famous last words), the number of words per sentence will soon hit zero and then go negative.
It is also an incautious extrapolation to assume that a firm’s extraordinary profits will continue indefinitely.
Growth per se is not valuable. A company that reduces current dividends in order to grow bigger always increases the firm’s earnings growth (as long as profits are positive) but doesn’t increase the intrinsic value of its stock unless the profit is larger than the shareholders’ required return.
Here is a great example of how growth is not necessarily valuable. Consider a mutual fund that invests in Treasury bills—all Treasury bills all the time—paying 4 percent interest. If the fund pays all the interest to its shareholders, they earn a 4 percent return and the fund has a zero percent growth rate.
Now suppose the fund switches to a growth philosophy by paying out half the interest to its shareholders and using the other half to buy more Treasury bills. The fund is growing now, but shareholders are no better off—because all the firm did is buy Treasury bills that investors could have purchased themselves.
The Book value of a firm is the net worth shown on the accountants’ books. It is commonly calculated on a per-share basis, total assets minus liabilities, divided by the number of shares outstanding. book values are generally based on the cost of a firm’s assets, depreciated over time for presumed wear and tear. However, a firm’s value to shareholders is the cash that it generates, not the cost of its assets.
A firm that loses money year after year is of little value to shareholders even if it costs billions to construct its money-losing buildings and equipment. (Imagine a tomato farm on top of Mount Everest.) Conversely, a high-technology company operating out of a garage can be worth millions of dollars even though its garage is only worth thousands.
There is considerable evidence of abnormal returns from “value” stocks that have low prices relative to dividends, earnings, and blog value. Mechanical rules are no guarantee of success, but these valuation metrics have the virtue of cultivating a contrarian approach to investing. Value investing and contrarian investing are often two sides of the same coin.
When investors are fearful and contrarian investors look to pounce, stock prices are low relative to dividends, earnings, and blog value, and these bargain prices attract value investors. Greed, on the other hand, which is a sell sign for contrarian investors, fuels high stock prices that scare off value investors.
Eugene Fama and others who believe that the market never makes a mistake argue that the abnormal returns from value strategies must be risk premiums. Therefore, value strategies must be risky—even if the only evidence we have of their riskiness is that they are profitable. Skeptics dismiss this reasoning as circular and argue that animal spirits and other market noise create profitable opportunities for contrarians and value investors.
LOW PRICE-BOOK RATIOS
Another plausible identifier of out-of-favor stocks is the ratio of the stock price to book value. One study calculated the ratio of market value to blog value for the S&P 500 on January 1 of each of the forty-five years from 1950–1994. The years were then divided into two categories, depending on the beginning-of-year ratio of market value to book value: 1) the twenty-two years with the highest ratios and 2) the twenty-two years with the lowest ratios. (The median year was discarded.) The years with low beginning-of-year price-book ratios had an average return of 20 percent, the years with high price-book ratios 6 percent.
Similar results have been found for individual stocks. Even Fama, perhaps the most enthusiastic proponent of the efficient market hypothesis, has admitted that stocks with low price-book ratios beat the market. His explanation is unconvincing. He argues that because markets are efficient (might as well assume what you are trying to prove), low price-book stocks must have risks that we do not understand but investors fear.
A more persuasive explanation is that Mr. Market’s prices fluctuate more than intrinsic values. When Mr. Market’s prices are high relative to benchmarks like dividends, earnings, and blog value, his prices are probably too high. When Mr. Market’s prices are low relative to these benchmarks, they are probably too low.
SMALL CAPITALIZATION STOCKS
It is not profitable for large institutions to do expensive research on small companies because any attempt to buy a substantial number of shares will drive the price upward and a later sale will force the price down. If small stocks are neglected, they might also be cheap.
Several studies have found that firms with a small total market value, called small capitalization or small-cap firms, have significantly outperformed larger companies. One study considered the performance of ten portfolios revised annually by dividing 2,000 stocks into ten groups based on their total market value at the time.
Philip Fisher touted “scuttlebutt”—talking to a company’s managers, employees, customers, and suppliers and to knowledgeable people in the industry in order to identify able companies with good growth prospects. In an efficient market, if it is well known that a company is great, its stock should trade at a price that reflects its greatness. However, Fisher’s scuttlebutt might be justified by the argument that Wall Street is fixated with numbers and it takes more than numbers to identify a great company.
Since 1983, Fortune magazine has published an annual list of the most-admired companies based on surveys of thousands of executives, directors, and securities analysts. The top 10 (in order) in 2016 were Apple, Alphabet (Google), Amazon, Berkshire Hathaway, Walt Disney, Starbucks, Southwest Airlines, FedEx, Nike, and General Electric.
I looked at the performance of a stock portfolio of Fortune’s top-10 companies from 1983 through 2015. In one set of calculations, the trading day for the Fortune portfolio was the official publication date, which is a few days after the magazine goes on sale. I also looked at trading days one to four weeks after the publication date. On the trading day each year, the Fortune portfolio was reinvested in that year’s ten most admired companies.
The Fortune strategy beat the S&P 500 soundly, with respective annual returns of 15 percent versus 10 percent. It is unlikely that this difference is some sort of risk premium since the companies selected as America’s most admired are large and financially sound and their stocks are likely to be viewed by investors as very safe. By the usual statistical measures, they were safer. Nor is the difference in returns due to the extraordinary performance of a few companies. Nearly 60 percent of the Fortune stocks beat the S&P 500.
This is a clear challenge to the efficient market hypothesis since Fortune’s list is readily available public information. I have no compelling explanation for this anomaly. Perhaps Fisher was right. The way to beat the market is to focus on scuttlebutt—intangibles that don’t show up in a company’s balance sheet—and the Fortune survey is the ultimate scuttlebutt.
THE CONSERVATION OF VALUE
The idea is there, locked inside. Mergers, stock splits, stock dividends, cash dividends, share repurchases, stock sales. Many potentially confusing corporate actions seem difficult to assess. Which are good for share-holders? Which are bad? Which are meaningless?
Once again, John Burr Williams helps clarify our reasoning, this time with his Law of the Conservation of Investment Value: The value of a firm depends on the cash it generates, regardless of how that cash is packaged or labeled. Nothing is gained or lost by combining two income streams or by splitting income in two and calling one part one thing and the rest something else. Many seemingly important corporate decisions are, in fact, nonevents that do not leave $100 bills on the sidewalk.
A Business Week article was titled “How to Double Your Shares With-out Spending a Dime.” The secret is to buy a stock before it splits 2-for-1, doubling the number of shares you own. Sounds great, right?
Just be sure to buy the stock before it splits. In the Sopranos television show, Carmela has been nagging Tony to buy a stock. Finally, Tony says, “Let’s buy that stock.” Carmela makes a face: “It split. It’s too late, we missed it.”
If it’s so great for shareholders, why don’t all companies split their stock? The answer is that stock splits do nothing at all for shareholders. A stock split increases the number of shares, true enough, but it reduces the value of each share proportionately. The real mystery is why companies split their stock.
Consider a company with one million shares valued at $60 apiece, or a total market value of $60 million. If the stock splits 2-for-1, with no change in the company’s operations, its aggregate market value should stay at $60 million, implying a $30 per-share market value: A doubling of the shares halves the value of each.
If stock splits are nonevents, why do firms bother? One explanation is that if a company allows its price to rise to $200, $500, or $1,000 a share, some investors cannot afford to buy it. Periodic splits that keep the price per share under $100 keep the stock affordable.
In a market dominated by large institutions, the affordability argument is a bit strained and sometimes preposterous. American Tel-net, a television production firm, went public in March 1979 at 50 cents a share. By August 1980, the firm had yet to sell a television show and the price had sagged to 43 3/4 cents.
At this point, the firm split its stock 5-for-1 so that, according to its president, the stock would be “more affordable.” Given the structure of brokerage commissions, however, it doesn’t make sense to buy just one share of an 8-cent stock, and there is no real difference between buying 1,000 shares at 40 cents each or 5,000 shares at 8 cents each.
A somewhat more satisfactory reason for stock splits is that, traditionally, blue-chip stocks have traded in the $20 to $100 range, and some companies like to stay in that range. If a stock’s price goes up to $80 and the firm declares a 2-for-1 split, reducing the price back to $40, the split signals to investors that the board of directors is confident that the run-up was justified—not mindless speculation that, when it has run its course, will cause the price to collapse to below $20, where lesser stocks trade.
A stock that falls below $20 could do a reverse split—for example, a 1-for-2 split that halves the number of shares and doubles the price. However, resorting to this tactic would be a damaging admission by the company’s board that the firm’s prospects are not bright enough to increase the price soon.
On April 23, 2014, with Apple stock trading at $524.75, Apple announced a 7-for-1 split. Apple generally has very good reasons for doing what it does, so many investors thought that the highly unusual choice of a 7-for-1 split reflected the company’s belief that the stock price would soon approach $700, which would be $100 after the split. They were right. Four months later, Apple popped through $100.
For American Telnet, a logical explanation for the 5-for-1 split is that splitting its stock down to the penny range may attract speculators who habitually restrict their attention to penny stocks. In sharp contrast, Berkshire Hathaway, run by Warren Buffett, has never split, even when its shares cost more than $100,000 apiece. In a Christmas card to a friend, Buffett wrote, “May you live until B-H splits.”
In 1996, with Berkshire Hathaway selling for $33,000 a share, outsiders planned to buy Berkshire shares and resell them to investors in $1,000 pieces through unit investment trusts. Not wanting small investors to have to pay sales charges and other administrative expenses that such trusts would entail, Berkshire Hathaway issued Class B shares—dubbed “Baby Berkshires”—each without voting rights and worth one-thirtieth of the regular Class A shares. In January 2010, Class B shares were split 50-to-1, making each worth 1/1,500 the value of Class A shares. On January 10, 2014, Berkshire A closed at $171,322 a share and Berkshire B closed at $114.97 a share.
A stock dividend is a stock split, only smaller. If a company declares a 5 percent stock dividend, stockholders receive five additional shares for every 100 shares they hold, which is effectively a 21-for-20 stock split. The conservation-of-value principle implies that the value of each share falls by 5 percent, leaving shareholders no better or worse off than before.
Nonetheless, Barron’s Finance and Investment Handblog made this silly claim:
From the corporate point of view, stock dividends conserve cash needed to operate the business. From the stockholder point of view, the advantage is that additional stock is not taxed until sold, unlike a cash dividend.
This claim is misdirected and misleading. Companies and their shareholders may well want to retain earnings to finance expansion. But there is no need to declare a stock dividend to do so. The company can expand as planned, and it doesn’t matter at all whether the company leaves the number of shares unchanged, declares a 2-for-1 split, or declares a 21-for-20 split (a 5 percent stock dividend).
As with any stock split, the question is why bother? A stock dividend costs the firm the expenses of administering it and doesn’t give shareholders anything. If stockholders feel better off having received a stock dividend, they apparently did not notice the offset-ting dip in the value of each share, perhaps because a small price change is lost among the daily fluctuations in stock prices.
Andrew Tobias, a keen observer of the stock market, offers a similar explanation for why companies declare stock dividends.The only difference between a stock dividend and a stock split is that being a very small split, it is hoped that no prospective buyers will notice that it has taken place. . . . Sometimes it actually works.
By this logic, stock dividends are like candy-bar inflation: The company shrinks the bar and hopes that consumers will continue to pay full price. If so, Mr. Market is truly dumb.
Writing in the Harvard Business Review, a finance professor argued that stock dividends are real events and they are bad:
What about stock dividends, which are theoretically used as a way of sharing profits while conserving cash? . . . As I see it, the trouble with this approach is that shares outstanding increase at a compound rate. It’s a 5% stock dividend this year, but next year it’s 5% of 105%, and so forth. This pattern holds down growth in earnings per share.
Once again, the conservation-of-value principle guides us to the truth. Stock dividends are nonevents. Yes, stock dividends (like stock splits) reduce earnings per share, but they also increase the number of shares, leaving earnings per shareholder constant. What this finance professor’s bogus argument really demonstrates is another reason why we should not worship earnings per share. There are lots of ways that companies can increase earnings per share without benefitting shareholders:
1. Invest in marginally profitable ventures.
2. Acquire companies with low price-earnings ratios.
3. Do a reverse stock split.
A 1974 Wall Street Journal editorial criticized the myopic focus of many corporate executives on earnings per share:
A lot of executives believe that if they can figure out a way to boost reported earnings, their stock prices will go up even if the higher earnings do not represent any underlying economic change. In other words, the executives think they are smart and the market is dumb. . . .
The market is smart. Apparently, the dumb one is the corporate executive caught up in the earnings-per-share mystique. A handbook on management consulting printed this editorial and then stated, “Unfortunately, our experience shows that many corporate managers still worship earnings per share, and thus are still betting that the market is dumb.”
Dividends are paid to those who own the company’s shares on the record date. For instance, the board of directors might declare a $1 dividend to be paid on March 1 to those who own stock on the record date of February 15. To allow for the processing of transactions, the NYSE and most other exchanges use an ex-dividend (excluding dividend) date two business days before the record date; those who buy the stock ex-dividend do not receive the dividend.
The Get Rich Investment Guide published by Consumers Digest offered this moneymaking tip:
Obviously, one strategy is to know when the stock will go ex-dividend and buy a day or two before the cutoff. Then you can receive the dividends, and you can sell the shares as soon as you have [received the dividend].
So, $100 bills are lying on the sidewalk for anyone who can figure out when a stock goes ex-dividend? That doesn’t seem difficult, so there must be a flaw in this strategy. The flaw is that a stock’s price falls when it goes ex-dividend.
Consider a company with one million shares valued at $100. If the company pays a $1 dividend, the aggregate market value of the company falls from $100 million to $99 million, because its assets have been reduced by the $1 million it paid in dividends. The price of each share should drop to $99. Someone who buys the stock for $100 before it goes ex-dividend gets the $1 dividend, but can only sell the stock for $99. (I’m ignoring other, unpredictable price changes.)
If dividends are nonevents, why bother? Some shareholders may welcome using dividends to pay their bills, but they could always sell some of their stock. This is a classic puzzle in finance—why do companies pay dividends? One answer is that cash dividends are clear and unambiguous proof that the firm really is making money. Firms can use creative accounting to create an illusion of profits, but you can’t fake dividends. Firms that pay dividends are making real profits, not imaginary ones.
DIVIDEND REINVESTMENT PLANS
Many companies have dividend reinvestment plans that allow shareholders to use their dividends to buy additional shares of stock. Shareholders avoid brokerage fees and the company raises cash without paying fees to an underwriter. It sounds like win-win but it isn’t because shareholders must pay taxes on dividends they don’t receive.
Consider again a firm with one million shares priced at $100 a share, and suppose the company pays a $1 dividend and all shareholders participate in a dividend reinvestment plan. What happens? The total number of shares increases by one percent and the value of each share declines proportionately.
Each shareholder continues to own the same fraction of the company’s shares as before. The only difference is that shareholders must pay income taxes on dividends they didn’t get. It is as if the company sent a notice to its shareholders every three months: “The company is doing fine; please send money to the IRS.”
STOCK REPURCHASES AND SALES
A legendary fund manager wrote, “If a company buys back half its shares and its overall earnings stay the same, the earnings per share have just doubled.” The obvious flaw is that the firm may have to liquidate half its assets to buy back half its stock. How can earnings stay the same if the firm gets rid of half its assets?
Consider a company with 20 million shares outstanding, each valued at $20 (an aggregate market value of $400 million). This firm has $20 million in cash to distribute to its shareholders. One alternative is to pay a $1 dividend per share; another is to purchase one million shares at $20 apiece.
Remember that we are comparing purely financial transactions in order to show that share repurchases are equivalent to dividend payments. Repurchases would increase the stock price if the firm bought the stock with money that would otherwise be wasted on unprofitable ventures. But it is the abandonment of the money-losing projects that raise the price of the stock, not the decision to distribute the proceeds through repurchases instead of dividends.
A co-manager of the Federated Strategic Value Dividend Fund gave this answer to the query, “Why isn’t it better for companies to engage in stock buybacks [instead of paying dividends]?”:
A dollar of dividends, albeit highly taxed, is still a check in the mail. A share repurchase goes off into the ether and never benefits Main Street. It’s just money that could’ve come to you that didn’t. True, a dividend puts cash in shareholders’ pockets, but they can always get cash by selling some of their shares—in essence, a share-holder-determined dividend policy.
Taxes give share repurchases a clear advantage over dividends. Shareholders pay taxes on dividends, but they do not pay capital gains taxes unless they realize their gains by selling—and, even then, they don’t pay taxes on the entire sale, only the capital gain (if any). A dividend gives shareholders no alternative but to take the cash and pay taxes. With a share repurchase, shareholders have a choice. Either they can sell shares and pay taxes on the capital gains, or they can let their investment ride.
If a share repurchase is like a dividend, issuing new shares must be equivalent to reducing dividends. Our conclusion is that except for the benefits from avoiding taxes on dividends, a stock repurchase is equivalent to paying dividends and a stock sale is equivalent to reducing dividends.
STOCKS AS A HEDGE AGAINST INFLATION
Investors ought to find protection from inflation by stockpiling things going up in price. If the price of soup is going up, buy soup before the price goes up. But it is impossible to store some things, such as medical services, and expensive to store others, such as automobiles. Some assets are beyond the budgets of most investors—Iowa farmland, Arizona shopping centers, Dallas skyscrapers. Perhaps one way to invest in real assets that may appreciate with inflation is to buy stock in companies that own land, buildings, and other tangible assets.
In the long run, dividends, earnings, and stock prices have increased faster than consumer prices. Many investors who suffered through the inflationary 1970s drew the understandable conclusion that inflation is bad for the stock market. However, the facts do not support this simplistic belief any more than they support the equally simple belief that inflation is good for the market.
ACCOUNTING FOR INFLATION
After-tax corporate profits often suffer during inflationary periods because taxes are levied on profits calculated according to more or less standard accounting principles, and some of these principles don’t make much sense during inflations. For instance, accountants spread out the cost of buildings, machines, and equipment with an annual depreciation expense that, in theory, reflects what it costs to replace things as they wear out.
In practice, depreciation expenses are based on original costs, which is like figuring labor costs on the basis of wages twenty years ago. The result is that inflation causes capital costs to be understated and profits to be overstated so that businesses, particularly those with lots of buildings, machines, and equipment, pay higher taxes.
On the other hand, conventionally measured profits are misleadingly low during inflations to the extent they ignore capital gains on corporate assets and liabilities. Just as households profit when they borrow at a fixed interest rate to buy a house whose value increases during an inflation, so do corporations that borrow to buy buildings and equipment. Yet such gains are not included in reported profits unless the firm sells its real estate or retires its debt.
Anchoring is a general human tendency to rely on a reference point when making decisions. A student did a term paper in one of my statistics classes in which randomly selected students were asked one of these two questions:
The population of Bolivia is 5 million.
Estimate the population of Bulgaria.
The population of Bolivia is 15 million.
Estimate the population of Bulgaria.
Those who were told that Bolivia’s population was 15 million tended to give higher answers than did those told that Bolivia’s population was 5 million. Several similar questions confirmed this pattern. People use the known “fact” as an anchor for their guess.
When we buy a car, we tend to judge whether we are getting a good deal by comparing the final negotiated price to the dealer’s initial price, no matter how unrealistic the initial price. Thus, a good salesman starts the haggling at a high price.
In real estate, many people use the price they paid for their home as an anchor for its current value: “Our house can’t be worth $300,000; we bought it for $400,000.” This anchoring causes some homeowners to behave in ways that are, well, irrational.
People who paid low prices for their condos in the early 1980s could make profits selling in the early 1990s, but people who bought in the late 1980s would have losses. A study found that sellers facing losses tended to ask higher prices for their condos. Not surprisingly, their high-priced condos went unsold. These condo owners were using their purchase price as an anchor for what their condos were worth and were so averse to taking a loss that they would rather not sell.
This behavior was not rational. There is no reason why condos purchased during a time of high prices are worth more than condos purchased during a time of low prices. It certainly makes no difference to buyers. If it makes a difference to sellers, they pay for their irrationality by not being able to sell their condos.
You buy a colossal ice cream sundae for a special price but, halfway through, you’re feeling sick. Do you finish the sundae because you want to eat what you paid for? The relevant question is not how much you paid, but whether you would be better off eating the rest of the ice cream or throwing it away.
You have season tickets to college football games in the Midwest. Come November, the team sucks and the weather is worse. Do you go to games because you already paid for the tickets? The relevant question is whether you would be happier at the game or somewhere else.
There is nothing to be gained and much to be lost by moping about things you can’t change. Things that can’t be changed are called sunk costs. The ice cream sundae you bought but would get sick finishing is a sunk cost. So are tickets to a miserable football game. So is the price you paid for a stock. Yet many investors are reluctant to sell losers, despite the tax benefit, because selling for a loss is an admission that they made a mistake buying the stock in the first place.
In the early 1980s a famous Yale professor boasted to a friend of mine that he made 25 percent a year during the terrible bear market in the 1970s. Surely, my friend asked, you must have bought some stocks that went down? He nearly fell out of his chair laughing when the Yale professor replied, “Yes, but I haven’t sold them yet!” Then my friend realized that the professor was not trying to be funny.
The price you pay for a stock is a sunk cost. We should think about whether a stock is cheap or expensive at its current price, not whether the price is higher or lower than the price we paid, but it is hard to forget that sunk cost.
REGRESSION TO THE MEAN
Horace Secrist had a distinguished career as a professor of economics at Northwestern University. He wrote thirteen textblogs and was director of Northwestern’s Bureau of Economic Research. In 1933, in the depths of the national economic tragedy that became known as the Great Depression, he published a blog that he hoped would explain the cause, provide solutions, and secure his legacy.
Secrist and his assistants spent ten years collecting and analyzing data for seventy-three different industries, including department stores, clothing stores, hardware stores, railroads, and banks. He compiled annual data for the years 1920 to 1930 on several metrics of business success, including the ratios of profits to sales and profits to assets.
For each ratio, he divided the companies in an industry into quartiles based on the 1920 values of the ratio: the top 25 percent, the second 25, the third 25, and the bottom 25. He then calculated the average ratio from 1920 to 1930 for the stocks in each 1920 quartile. He found that the companies in the top two quartiles in 1920 were more nearly average in 1930 and that the companies in the bottom two quartiles in 1920 were also more nearly average in 1930.
He had evidently discovered a universal economic truth. American business was converging to mediocrity. His blog documenting his discovery was titled The Triumph of Mediocrity in Business. The blog was a statistical tour de force, 468 pages long, with 140 tables and 103 charts, supporting his remarkable discovery.
Then a brilliant statistician named Harold Hotelling wrote a devastating review that politely but firmly demonstrated that Secrist had wasted ten years proving nothing at all. What Secrist became famous for was being fooled by regression toward the mean.
THE STATISTICAL FALLACY
I once did some research with a student on regression to the mean in baseball. Not 468 pages, but at least we recognized regression when we saw it. A referee for the journal that published our paper wrote:
There are few statistical facts more interesting than regression to the mean for two reasons. First, people encounter it almost every day of their lives. Second, almost nobody understands it.
The coupling of these two reasons makes regression to the mean one of the most fundamental sources of error in human judgment, producing fallacious reasoning in medicine, education, government, and, yes, even sports. Yes, we encounter regression to the mean almost every day and, yes, almost nobody understands it. This is a lethal combination—as Secrist discovered.
To understand regression, suppose that 100 people are asked twenty questions about world history. Each person’s “ability” is what his or her average score would be on a large number of these tests. Some people have an ability of 90, some 80, and some near zero.
Someone with an ability of 80 will average 80 but is not going to get 80 percent correct on every test. Imagine a test bank with zillions of questions. By the luck of the draw, a person with an ability of 80 will know the answers to more than 80 percent of the questions on one test and to fewer than 80 percent on another test. A person’s score on any single test is an imperfect measure of ability.
What, if anything, can we infer from someone’s test score? A key insight is that a person whose test score is high relative to the others who took the test probably also had a high score relative to his or her own ability. Someone who scores in the 90th percentile could be someone of more modest ability (perhaps the 85th, 80th, or 75th percentile in ability) who did unusually well, or someone of higher ability (perhaps the 95th percentile in ability) who did poorly. The former is more likely because there are more people with ability below the 90th percentile than above it.
If this person’s ability is, in fact, below the 90th percentile, then when this person takes another test, his or her score will probably also be below the 90th percentile. Similarly, a person who scores well below average is likely to have had an off day and should anticipate scoring somewhat higher on another test. This tendency of people who score far from the mean to score closer to the mean on a second test is an example of regression toward the mean.
Regression does not imply that everyone will soon get the same score on history tests, only that scores fluctuate around ability.
In the same way, Secrist’s study of successful and unsuccessful companies involved regression toward the mean. In any given year, the most successful companies are likely to have had more good luck than bad and to have done well not only relative to other firms but also relative to their own long-run profitability.
The opposite is true of the least successful companies. This is why the subsequent performance of the top and bottom companies is usually closer to the average company. At the same time, their places at the extremes are taken by other companies experiencing fortune or misfortune. These fluctuations do not mean that all companies will soon be mediocre. As Hotelling put it, Secrist “really prove[d] nothing more than that the ratios in question have a tendency to wander about.”
REGRESSION IN THE STOCK MARKET
In Against the Gods, a bestselling, prize-winning blog, Peter Bern-stein wrote:
The track records of professional investment managers are also subject to regression to the mean. There is a strong probability that the hot manager of today will be the cold manager of tomorrow, or at least the day after tomorrow, and vice versa. . . . [T]he wisest strategy is to dismiss the manager with the best track record and to transfer one’s assets to the manager who has been doing the worst; this strategy is no different from selling stocks that have risen the furthest and buying stocks that have fallen furthest.
Bernstein is wise, but this is not wisdom. The idea that the best will be the worst and the worst will be best is the gambler’s fallacy that good luck makes bad luck more likely. It is false and it is not regression to the mean. Regression occurs because the managers with the best track records probably benefited from good luck and are consequently not as far above average in ability as they seem.
If there is any skill to stock picking, the person with the best track record can be expected to outperform the person with the worst record, but not by as much next year as last year. If stock picking is all luck, you may as well pick managers randomly—or save money by not using a manager at all—but there is no reason to choose the worst manager.
SHRUNKEN EARNINGS PREDICTIONS ARE BETTER PREDICTIONS
There is regression to the mean in stocks, as well as managers. A natural tendency in the stock market, where investors hope to invest in the next IBM, Walmart, or Google, is to see a year or two of rapidly increasing earnings and assume many years of similar rapid growth. Regression teaches us that a company with earnings up by 20 percent this year (or over the past few years) is more likely to have experienced good luck than bad luck and, most likely, will regress toward the mean in the future, disappointing overly optimistic investors.
Something very similar is true of predicted earnings. The most optimistic predictions are more likely to be overly optimistic than to be excessively pessimistic, so the companies with the most optimistic forecasts probably won’t do as well as predicted.
Two colleagues and I investigated this reasoning. Regression relates to relative values. Our guiding principle was that firms whose growth rates are predicted to be far from the mean will probably have growth rates closer to the mean. So, we adjusted the analysts’ forecasts by shrinking them toward the average forecast for all companies. Our adjusted forecasts were more accurate than the analysts’ forecasts 70 percent of the time, which is better than a 2-to-1 margin.
We didn’t use spreadsheets to predict market share, revenue, expenses, and the like. We didn’t even look at the company names. We just downloaded the forecast earnings growth rates and shrunk them toward the overall mean, using a marvelous statistical formula known as Kelley’s equation, and we out-predicted the professional predictors.
If investors are paying attention to these professional analysts (or making similar predictions themselves), stock prices are likely to be too high for companies with optimistic forecasts and too low for those with pessimistic forecasts—mistakes that will be corrected when earnings regress to the mean. If this conjecture is correct, stocks with relatively pessimistic earnings predictions may outperform stocks with relatively optimistic predictions.
Five portfolios were formed each year based on the analysts’ predicted earning growth for the current fiscal year. The most optimistic portfolio consisted of the 20 percent of the stocks with the highest predicted growth. The most pessimistic portfolio contained the 20 percent with the lowest predicted growth. The stock returns were then calculated for each portfolio over the next twelve months. A similar procedure was used for the year-ahead earnings forecasts, but the stock returns were calculated over the next twenty-four months.
The Dow Jones Industrial Average is an average of the prices of thirty blue-chip stocks that represent the most prominent companies in the United States. An Averages Committee periodically changes the stocks in the Dow, sometimes because a firm merges with another company or is taken over by another company.
More often, though, a company has some tough years and is no longer considered to be a blue-chip stock. Such fallen companies are replaced by more successful companies; for example, a thriving Home Depot replaced a struggling Sears in 1999.
When a faltering company is replaced by a flourishing company, which stock do you think does better subsequently—the stock going into the Dow or the stock going out? If you take regression into account, the stock booted out of the Dow probably will do better than the stock that replaces it.
This is counterintuitive because it is tempting to confuse a great company with a great stock. LeanMean may have a long history of strong, stable profits. But is it a good investment? The answer depends on the stock’s price. Is it an attractive investment at $10 a share? $100? $1,000? There are prices at which the stock is too expensive. There are prices at which the stock is cheap. No matter how good the company, value investors need to know the stock’s price before deciding whether it is an attractive investment.
Regarding Dow additions and deletions, the question for value investors is not whether the companies going into the Dow are doing better than the companies they are replacing, but which stocks are better investments. The stocks going into and out of the Dow are all familiar companies that are closely watched by thousands of investors.
In 1999, investors were well aware of the fact that Home Depot was doing great and Sears was doing poorly. Their stock prices surely reflected this knowledge. That’s why Home Depot’s stock was up 50 percent, while Sears was down 50 percent.
However, the regression argument suggests that the companies taken out of the Dow are generally not in as dire straits as their recent performance suggests and that the companies replacing them are generally not as stellar as they appear. If so, stock prices will often be unreasonably low for the stocks going out and undeservedly high for the stocks going in.
When a company that was doing poorly regresses to the mean, its stock price will rise. When a company that was doing spectacularly regresses to the mean, its price will fall. This argument suggests that stocks deleted from the Dow will generally outperform stocks added to the Dow.
Sears was bought by Kmart in 2005, five-and-a-half years after it was kicked out of the Dow. If you bought Sears stock just after it was deleted from the Dow, your total return until its acquisition by Kmart would have been 103 percent. Over the same five-and-a-half-year period, an investment in Home Depot, the stock that replaced Sears, would have lost 22 percent.
The S&P 500 index of stock prices during this period had a return of –14 percent. Sears had an above-average return after it left the Dow, while Home Depot had a below-average return after it entered the Dow. (The Kmart-Sears combination has been ugly, but that’s another story.)
Is this comparison of Sears and Home Depot an isolated incident or part of a systematic pattern of Dow deletions outperforming Dow additions? There were actually four substitutions in 1999. Home Depot, Microsoft, Intel, and SBC replaced Sears, Goodyear Tire, Union Carbide, and Chevron. Home Depot, Microsoft, Intel, and SBC are all great companies, but all four stocks did poorly over the next decade.
Suppose that on the day the four substitutions were made, November 1, 1999, you had invested $25,000 in each of the four stocks added to the Dow, for a total investment of $100,000. This is your Addition Portfolio. You also formed a Deletion Portfolio by investing $25,000 in each of the stocks deleted from the Dow.
After ten years, the S&P 500 was down 23 percent. The Addition Portfolio did even worse, down 34 percent. The Deletion Portfolio, in contrast, was up 64 percent.
WOULD A STOCK BY ANY OTHER TICKER SMELL AS SWEET?
Several companies have shunned the traditional name-abbreviation convention and chosen ticker symbols that are memorable for their cheeky cleverness. Southwest Airlines’ choice of LUV as its ticker symbol was related to its efforts to brand itself as an airline “built on love.” Southwest is based at Dallas Love Field and has an open-seating policy that reportedly can lead to a romance between strangers who sit next to each other.
Its onboard snacks were originally called “love bites” and its drinks “love potions,” and a Southwest spokes-man boasted about the number of romances that started on South-west flights: “At times, we feel that we are the love brokers of the sky.”
Perhaps a clever ticker symbol is an indicator that a firm’s managers are smart and creative, with a sense of humor. On the other hand, wary investors may interpret a clever symbol as a silly marketing ploy by a company that feels it must resort to gimmicks to attract investor attention. Perhaps a clever symbol is a signal of desperation rather than intelligence.
Another possibility is that clever ticker symbols matter because they are memorable. There is considerable evidence that human judgments are shaped by how easily information is processed and remembered:
1. Objects shown for longer periods of time or with greater background contrast are rated more favorably.
2. Statements like “Osorno is in Chile” are more likely to be judged true if written in colors that are easier to read.
3. Aphorisms that rhyme are more likely to be judged true; for example, “Woes unite foes” versus “Woes unite enemies.”
These arguments suggest that ticker symbols that are easily processed and recalled might be rated favorably. For example, an investor might look at pet-related companies and come across VCA Antech, which operates a network of animal hospitals and diagnostic laboratories. A ticker symbol VCAA might pass unnoticed.
But the actual ticker symbol, WOOF, is memorable. Perhaps a few days, weeks, or months later, this investor decides to invest in a pet-related company and remembers the symbol WOOF. (In a weird coincidence, it was a former student of mine who thought up the ticker WOOF.)
The market-beating performance was not because the clever-ticker stocks were concentrated in one industry. Our eighty-two clever-ticker companies span thirty-one of the eighty-one industry categories used by the U.S. government, with the highest concentration being eight companies in eating and drinking places, of which four beat the market and four did not. Nor was it due to the extraordinary performance of a small number of clever-ticker stocks: 65 per-cent of the clever-ticker stocks beat the market.
We do not know why these stocks did so well. Perhaps a clever ticker is a useful barometer of the managers’ ability, which reveals itself over time as the firm repeatedly exceeds investors’ expectations. Or perhaps a clever ticker matters because it is memorable and has a subtle but persistent influence on investors who buy the stock. Either way, it’s an unexpected $100 bill on the sidewalk.
IF HUMANS AREN’T AS SMART AS COMPUTERS
If humans are, well, human with human emotions, frailties, and inconsistencies, should we turn our investment decisions over to computers? Computers don’t have emotions, and well-written software doesn’t have inconsistencies. True enough, but computers don’t have common sense, either. Computers can identify statistical patterns but cannot gauge whether there is a logical basis for the discovered patterns.
When a statistical correlation between gold and silver prices was discovered in the 1980s, how could a computer possibly discern whether there was a sensible basis for this statistical correlation? In the 1990s, Long-Term Capital Management was bankrupted by a number of bets on correlations that did not have a persuasive explanation;
for example, relationships among various French and German interest rates. A manager later lamented that “we had academics who came in with no trading experience and they started modeling away. Their trades might look good given the assumptions they made, but they often did not pass a simple smell test.”
Humans do make mistakes, leaving profitable opportunities for others, but humans also have the potential to recognize those mistakes and to avoid being seduced by patterns that lead computers astray.