Chapter 2

The Perils of Affect

YES, IT’S AMUSING that investors poured money into all the historical bubbles described in the previous chapter. Even Bernie Madoff might envy the promoters back then. Their schemes were far less sophisticated than his, and if you got caught you didn’t go to jail and have your underwear auctioned off to boot. But it’s anything but funny to think of the millions of people who have taken brutal hits on their retirement funds and financial nest eggs.

Although the findings we’ll consider about the psychology of investor and market behavior are increasingly followed by interested investors, the majority of economists, financial academics, and Wall Street professionals still dismiss the idea that psychology plays any role in investment decisions. I’ve written about and participated in parts of this work in cognitive psychology, sociology, and experimental psychology since the late 1970s, and there is no doubt that these findings are important to our understanding of market phenomena that entrap us repeatedly; this is why we’ll look at them in some detail over the next two chapters.

Until a few years back, I continued to be perplexed by some key market events. Although a lot of the blanks had been filled in, an important piece still seemed missing from the puzzle. It’s the piece we looked at in the previous chapter: why do crowds go as berserk as they do in manias and panics? How could they pay $75,000 (in today’s purchasing power) for a rare tulip and then some months later refuse to pay $750 for it? How could investors shell out $150 a share for Red Hat, a sizzling computer software company, in early 2000, and only $3 a share not even two years later?

What is it that drives the supposedly totally rational investors of the late twentieth and early twenty-first centuries into speculative frenzies even more damaging than those of centuries ago?

Yes, cognitive psychology, social psychology, and a score of related disciplines have been extremely helpful in pointing out numerous psychological errors that investors make and how understanding these mistakes can both protect you and help to make you some very good money. Still, none of the psychological research could answer the question of how price movements can be so extreme or how euphoria can turn to panic in almost the blink of an eye.

Affect: A Powerful New Psychological Influence

Fortunately, there are now answers. Beginning with work in the early 1980s and drawing increasing interest from researchers in the last two decades, new and dramatic findings have begun to emerge to answer this question. Not only do the answers form the core of our understanding of why bubbles occur so frequently and result in such enormous price movements in manias and panics; they also address many other market questions, some of which are critical to our investment decisions, as well as others that cut through the heart of contemporary risk analysis.

The most important discovery is that of Affect, or the Affect heuristic, as it is sometimes called, which has only recently been recognized as an important component of our judgment and decision making.*3 What the Affect findings have shown is that our strong likes, dislikes, and opinions, experienced as feelings such as happiness, sadness, excitement, and fear, can, either consciously or unconsciously, heavily influence our decision-making processes.

Affect can work either on its own or in tandem with our rational decision-making processes within or outside markets. Affect is emotional, not cognitive, so it responds rapidly and automatically. The response, being emotional, need not be rational, and often isn’t.

Professor Paul Slovic, a leading authority on cognitive psychology, whose work, along with that of Daniel Kahneman, a Nobel laureate in economics, is central to our look at Affect and heuristics, wrote: “Images, marked by positive and negative affective feelings, guide judgment and decision making.”1 That is, representations of objects or events in people’s minds are tagged to varying degrees with positive or negative Affect. A rabid sports fan, for example, will have positive representations for a favored sports team and negative ones for an archrival team. Slovic’s paper continued: “People use an Affect heuristic to make judgments. . . . People consult or refer to an ‘affective pool’ (containing all the positive and negative tags associated with the representations consciously or unconsciously) in the process of making judgments.”2

We all know that we often develop very intense likes or dislikes that probably taint our judgment. And I’m sure you’ve found that if someone has a strong political or religious view, it is very hard, if not impossible, to change his or her thinking, no matter how powerful the argument you think you have presented. There is evidence that any such arguments will in fact actually only bolster your interlocutor’s view. He may, for example, categorize your argument as a stereotypical response. Similarly, the more we like an investment choice, the stronger the positive Affect we have for it; and the more we dislike a stock or industry, the more negative the Affect it produces on us. And as in the case of political views, further positive news reinforces our positive Affect for a security while negative news reinforces our negative Affect for one we don’t like.

Affect plays a central role in what have become known as dual-process theories of our mental processing of information.3 As the psychologist Seymour Epstein states, individuals understand reality through two interactive parallel processing systems. The rational-analytic system is deliberative and analytical, functioning by way of established rules and evidence (such as mathematics and engineering). The other system, which psychologists have labeled the experiential system, is intuitive and nonverbal. The experiential system draws on information derived from experience and emotional recall and encodes reality into images, metaphors, and narratives to which affective feelings have been attached.4

Being emotional, not cognitive, the experiential system is much faster than the rational-analytic system, which may take many days or weeks to gather and put together all the required parts. Notice how quickly a reaction starts to form in our minds if we think we’ll have a big investment gain (positive Affect) or we have a wipeout in a favorite stock (negative Affect) or even hear words such as “kidnap” or “drive-by shooting.” Affect can be such a powerful emotional pressure that it can insidiously override our training and experience in the marketplace, and, as we’ll soon see, it goes a long way toward explaining extreme stock mispricing.

Images and associations are pulled into the conscious mind from past, current, and hoped-for experiences. And the more intensive our positive or negative feelings are, whether about ideas, groups of people, stocks, industries, or markets, the more intensely Affect influences our decisions on them.

Affect may also have an influence on eyewitnesses to an accident or a crime. They are often poor witnesses, and find it difficult to reconstruct events, as they are focused on one emotional detail or another. The Affect system, anchored in emotions, can at times be far from completely rational.

Although analysis is critical to many decision-making circumstances, reliance on Affect and emotions is a quicker, easier, and more efficient way to navigate in a complex, uncertain, and sometimes dangerous world. In periods of great anxiety and uncertainty, it is quite natural for the experiential system, often dominated by Affect, to take over.

A key reason is that Affect often has the power to overwhelm our analytical, rational-analytic memory banks, substituting its independent memory bank of related events and their emotional accompaniments. Easy to use, yes, but sometimes extremely dangerous. Every bubble we looked at in the previous chapter, as we shall see, had Affect at its base.

You may already have guessed that Affect can also be a powerful subliminal force likely to sway an investor’s judgment when information is sizable but still incomplete or conflicting and yet a decision must be made. In a strong market investors are often mesmerized by the major gains already made and mouthwatering images of even larger gains ahead. The experiential system easily subdues the more cautious images of the rational-analytic system. In the inevitable panic that follows a bubble, the Affect images change dramatically. We no longer think of enormous gains to be made by holding stellar investments. Now we are forcibly introduced to negative Affect. The Affect system flashes out images of crushing losses ahead. The more the stock falls, the more powerful the negative Affect gets. “Sell, sell,” flashes in the mind of most investors before the stocks drop even further. Before long, the image becomes one of total doom ahead.

Affect is by no means limited to the marketplace. Successful marketers of anything from cars to the world of fashion have capitalized on the effects of Affect for decades to manipulate buyers’ preferences for their products. Researchers are beginning to study its power in motivating groups to take actions that are sometimes silly, sometimes deadly, in many other areas of behavior, from rioting sports fans to murder. At its darkest there is genocide; dozens of repulsive episodes have taken place since the Holocaust, even though the war crimes trials in Nuremberg in 1945–1946 and the International Court of Justice in The Hague in 1945 laid out rigorous punishments for it. Hatred promulgated by Affect at its extreme can become so deep that the victims are no longer thought of as humans but as predatory animals that must be exterminated in order to survive. Affect can cause behavior to move many standard deviations away from the norm.

Affect can also act very subtly. For example, you think Merck looks good and pharmaceutical stocks are depressed, but there is more information you want to go through. However, you don’t have the time to get all the data you need because the stock is moving up. You fall back on Affect, often without knowing it, because it’s in sympathy with your gut feeling and reinforces your willingness to make the decision to buy.

Reliance on Affect can mislead us, sometimes very badly. If it were always optimal to follow our affective instincts, there would be no need for the rational-analytic system of thinking to have evolved and become so prominent in human affairs.5 It’s time to move on to some of the deadly shortfalls that Affect bequeaths to markets. The experiential system, provided with psychological rocket fuel from Affect, enhances the powerful images of gains. Affect is potentially more helpful or more dangerous because of its emotional rather than cognitive basis.

Next we’ll look at four important forms of Affect, all of which have bloodied investors’ portfolios over time: (1) insensitivity to probability, (2) negatively correlated judgments of risk and benefit, (3) the Durability Bias, and (4) Temporal Construal.

1. Insensitivity to Probability

There are a number of key ways in which Affect leads to errors in judgment. The most important is that it causes us to be insensitive to the true probability for investments to increase (or fall) in price while not factoring in the reasons this should happen. When a potential outcome, such as a major gain from a stock purchase, carries sharp and strong affective meaning, the actual probability of that outcome, or changes in the probability due to changing circumstances, will tend to carry very little weight.6

Insensitivity to probability is backed by strong research findings.7 Professors George Loewenstein, Elke Weber, Christopher Hsee, and Ned Welch8 conducted a fascinating study that showed that if people think they are going to win a state lottery, their bets and their expectations about the chances of winning are likely to be similar whether the probability is 1 in 10,000 or 1 in 10 million. If people feel they are going to win, they can be willing to pay up to 1,000 times more for the same lottery odds! Interestingly, these figures are in line with what speculators paid to buy the hot stock of the day in a stock mania. Loewenstein and his coauthors note that what is going on here is that gamblers are more moved by the possibility, rather than the probability, of a strong positive consequence. The result is that very small probabilities carry great weight.

Another interesting study found that investors apparently did not care what price they paid for an exciting initial public offering (IPO) in a bubble market. The distinguished economist Robert Shiller demonstrated that if an investor wanted to buy a hundred shares of a company (say, at $10), it didn’t matter to him if the company had one million shares to sell or, having split just prior to the IPO, five million shares to offer. Shiller found that quintupling the price of the outstanding stock was immaterial to the IPO buyer, who still wanted to buy the same one hundred shares at $10 a share even though they gave him only one-fifth of the previous value, because he was convinced the price would go higher.

Insensitivity to probability is backed by other strong research findings. Yuval Rottenstreich and Christopher Hsee9 demonstrated that if the potential outcome of a gamble is emotionally powerful, its attractiveness (or unattractiveness) is relatively insensitive to changes in probability as great as from .99 to .01, or 100-fold. These findings go to the heart of the overvaluation in a bubble. If we have very strong feelings about the prospects of a stock or another investment, we will sometimes pay 100 times its real value or more. This finding captures the major reason why stock prices are driven to astronomical heights during a bubble.

Figure 2-1 shows just how dead-on these findings are. If anything, as we’ll see, overvaluations can be even greater than 100 times earnings. From the beginning of 1996 through the peak in March 2000, the NASDAQ 100 made up primarily of the largest dot-com and high-tech stocks increased 717 percent. From the high-water mark of March 2000, the index dropped 83 percent to its October 2002 low, the worst decline of a major U.S. market index since the 89 percent drop in the Dow Jones Industrial Average in the 1929–1932 period. Near the height of the bubble, the NASDAQ 100 had a P/E multiple of over 200 times earnings. Virtually all of the companies in the index had products or services that projected images of rapid growth, often far removed from any realistic chance of being attained.

Two fairly typical examples of the extent of investors’ enthusiasm for these stocks follow.

Example A: America Online (AOL) had a P/E of 200 times trailing earnings in March 2000. The company had shown spectacular earnings growth in the previous six years, and analysts believed the growth rates were likely to accelerate as millions of new customers signed up annually to use its popular online services. Using a standard earnings discount model, I calculated that to justify its then-current price, it would need approximately 18 billion subscribers, or roughly three times the population of the earth. My conclusion at the time was that a large extraterrestrial population needed to be discovered quickly to meet these “modest” growth goals.

Soon after, AOL merged with Time Warner. Much heavier than anticipated competition worldwide, a severe slowdown in online growth, and accounting methods that dramatically overstated earnings and had to be subsequently sharply revised downward, resulting in a major drop in price of the shares of the merged company. From a price of $100 in the first quarter of 2000, the value of the AOL portion of the company fell about 90 percent to its low.

Example B: eToys.com was an exciting new dot-com merchandiser that sold a large variety of toys online. eToys.com’s concept was not only to provide shoppers with the largest variety of popular toys available but also to be a substantial time-saver for users of the site. Moreover, users would benefit from discounts that would underprice almost all its other major competitors.

The fact that a number of large competitors already had or were constructing powerful online sites was dismissed by most analysts and investors, as was the fact that eToys.com’s discounting policy resulted in large losses for the firm because it did not have the sales volume to receive the large discounts that its competitors did from the major toy manufacturers. At its peak in October 1999, eToys.com had a market value of $10.7 billion, more than triple that of Toys ‘R’ Us, the largest toy retailer, which had many hundreds of stores nationwide. eToys.com’s sales were less than 1 percent of those of Toys ‘R’ Us. The company, as noted, operated at a loss, whereas Toys ‘R’ Us had a long record of profitability. eToys.com was also conceived and operated by a middle-level retail executive, and its management depth was limited. More accurately but less kindly, its management was so-so at best.

With no real business plan, the company continued to generate large losses and went into bankruptcy in 2001. All those facts were readily available at the time; however, analysts, money managers, and investors did not process the information because of the very powerful positive affective image the company conveyed, and continued to believe that the stock was an outstanding investment opportunity almost all the way to the funeral. Once again, it seems clear that when positive Affect is in high gear, rational and logical analysis is often overwhelmed by the experiential system.

Although the two stocks had unique individual characteristics, both were part of the NASDAQ Composite Index. Along with many other high-tech and dot-com issues, they were markedly overvalued through this time period. The sensitivity to the possibility of a major gain, rather than the probability of one, appears to have played an important role in the stupendous overvaluation of dot-com and high-tech stocks in the 1996–2000 dot-com bubble.

Table 2-1, which I originally constructed in November 1999, near the height of the Internet bubble, shows the price-earnings ratios (P/Es) of ten of the most popular Internet stocks at that time. The classic two-tier market of 1971–1974, which focused entirely on large, rapidly growing companies, had an average P/E of 51 for the fifty leading growth stocks at its peak, well above the normal P/E of such growth stocks of 25–35х earnings. After the stocks dropped drastically in the 1973–1974 bear market, they were long held up as the leading example of investors’ paying far too much for prospective growth.

Not so in the 1996–2000 dot-com bubble. The price-earnings ratios in Table 2-1 were as high as 1,930 times earnings; the average P/E of the group was an amazing 739. The magnitude of the excesses of this bubble can be gauged from the fact that the average P/E of the ten dot-com companies shown in Table 2-1 was fourteen times as high, on average, as the average P/E of fifty-one of the “Nifty Fifty” stocks of the two-tier market of 1971–1974. The 1996–2000 bubble companies were not small or unknown but had market capitalizations ranging from $1.6 billion to $30.1 billion, larger than the average company in the S&P 500.

We decided to find out what the fundamental value of the stocks in the table really was in October 1999, near the top of the bubble. The analysts’ consensus earnings estimates for 1999 were used as the starting point, and then the highest earnings growth rates in U.S. company history were applied for the next twenty-one years for each company in the table, after which the normal earnings growth rate of the S&P 500 was applied. The earnings growth rates attributed to the exciting concept companies were almost farcically high (see the assumptions in Table 2-1), demonstrating that even if earnings had met the almost impossible targets, which almost no company had ever achieved since the founding of the nation, the stocks would still be wildly overpriced.

The stock prices we created in column 3, which showed somewhat lower but still wildly bullish evaluations, were derived by using the discounted earnings of each company. To arrive at these prices, we used a simple earnings discount model, one of the basic methods of valuing a stock’s price.*4

Compare column 3 with column 1, the then current price of each company. The stocks in column 1 were trading from fourteen times the highest valuations the model gave a company in column 3 to slightly over two times as much on the lowest. Although the prices we gave the column 3 group, based on the hyperexpanding earnings, were preposterously high, the actual company prices in column 1 almost doubled again by March 2000, catapulting the prices of dot-com stocks sharply higher again.

Column 4 of Table 2-1 (the one column added since the 1999 presentation) shows the prices of the stocks on August 31, 2002, after the bubble imploded. The average decline of the group was 79.1 percent. Only one stock, eBay, remains well above the present-value model estimates established in column 3. One stock is a little less than 1 percent above the prices in column 3, while the remaining eight are below these estimates, some very substantially. As noted, an estimated $7 trillion was lost in the bubble in high-tech stocks and the market. As a comparison, the loss from the market’s 1987 crash from its peak to its nadir was $1 trillion.

2. Judgments of Risk and Benefit Are Negatively Correlated

Does Affect also blind us to the risk of a security or of our entire portfolio? Our investment teachings clearly state that it doesn’t. After all, risk theory has existed for fifty years, and there are untold numbers of antirisk defenses out there to protect risk from attacking our portfolios. The efficient market hypothesis (EMH) and the great preponderance of modern risk theory, used by investors, their advisors, and their mutual funds, believe that risk is solely volatility. Unfortunately, recent work on Affect provides strong evidence that the defenses most people use will not do their job.

EMH argues that the greater the risk taken, the higher the perceived rewards of an investment will be. Affect theory discovered that it doesn’t quite work this way. Professors Baruch Fischhoff, Paul Slovic, Sarah Lichtenstein, Stephen Read, and Barbara Coombs10 found that the judgments of risk and rewards are negatively correlated. That is, the greater the actual risk, the smaller the perceived gain, while the smaller the perceived risk, the greater the perceived gain. In a word, modern risk theory is turned upside down. And research into the role of Affect appears to explain why.

These findings were supported in numerous follow-up papers. Researchers asked subjects how risky various activities were. Repeatedly, subjects answered that for many potentially dangerous or hazardous situations the greater the perceived benefit or reward, the lower the perceived risk. In virtually every case the risk is there but is perceived differently depending on the magnitude of the benefits. Conversely, the lower the perceived reward or benefit, the greater the perceived risk. Researchers, for example, have found that alcoholic beverages and food additives are believed to be low in benefit and high in risk, whereas vaccines, antibiotics, and X-rays tend to be seen as high in benefit and low in risk. In the investment world higher risk is believed to have a close correlation to higher return. This psychological behavior also simply cannot exist according to current investment teachings.

Slovic, MacGregor, Malmfors, and Purchase11 surveyed members of the British Toxicological Society and found that these experts, too, produced the same inverse relation between judgments of risk and benefit.*5 12 The behavioral researchers state that a negative correlation between risk and reward occurs even when the nature of gains or benefits is distinct and qualitatively different from the nature of the risks. What is difficult for investors to understand is that psychologically risk and return appear to often be negatively correlated. In markets we believe that rationally they have to be positively correlated.

A research paper by Ali Alhakami and Paul Slovic13 indicates that the perceived risk and perceived benefit of an activity (e.g., using pesticides) were linked to the positive or negative Affect associated with the activity. The result implies that the more strongly people like an activity, such as the use of an untested treatment for cancer or the purchase of a dot-com stock, the more they judge the risks to be low and the benefits high. Conversely, the more they dislike activities, such as using coal as a source of energy, drinking alcoholic beverages, or buying stocks with so-so returns, the higher the risk levels they attribute to these.

According to Alhakami and Slovic, this result implies that people base their judgments of the risk and benefits of an activity or a technology not only on what they think about it, but also on how they feel about it. If they have an idea or concept they strongly like, they are moved to judge that the risk is low. The more they dislike an idea or concept, the higher they judge the risk. So Affect again enters into the picture, this time allowing our feelings to tamper with and alter our rational decision making and choices on risk. In the realm of finance, Yoav Ganzach14 found support for concluding that if stocks were perceived as good, they were judged to have higher returns and low risk, whereas if they were perceived as bad, they were judged to be lower in return and higher in risk. However, for familiar stocks, perceived risk and return were positively correlated, rather than being driven by this attitude.

This work is important in explaining the strong role of Affect in the perception of risk in Figure 2-1 and Table 2-1. In both cases the Affect about the investments was very positive. As indicated, it appeared that stocks were priced at many times their real values; in the face of widely followed investment principles, the risk of investing in them was probably overriden by the strong positive Affect for them. Analytical or logical comparisons, which showed how overvalued top NASDAQ stocks were in late 1999 (Table 2-1), failed to change investors’ opinions that they were low-risk holdings.

In making their evaluations, swayed by Affect, the analysts probably really did not believe the risk factor was unduly high relative to the expected returns. In my reading of large numbers of analysts’ reports near the height of the dot-com bubble, I saw very little analysis of the major risks involved. This point is important in gauging the effectiveness of modern security analysis, and the rational-analytic processes more generally, when emotional influences such as Affect are involved. The risks were often obvious, as in the case of eToys.com and many hundreds of similar stocks of companies that had limited finances, questionable management, and poor business plans but skyrocketing stock prices. Almost all these factors could be checked quickly by any competent researcher but were not, or the information did not register on the researchers. Instead, the great majority of reports discussed the enormous potential returns ahead and detailed why they should occur.

Conversely, in the 1996–2000 bubble, value stocks, which were far less risky by standard valuation yardsticks, had very negative Affect attached to them. As a result, the influence of Affect on most investors at the time made them believe that those stocks were far more risky than their valuation standards would indicate. They were often thought to be significantly more risky than IPOs or dot-com and high-tech stocks, both of which subsequently collapsed. The risk-reward situation had obviously been turned upside down during the dot-com mania. But as we’ll see, though a bubble brings these relationships out with far greater clarity, they are always there, opening the road to major opportunities.

When the Internet bubble imploded in the spring of 2000, the supposedly significantly more risky, low-reward value stocks showed stunning positive reappraisals as the carnage in dot-com and other favorites was inflicted. Once again in the investment world, we see a clear case of Affect overpowering the rational-analytic approach, and probably to a degree that has not been observed elsewhere as yet in experiential experiments. Shortly thereafter, more normal evaluations began to be applied again to both dot-com and value stocks, resulting in the reversal, to a major extent, of the valuations during the 1996–2000 bubble.

Unfortunately, before that, the strong influence of Affect on many investors, including large numbers nearing retirement, resulted in enormous losses. The outcome, reported repeatedly in the press, is that millions of people have had to continue to work for extended periods. Introducing findings from the research on Affect appears to provide a plausible explanation of this harmful phenomenon.

Though the linking of strong positive Affect with limited risk in investors’ minds, and of negative Affect with high risk, appears far clearer in a bubble environment, it also seems to be present in many other types of market circumstances, as will be discussed. As we saw on page 39, the risk-Affect correlation also has important implications for contemporary risk management and financial theory.

3. The Durability Bias

Another way that Affect tends to skew our assessments in the market arises because we tend to overestimate how long a positive or negative event or earnings surprise will have an impact on a stock and industry or the entire market itself. Professors D. T. Gilbert, E. C. Pinel, T. D. Wilson, S. J. Blumberg, and T. P. Wheatley observed a consistent tendency of market participants to overestimate the length of time a positive or negative Affect would last after experiencing a pleasant or unpleasant event.15 This effect is called the “Durability Bias.”

The finding is important in gauging the overreaction to either a positive or a negative earnings surprise as well as a host of other market positive or negative events. To take one example, oil exploration and development companies plummeted sharply in the spring of 2010, after the BP rig blew up and a major oil spill ensued in the Gulf of Mexico. The prevailing feeling at the time was that no deep drilling would be allowed again in the gulf or other continental waters around the United States for years. Too, the cleanup costs were at the time thought to be prohibitive for both BP and its partners. Within a year, deepwater drilling was allowed again off U.S. shores, and the costs, though high, were manageable by all the firms involved. Some of the stocks originally believed to be the most exposed more than doubled in less than a year.

This finding, as we shall see in Parts III and IV, would seem to be helpful in explaining both the superior performance of “worst” over “best” stocks and the consistent but opposite reaction to surprise events by these two categories.

4. Temporal Construal

Affect leads us to make misjudgments related to time. Events nearer in time are likely to be represented in terms of more concrete and specific detail. Short-term events will include sales and earnings that are higher or lower than expected, and many other expectations that analysts report.16 Meanwhile, the farther an event occurs in the future, the more probable returns are likely to be represented in terms of a few abstract or general features that contain the perceived essence of the concept of the stock under consideration. This phenomenon is called “Temporal Construal.” This Affect characteristic causes investors to extend their views of the prospects of stocks both in and out of favor far into the future. If the future outlook is negative, the results will be reversed.

Affect results in a smoothing effect on the judgment of longer-term prospects. Rather than focusing on dozens of shorter-term inputs—most positive, but some negative—focusing on the more general thoughts about exciting longer-term prospects tends to result in more favorable forecasts than those made for the short term.

As a result of a strong positive or negative Affect, a stock, an industry, or the market itself can be priced too high or too low. Professors Trope and Liberman reported that great optimism toward long-term returns might be explained by this Affect characteristic.

Temporal Construal helps explain the consistent outperformance of contrarian investment strategies over time, as well as the major overpricing of technological stocks in bubbles. In the first case (as we shall see in detail in Part IV), investors expect the worst from out-of-favor contrarian stocks and discount their prospects far into the future. In the second case, investors generally extend expectations for concept companies’ positive results as they expand their markets rapidly too far into the future. We’ve now seen a number of instances of just how overvalued such concept stocks can get as investors overestimated the length of time a positive Affect would last for favored stocks such as the dot-com issues in the tech bubbles. Professor Trope and his colleagues have produced a good deal of experimental evidence that documents the difference in the way long-term and short-term estimates are constructed.

A glaring case in point is the expectations before the dot-com bust about Yahoo!’s growth. Yahoo! went public in April 1996 and was the hottest of the hot IPOs through the dot-com bubble.

The stock appreciated 18,000 percent from late July 1996 to its high of $119 (adjusted for stock splits) in January 2000. Investors considered it the Web site extraordinaire, with the potential for almost unlimited growth of advertising, strong revenue streams, and enormous user growth. Yahoo!’s potential growth was considered unequaled.

The company’s sales growth in the 1996–2000 period was enormous, rising from $23.8 million to over $1.1 billion (116 percent compounded annual growth over the five-year period). However, earnings did not do quite as well. The company lost money in 1996–1998 before making pennies in earnings per share in 1999 and 2000. It made only a couple of pennies a share for the entire 1996–2000 period on a stock that at its high was, as noted, close to $120. But concept ruled supreme.

In early 2000, Yahoo! had a P/E of 5,938, and most investors expected huge earnings growth in the next ten years—well over 50 percent a year. As good a company as it was, these long-term expectations were wildly overestimated. In the dot-com crash from 2000 to 2002, both a severe reappraisal of its future growth rate and the fact that Yahoo!’s earnings had grown at a far lower rate than the market expected resulted in the stock’s dropping 97 percent to its low in early fall of 2001, as the dot-com crash approached its nadir.

Another interesting instance of Temporal Construal can be observed in studies that asked investors to estimate the future performance of their stock portfolios during the 1996–2000 bubble. In 1998, near the height of the bubble, U.S. investors were extremely optimistic about the future returns of stocks. Late that year Paul Slovic, Stephen Johnson, Donald MacGregor, and I were coauthors of a study to pinpoint the expectations of a large number of mutual fund investors for the next ten years. According to our study,17 when subjects were asked to forecast their average return over the next ten years, investors were highly optimistic, estimating 14 percent annually on average. Stocks have in fact returned about 10 percent over time since the 1920s, so those enthusiastic investors appeared to be anticipating market returns in the decade ahead some 40 percent above those of the past seventy years.

The Implications of Affect on Security Analysis

In this chapter, we have seen how powerful various forms of Affect were in both fueling the bubbles and manias we observed in chapter 1 and then, as circumstances changed, sharply escalating the terror and panic when each bubble imploded. The Affect heuristic is both wondrous and frightening: astounding in its speed, subtlety, and sophistication; frightening in its power to lead us astray. According to Paul Slovic, “It is sobering to contemplate how elusive meaning is, due to its dependence upon Affect.”18

We should pause for a second to consider the forms of meaning which we take for granted and in which we justify spending immense time and expense to gather information. Will the use of “meaningful information,” such as thorough security or market analysis, be in many cases illusionary, since as we’ll see, these cases are all too often laced with Affect?

Obviously, it is not going to be easy to protect ourselves from such dangers. That is why the book will introduce disciplines you can follow that will, like a stun gun, stop Affect when it’s working against your interests. The real question is whether you can pull the trigger when the time comes. It’s harder than it seems. Still, being aware of Affect is a good first step to a better investment strategy.

The Disconnect Between Fundamentals and Price

One of the most notable characteristics of an Affect-induced bubble, as we have seen, is the enormous disconnect between fundamentals and price for analysts, money managers, and other highly trained financial professionals. The standard guidelines used to evaluate a company’s outlook are derived from the rational-analytic system.

The CFA Institute and virtually all of the academic training materials teach analysts and money managers to behave in a rational manner and thus to look at all the important fundamentals of a company to determine its price. A bible for this training is Benjamin Graham and David Dodd’s Security Analysis.19 A myriad of other books adhere to very similar methods. As we saw earlier, Graham and Dodd use a large number of financial ratios as well as detailed company information to decide on the value of a stock. The standard guidelines used to evaluate a company’s outlook sketched out earlier are most often bypassed or short-circuited.

Unfortunately, most professionals—despite their training, the state-of-the-art information available to them, and the best research available—often do no better and sometimes do worse than the average investor. This gives us a good idea of just how strong the forces of Affect actually are.

Insensitivity to probability appears to be the most important Affect characteristic that leads financial professionals to make exactly the same mistakes as the average investor by focusing on a grossly exaggerated view of the values for companies that appear at the time to have almost unlimited growth and profitability. As we’ve also seen, the finding that judgments of risk and benefit are negatively correlated, the Durability Bias, and Temporal Construal all can knock our portfolios down mercilessly. Affect can turn the playing field upside down. Unfortunately, Affect can take down the professional investor despite his extensive knowledge and training as quickly as it does the individual investor.

Almost no price seemed too high for companies that have already had explosive price appreciation as well as an exciting, if not almost irresistible, story to tell. Strong positive Affect attaches to the image that the dot-com or IPOs issues create instant wealth, and quite likely overwhelms the rational-analytic system, resulting in a major swing away from optimum decision making.

The Consequences for Security Analysis

We should ask if analysts or money managers use any rational evaluation models at all to justify extreme overpricing. While almost all claim they do, the enormous disparity between the maximum price a fundamental model would allow and the substantially higher market prices makes it clear that, as Table 2-1 demonstrated, the models could not in fact be based on accepted valuation techniques.

Looking at the sky-high prices so many analysts paid in recent years, Graham would have shaken his head, although I’ve heard from some of his still-living contemporaries that the response would have been a little more lively. Graham was ultraconservative on price-earnings and other valuation ratios, normally arguing not to buy a stock with a P/E much above 20, regardless of its outlook, as he believed “Mr. Market,” the name he coined for the market, could go to major extremes quickly.

Stocks with average prospects will normally have P/Es of 12 to 15; those with well above average earnings growth prospects will be in a 25-to-35 P/E range; and those with extraordinary growth will be at possibly forty to fifty times earnings. How, then, can we justify the fact that many stocks, recommended by scores of analysts, trade at 100 to 1,000 or more times earnings?

Can Affect Fatally Flaw Security Analysis?

Unfortunately, what comes out clearly is that investment professionals cannot accurately gauge prices in the range of approximately plus or minus 25 to 35 above or below fundamental value that financial theory holds a good analyst should, thereby allowing her or him to profit through buying undervalued or selling overvalued securities respectively. As we just saw, analysts often missed the proper valuations of Internet and high-tech stocks by fifty- to 100-fold as evidenced repeatedly in the dot-com bubble and other tech bubbles since the early 1960s. This created, at least temporarily, valuations sharply higher than any considered acceptable that were based on standard security analysis.*6

What we see, then, is that positive Affect, in extreme circumstances such as bubbles overpowers analysts following contemporary security analysis and raises prices significantly above what long-term valuation standards will sanction. In a panic, negative Affect results in the opposite case, reserved for investors who want to sell at any price. Unfortunately, the very people we depend on most to guide us to safety when a market storm appears are the first to be swept away when the terrible weather hits.

The Great Escape

The standard guideline used to evaluate a company’s outlook is based on the rational-analytic system. According to the CFA Institute and virtually all sophisticated academic training in the field, analysts and money managers behave in a rational manner and so look at all the important fundamentals of a company to determine its price, as we discussed earlier. The bible for this training, as we saw, is normally Graham and Dodd’s Security Analysis, or a similar text, as well as hundreds of pages of CFA instruction, written by highly experienced investment professionals.

Worth repeating is that security analysis can be effective in a range of approximately plus or minus 25 to 35 above or below fundamental value. So if a company is undervalued at, say, 10 times earnings, an astute analyst can buy it and perhaps double his money. However, fundamental analysis cannot work when prices move into outer space. In a bubble, a price-earnings ratio or price-to-cash-flow ratio can go from 10 to 100, or even sometimes to 1,000 or more. A price-to-book-value ratio of 4 can go to 40 or even 400 and so forth. At these levels fundamental security analysis breaks down entirely.

In a bubble or another period of extreme overvaluation, the focus is on grossly exaggerated prospects for a company or an industry that analysts or money managers forecast. Importantly in this situation, standard fundamental analytic tools, such as earnings discount models, which project highly optimistic earnings estimates several dozen years into the future, repeatedly show that the current price of the bubble stock is far too high, as we saw in Table 2-1.

What analysts, along with most people, don’t know is that most of the investment rational-analytic standards are often bypassed or short-circuited by Affect as well as by other psychology we will view. Analysts and money managers have enormous difficulty in attempting to work within the rational-analytic system when the experiential system is under full sail. Most professionals do try to get it right, but the force of powerful positive or negative Affect is difficult to cope with, as we now know. I’ve spoken at half a dozen of the CFA Institute’s National Conferences on investor psychology over more than three decades, as well as to many large societies of security analysts and to leading business schools, and I know professionals make a genuine effort to follow their fundamental methods.

Unfortunately, psychology, although widely acknowledged by these groups, is impossible to square with current investment theory, because the latter sits on a foundation solidly built on the rational-analytic approach. When this happens, analysts and money managers abandon this approach all too frequently and shift over to the experiential system, where Affect, in extreme circumstances, as we saw, easily overwhelms our rational-analytic processes.

The bottom line is that no analyst could recommend and no money manager could buy bubble stocks using rational-analytic methods. Analysts and money managers didn’t need a financial GPS to calculate how far off course the stocks’ pricing was. With this disparity in price, a sextant would have worked just as fine.

The analysts and money managers were hijacked by their expectations, which were heavily weighted by their strong positive Affect for these issues. We should see clearly now that professionals cannot accurately gauge prices in the relatively limited range*7 that financial theory holds a good analyst should, thereby allowing him to profit through buying undervalued or selling overvalued securities respectively.

Analysts missed the proper valuations, as we saw, by 50 to 100 times on many occasions in bubbles. They created valuation paradigms that justified price-earnings almost exponentially higher than any considered acceptable, employing standard analytical techniques. Does security analysis work using today’s rational-analytic methods? Possibly for brief periods of time when the water is tranquil. But don’t go out too far: remember, you’re paddling a twelve-foot kayak; you’re not in a 700-foot ocean liner.

I will be introducing a number of Psychological Guidelines that, if followed, should limit your losses in the psychological traps we will be examining through the course of the book. If they are adhered to, they will protect you from making some of the mistakes most investors are entrapped by. We should also find ways to use these predictable errors to our advantage. Here is the first one.

 

PSYCHOLOGICAL GUIDELINE 1: Do not abandon the prices projected by careful security analysis, even if they are temporarily far removed from current market prices. Over time the market prices will regress to levels similar to those originally projected.

Next, let’s extend our look into other major heuristics or mental shortcuts that we use daily to simplify our lives. Each, although extremely helpful, can also open the door to bias in our investment decisions, resulting in other serious errors that can also be very damaging. Knowing what they are and how they work can help you develop some strong defenses against their excesses.