While Good for Hunting and Gathering, Evolution Has Left Us Ill-Prepared to Survive on Wall Street

To demonstrate how behavioral finance ultimately impacts Mr. Market, we need to return to the dawn of man. Anthropological data show that anatomically modern Homo sapiens originated about 200,000 years ago. However, behaviorally, we reached modernity only about 50,000 years ago. With new agricultural techniques and domesticated animals, our hunter-gatherer ancestors started to settle into what we call civilizations only 10,000 years ago. We have made great strides in developing new communications-related technologies in the last 150 years—electricity, telephones, radios, television, computers, cell phones, and the Internet—all of which have accelerated the amount of information that is available to us and that we must process. To put this technological progress in perspective, our species may be 200,000 years old, but we have lived in an information-rich society, in its modern form, for only 150 of those years. One could argue that the current volume and velocity of information available in the financial markets, and relevant to investment decisions, started only 40 years ago. Simply put, our minds have not had enough time to evolve to a point that we can effectively process the volume of information that we experience daily in order to invest successfully.

Evolutionary biology teaches us that humans have evolved to conserve energy and avoid exertion. Professor Daniel Liberman, an evolutionary biologist at Harvard University, believes that because food was in such short supply for our hunter-gatherer ancestors, only those who conserved physical energy in hard times and expended only the energy necessary to survive had the opportunity to reproduce and flourish. In other words, we are genetically programmed to be lazy. As with our bodies, we are also conditioned to be lazy with our minds, striving to minimize our mental effort, which is also known by its more academic title—cognitive load. Here, too, we have evolved to rely on mental shortcuts whenever possible.

Evolutionary psychology teaches us that humans developed conditioned reflexes, which were driven by our need for self-preservation. For example, when our ancestors on the savanna heard a rustling in the bushes, they feared for their lives and ran away, driven by fright, confusion, and stress. Those who weren’t afraid and didn’t respond quickly enough were eaten.

To survive, humans evolved different heuristics—mental “hacks” or shortcuts—to help us navigate our ancestral environment, while conserving as much energy as possible. Unfortunately, the heuristics that allowed humans to survive and flourish thousands of years ago are potentially harmful to our health in modern society and specifically our “financial health” in the stock market.

Human feelings fall into two broad categories. When our needs are satisfied, we have positive feelings—we are happy, excited, peaceful, confident, hopeful, and engaged. On the other hand, when our needs are not being met, we have negative feelings—we are angry, afraid, confused, fatigued, stressed, embarrassed, jealous, sad, and vulnerable.

Behavioral Finance Explains the Errors of Our Hunter-Gatherer Brains

These positive and negative feelings often drive individual behavior and can result in any number of cognitive biases, which are defined as mistakes in reasoning, evaluating, remembering, or some other cognitive process. In investing, these biases often express themselves as fear and greed, which can lead individuals to make suboptimal investment decisions.

Behavioral finance has flourished in the 40 years since Kahneman and Tversky first published their research and established its theoretical foundation. It is important to recognize that behavioral finance comprises two distinct, albeit interrelated disciplines: micro behavioral finance, which deals with decision making and the behavior of individuals, and macro behavioral finance, which deals with the behavior of the collective.⁵ Although actions of the individual can influence the actions of the collective, the relationship is not a direct one, which appears to cause a fair amount of confusion among many people.

Behavioral finance holds that investors may not always act rationally, and investment decisions may be driven more by human emotions than classical economics predicts. This insight leads many people to jump to the conclusion that because men are irrational and markets are made of men (and women),⁶ markets are also irrational. In turn, many individuals believe that the behaviors of individual market participants (micro behavioral finance) can be scaled up to represent the behavior of the collective (macro behavioral finance).

Individual Behavior Cannot Explain Crowd Behavior

This belief is an incorrect conclusion, however. Just as we cannot predict the direction of the economy (macroeconomics) by predicting only the behavior of individuals (microeconomics), even though the two disciplines are interrelated, we cannot predict the movements of the stock market by predicting the behavior of individual investors. The primary reason why individual behavior does not aggregate to explain group behavior is that the stock market is a complex adaptive system. The most important characteristic of complex adaptive systems is that they exhibit emergent behavior, meaning that when individual actions are aggregated, patterns emerge that cannot be predicted from analyzing individual behavior. Simply put, the whole is greater than the sum of the parts. To reiterate, we cannot predict how the collective will behave by analyzing only individual behavior.

Rather than focusing on the micro and how individuals behave, Robert Shiller, who shared the Nobel Prize with Eugene Fama in 2013 for his work on behavioral finance, focused on macro or collective behavior. In his 1981 paper “Do Stock Prices Move Too Much to Be Justified by Subsequent Changes in Dividends?” Shiller compared the volatility in stock prices to the variations in the underlying company’s dividends and found no evidence that investors acted in accordance with the efficient market hypothesis. In other words, Shiller found that stock market swings were significantly greater than could be explained by any rational economic behavior, and he uncovered a consistent pattern of excess volatility. It would logically follow that if investors’ behavior is not rational, it must be driven by feelings and emotions. Or, in other words, behavioral finance.

Shiller believes there are too many contradictions to the efficient market hypothesis for it to be considered an accurate theory and “that bubbles or other such contradictions to efficient markets can be understood only with reference to other social sciences such as psychology.”⁷ He defined a bubble as:

A situation in which news of price increases spurs investor enthusiasm which spreads by psychological contagion from person to person, in the process amplifying stories that might justify the price increase and bringing in a larger and larger class of investors, who, despite doubts about the real value of the investment, are drawn to it partly through envy of others’ successes and partly through a gambler’s excitement.⁸

Shiller believes that market movements are influenced substantially by social psychology. Instead of a crowd of rational decision makers, he sees the crowd as “investors who do not pay much attention to fundamental indicators of value”⁹ and who are emotionally driven by animal spirits.

It is interesting that Shiller won the Nobel Prize for his work on behavioral finance yet only mentions the work of Kahneman and Tversky twice, and then only briefly, in his 33-page acceptance lecture. Shiller’s apparent disregard for micro behavioral finance is consistent with the statement that one cannot predict how the collective will behave by analyzing the individual behaviors within the collective.

In fact, individual behavior does not matter unless that behavior produces a systematic error in the collective. And, as we discuss at length in the previous chapter, the only way errors are created in the collective is by a reduction in the crowd’s diversity or a breakdown in its independence. Individual errors offset each other if the models individuals use are diverse and will not affect the crowd error if the individuals act independently. Therefore, although individuals can, and will, make errors and may appear to act irrationally, the individual errors will cancel out each other and have no impact on the crowd’s error as long as the individual errors are not systematically correlated.

Crowd Behavior When Diversity Is Lacking

We define lack of diversity within the collective as when investors think the same way because they use the same, or similar, models to arrive at their estimates of intrinsic value. With a lack of diversity, the crowd’s view will reflect nothing more than the view of a select few or a single individual, rather than different views from multiple individuals. Under these circumstances, the crowd produces an answer that often is only a small deviation from the average individual estimate, as we show in the wisdom of the crowd examples in the previous chapter.

On the other hand, a breakdown of independence occurs when a diverse group of investors is influenced by the same external stimuli and sets aside its prior estimate or private information when expressing its views. The individuals may be diverse because they use different models to arrive at their estimate of intrinsic value, but they lose their independence when they imitate or adopt the views of other investors. In other words, diversity is a function of the variety of models used, while independence is compromised if the model outputs are systematically influenced by external stimuli. Building upon Professor Roger Murray’s analogy from Chapter 4, we show the forces introducing error as magnets pulling the consensus away from the correct answer in Figure 7.3.

We can demonstrate how diversity decreases and why it results in a mispriced stock using a simple example from Chapter 3. We show in that discussion that cash flow can be generated from either operating an asset or selling an asset. Liquidation value and Private Market Value approaches are used to determine the intrinsic value when selling an asset. Determining the intrinsic value when operating an asset is comprised of three layers: value of invested capital, value of competitive advantage (no growth), and value of incremental growth, as shown in Figure 7.4.

Each valuation method (or layer) produces a different range or distribution of intrinsic value estimates, as shown in Figure 7.5. We will let each of the five different valuation types represent a different “model” for the discussion and assume 20% of the crowd uses each “model” exclusively to calculate their intrinsic value estimate.¹⁰

We derive an estimated liquidation value for Sevcon within a range of $0.50 to $3.00 per share in Chapter 3, with the most likely liquidation value being $1.50, as we show in Figure 7.6. As a reference, the stock was trading at $7.60 at the time of this analysis.

Let’s assume that Sevcon announces to the market that it lost an important customer. If, based on this news, Mr. Market becomes fearful, anxious, and depressed, he would likely assign a low value to the company, perhaps nothing more than its liquidation value. If the 80% of the market participants using one of the other models to value the company begin to think that the lost customer could be the beginning of a trend and all shift to valuing Sevcon using the same liquidation model, then a systematic bias will have emerged and the stock would fall from its price of $7.60 to its estimated liquidation value of $1.50. The crowd will have lost its diversity as investors are now all using the same model and calculating Sevcon’s intrinsic value the same way.

Assume that you had followed the company for a number of years. You thought the stock was fairly valued at $7.60 based on your estimate of Sevcon’s Private Market Value, which is the model you use in your analysis. You are also aware that Sevcon lost a key customer and incorporate the new information into your valuation model. Your revised estimate of intrinsic value is now $5.45 per share. If you have confidence that your analysis is correct, then Mr. Market’s reaction creates a mispricing and an opportunity for you, as shown in Figure 7.7. Although your estimate of the company’s intrinsic value dropped from $7.60 per share to $5.45 per share, the market price has declined from $7.60 to its current price of $1.50. Given your estimated value of $5.45, purchasing the stock at $1.50 will seem like a bargain.

However, it is important to note that although the crowd has lost its diversity that alone does not mean that a mispricing exists. The paragraph above contains an important qualifier, “If you have confidence that your analysis is correct.” The implication is that, in your opinion, the consensus is wrong. To you there appears to be a genuine mispricing and the stock seems “like a bargain.” The crowd’s bias may have created a mispricing, but then again, that is not a foregone conclusion just because of the crowd’s lack of diversity.

The extreme price move is also illustrated in Figure 7.8.

Although the Sevcon example is overly simplistic, it reflects the fact that the market is comprised of different constituents. In the real world, there are investors who are long-term oriented and others who are focused on the short term. There are investors who focus on technical analysis, quantitative investors, or fundamental analysis. The investors in each of these investment disciplines often think alike and use similar models to generate their estimates of intrinsic value. For example, fundamental investors value companies based on metrics such as price to book or price to cash flow, while investors who practice technical analysis focus on trading metrics such as comparing the stock’s 200-day moving average with its 50-day moving average. Growth investors focus on sales or earnings growth and use metrics such as price to sales or earnings momentum. When the shareholder base is diverse and includes investors following different disciplines, most often the collective is wise and the stock will be efficiently priced. If, on the other hand, the shareholder base lacks diversity and is comprised mostly of investors from a single group, the collective is less wise and the stock less efficiently priced. While this statement is an oversimplification, we can use it to make an important point about investor diversity.

There have been many situations in the real world where crowding or herding among investors exists in an individual stock or market sector, resulting in a lack of diversity among the market participants in that particular area of the market. Because most of the market participants in these situations are using similar models in their analysis, a small, seemingly innocuous event can trigger a stampede, causing Mr. Market to become manic. We want to emphasize that an “appropriate” reaction to an event by an otherwise diversified shareholder base can easily turn into an overreaction to the same event when the shareholder base lacks diversity.

One such incident was the Quant Crisis of 2007. Quantitative investors use computer models to identify patterns in securities prices and then buy or sell thousands of different securities to capture small mispricings. It has been an enormously successful strategy over the years and has made billionaires of investors like James Simons of Renaissance Technologies, Ken Griffin of Citadel, and Cliff Asness of AQR Capital. However, sometimes a successful strategy breaks down and causes pandemonium.

Several large hedge funds and proprietary trading desks on Wall Street began losing billions of dollars over a matter of a few days in early August 2007 and no one knew exactly why. Because the majority of these funds were implementing a similar strategy (using the same models) and employing significant amounts of leverage, many of them received margin calls simultaneously when prices started to decline (the securities they held were the collateral for their loans) and were forced to sell at least some of their holdings. Unfortunately, because many of the portfolios were similarly constructed, the margin calls created a ripple effect, and the selling begat more selling, which resulted in a “death spiral” in many of the widely-owned assets.

In his excellent book The Quants, Scott Patterson describes events during those days in August that seem more fitting for a John Grisham suspense novel than a Wall Street trading desk. The following are a few excerpts:

The market moves PDT and other quant funds started to see early that week defied logic. The fine-tuned models, the bell curves and random walks, the calibrated correlations—all the math and science that had propelled the quants to the pinnacle of Wall Street—couldn’t capture what was happening. It was utter chaos driven by pure human fear, the kind that can’t be captured in a computer model or complex algorithm. This wasn’t supposed to happen! [emphasis original]

The quants did their best to contain the damage, but they were like firefighters trying to douse a raging inferno with gasoline—the more they tried to fight the flames by selling, the worse the selling became. The downward-driving force of the deleveraging market seemed unstoppable.

It made no sense. A true believer in market efficiency who’d studied under Eugene Fama at Chicago, Rothman expected the market to behave according to the strict quantitative patterns he lived to track. But the market was acting in a way that defied any pattern Rothman—or any other quant—had ever seen. Everything was losing money. Every strategy was falling apart. It was unfathomable, if not outright insane.¹¹

Crowd Behavior When Information Cascades: The Breakdown of Independence

While similar to a lack of diversity, a breakdown of independence has subtle but important differences. Although members of the crowd might use different models to arrive at their own independent estimates, thus preserving the crowd’s diversity, a breakdown of independence occurs when some external influence causes them to alter or set aside their original estimate. This phenomenon is known as an information cascade.

This subtlety bears repeating to ensure clarity. When there is a breakdown of independence, individuals fixate on some external factor, set their model aside, and follow the crowd.

In Chapter 6, we defined private information as an individual’s knowledge of facts and experiences that represents their domain-specific knowledge and forms their information processing model, which we show in Figure 6.6 and reproduce in figure 7.9:

As we mention throughout the book, each investor has their own unique private information, which impacts their estimate of intrinsic value. If the investor arrives at their estimate without any bias, then their estimate is said to be independent. Absent a genuine informational advantage, however, the investor’s private information is usually imperfect. Consequentially, the investor might not have a high degree of confidence in the accuracy of their estimate.

The classic example of an information cascade involves choosing where to eat when traveling to a city you don’t know well. Imagine you are planning on spending a few days in London on business. You have never been to London before and would like to find a nice restaurant for one of your dinners. Before leaving on your trip, you research options by reviewing the Michelin guide and reading online Yelp reviews. You arrive in London, drop your bags at your hotel, and head out to dinner. As you approach the restaurant you chose through your research, you see that it is empty. When you look at the restaurant next door, however, it is packed and looks like a nice place to eat. At that instant, you change your plans and select the new restaurant for dinner without any additional research or information. What you have done is set aside the private information you obtained from your earlier research in response to observing the actions of other individuals. An information cascade will unfold if other people decide to eat at the crowded restaurant for the same reasons you did when you made your impromptu decision.

We can illustrate how information cascades occur in the stock market with another simple example. Bob is an investment adviser and manages separate accounts for high-net-worth individuals on a discretionary basis.¹² Bob closely follows Biotech Inc., a company that is developing a drug for neon nose syndrome. The company is currently waiting for the results of the FDA Phase II trials for their drug. After talking with several doctors and patients aware of the trial, Bob believes the drug is effective and will get Phase II approval. He purchased Biotech’s stock in 20 of his client accounts at an average price of $15.37. A few weeks later the stock price has increased to $17.04.

Judd is a well-known and highly regarded biotech investor. He appears regularly on CNBC and has an impressive investment record. Since Judd is a successful investor, many market participants track his fund’s holdings through its 13G filings.¹³ Bob notices in a new amended 13G filing with the SEC that Judd’s firm no longer holds a position in Biotech Inc., which shocks Bob because he knows Judd’s fund previously owned 6.2% of the company’s shares.

Bob’s heart sinks as he infers that if Judd’s fund sold its Biotech Inc. stock, Judd must believe that the company will not get FDA approval for its neon nose drug. If the drug is not approved, Biotech stock could easily fall in half to $8.50, or possibly even lower. Since Bob’s accounts own a lot of the stock, he panics and sells the shares for his 20 clients pushing Biotech Inc. stock down from $17.04 to $16.23 in the process. He also notices that there are postings on Twitter also speculating that if Judd’s fund sold its Biotech Inc. position, it signals that the company is unlikely to get FDA approval. Reading the negative Tweets further reinforces Bob’s decision. By the time the market closes a few hours later, Biotech Inc.’s stock has traded down to $13.78 on seven times the average daily volume.

This series of events is the beginning of an information cascade. Bob does not know what private information Judd has, although he infers Judd’s knowledge from the 13G filing. Bob set aside his private information (his estimate) and imitated (what he thought was) Judd’s decision to sell the shares.

Bob’s clients, who have been watching the trading in Biotech Inc. stock, receive trade confirmations in the mail prompted by Bob’s sales of Biotech Inc. Randi, one of Bob’s clients who works at a rival biotech company and is familiar with Biotech Inc.’s neon nose drug, notices Bob sold the stock in her account. Randi hired Bob to manage her money because she thinks he is smart and respects his judgment. She, too, has been following Biotech Inc.’s progress for a while and thought the drug would get FDA approval. She agreed with Bob’s original analysis of the company’s potential, but now has doubts.

Randi also invests her money on her own. She notices that Biotech Inc. declined $3.26 on the day to close at $13.78 on significant trading volume. The next morning, she decide to sell short some Biotech stock in her own account the thinking the price will continue to fall further. The information cascade continues. Randi assumes that Bob has good private information as expressed by the stock sales in her account. This was “confirmed” by the significant decline in the stock price. Randi thinks that “smart” investors must know something that she does not know about the company’s Phase II trial, so she sets aside her private information and imitates Bob and the rest of the traders in the market.¹⁴

The only way information can be expressed, aggregated, and incorporated into the stock price is through trading. Therefore, although investors have no way of knowing the private information prompting the stock trades, they see the actions of other investors (as evidenced by the decline in Biotech Inc.’s stock price) and infer that the sellers have better private information. It is important to note that the information conveyed by the stock decline might not have been accurate. As every seasoned investor knows, just because someone sells a stock does not mean they have better information. Paul S. remembers one of Chuck Royce’s comments after witnessing a large move in a particular stock, “Someone either knows something or thinks they know something.”

The price decline in Biotech Inc. stock caused individuals to doubt their independent analysis. Bob and Randi both set aside their private information and imitated other investors. And, as a result, there was a breakdown in the crowd’s independence.

We also saw that there was a feedback loop. As Biotech Inc.’s stock declined in value, the trades reinforced everyone’s conviction that someone must know something to justify the lower stock price. Most investors thought that if the stock was falling ahead of the FDA announcement, other investors must have information that indicated that the company would not get approval. However, as it turned out, the market signal was false—it was noise. Investor selling pushed the price lower, but the lower price conveyed false information to the market participants. The cycle became a positive feedback loop based on noise, not better information. No one really knew anything, although everyone thought that someone must know something.

If Bob and Randi remained independent, they would have ignored the noise and stuck with their original decision, which was based solely on their private information. However, because they lacked conviction in their original assessment, they set aside their estimate (private information) and adopted the actions of other investors they thought had better information, which created the information cascade.

Biotech Inc. announced a week later that it had passed Phase II FDA trials. The stock closed that day at $24.55, up sharply from the prior day’s closing price, and significantly higher than the prices where Bob and Randi sold their shares. Judd happened to be on CNBC that day and the reporter asked why he had sold Biotech Inc. stock prior to the company’s positive announcement. With a look of surprise, Judd exclaimed, “We didn’t sell.” The reporter referenced the amended 13G filing. Judd responded, “Oh. I see the confusion. No, we didn’t sell. My partner and I decided to part ways and we each took half of the assets into our new funds. We did this as an in-kind distribution, so there was no trading involved. Each of our funds now own 3.1% of Biotech Inc., so are we no longer required to file with the SEC since each fund owns less than 5%. We did have to update our filing as the old fund was dissolved and as a result, held no stock. I guess the view from the outside was that we sold our entire position.”

For a brief instant, a look of deep thought was apparent on Judd’s face. Then he said to the announcer, “You know, that explains a lot. I was wondering why Biotech stock fell so much last week. I didn’t even think about our 13G filing as a possible explanation. People must have thought we sold. That’s funny. When I saw that the stock was down a lot, my first reaction was fear. We had confidence in our research and thought that the move in the stock was just noise, but it is hard to stick to your guns when the market appears to be telling you that you are wrong. We used the sell-off as an opportunity to add to our position and wound up making a ton of money.” Judd sat back and laughed, saying, “Wow, if I panicked and sold the stock because of the move, I would have been reacting to the noise my filing produced without even knowing it!”

Intrinsic Value Acts as an Electromagnet

Expanding on Murray’s magnet analogy, we can think of market efficiency as an electromagnet that draws the stock price toward the true economic or intrinsic value of the underlying business, while thinking of behavioral finance as a different magnet, powered by human emotions, that pulls the stock price away from its intrinsic value.²³

All one needs to construct an electromagnet is a nail, a battery, and some wire, as shown in Figure 7.10.

We can demonstrate the magnetic effect market efficiency has on stock prices, and how the efficient market hypothesis and behavioral finance coexist, using a few simple examples. Imagine we place a ferromagnetic emoji ball bearing inside the distribution curve in Figure 7.11 and position an electromagnet at the curve’s apex to represent market efficiency’s pull. The graphic shows that the electromagnet holds the emoji at the top of the curve, where price equals intrinsic value, representing that the market is functioning without error and there is no mispricing.

The Pull of Systematic Error

However, when the wisdom of crowds is not functioning properly because some type of systematic bias has created an error, behavioral finance has entered the picture, pulling prices away from their intrinsic value. When investors become overly optimistic or pessimistic, their independence and diversity declines and the correlation of their estimates increases, which drives the stock to a premium or forces it to a discount to intrinsic value, as shown in Figure 7.12. It helps to visualize biased investor behavior as strong magnets, pulling the ferromagnetic emoji ball down the curve and away from the electromagnet of intrinsic value.

The pricing error can be corrected in two ways. First, unbiased investors could enter the market to exploit the profit opportunity and correct the mispricing, in effect diluting the biased consensus. We can think of these investors entering the market as the equivalent of turning up the power on the electromagnet at the top of the distribution curve (where the price equals intrinsic value). This action overpowers the magnets representing the behavioral bias and pulls the stock back to its intrinsic value, as shown in Figure 7.13. The other way the mispricing could correct is if the bias dissipates or recedes. In the magnet analogy, this action would be equivalent to the behavioral bias magnets losing their strength. Often mispricings are corrected by a combination of the two forces.

Albeit overly simplified, the description illustrates how the efficient market hypothesis and behavioral finance together influence the movement of stock prices. A stock will be efficiently priced unless some type of systematic bias, driven by human emotions, pulls the price away from its true value, as shown in the various charts. Again, even if the consensus is wrong, as we discussed previously, a systematic bias does not automatically mean that there is a pricing error.

The Extreme Pull of Excessive Fear and Greed

Mr. Market’s overreactions will result in crashes and bubbles when taken to an extreme. Shiller describes the snowball effect of a bubble by stressing that it starts with a price increase, which spurs investor enthusiasm, and spreads like a contagion from person to person, becoming amplified in the process and sucking in more and more investors. This description echoes Graham’s observation that, “the investors’ mental attitude not only affects the market price but is strongly affected by it.”²⁴

In a mania or bubble, greed overwhelms fear and investors become overly optimistic. Conversely, in a panic or crash, fear overwhelms greed and investors become overly pessimistic. During these episodes, stock prices move away from their true intrinsic value and valuations expand to large premiums or contract to significant discounts, as the stock prices become detached from the business’s underlying fundamentals. Market efficiency is cast aside as the crowd’s fear and greed take over. Effectively, the magnetic pull of these emotions temporarily overwhelms the power of the efficient market electromagnet. At that moment, the biased magnets are stronger than the unbiased electromagnet and pull the emoji far away from the company’s true intrinsic value, down into the tails of the curve, as shown in Figure 7.14.

Overwhelmed by fear or greed in these situations, investors begin to think alike, group intelligence becomes groupthink, information cascades and the wisdom of crowds is lost, as the madness of crowds takes over.

In some extreme cases, such as the Internet bubble in 2000 and the 2008 financial crisis, investors get so carried away that their collective emotions overpower the unbiased electromagnet, preventing the emoji from returning to its intrinsic value. The unemotional traders simply do not have enough capital or fortitude to offset the impulsiveness and strength of the emotional crowd, as shown in Figure 7.15.

Bubbles Eventually Burst and Crashes Eventually End

Eventually, some event occurs, the bubble breaks, or the panic ends, and investors are snapped out of their manic state. The force of the biased magnets fades, releasing their grip. Investor independence and the crowd’s diversity returns, and stock prices begin to drift (or snap) back toward intrinsic value, as shown in Figure 7.16.

Despite the Nobel Committee’s decision to recognize both theories in the same year, blending the theories of behavioral finance with the efficient market hypothesis may still appear to be heresy to many academics and most practitioners. However, the merged theory provides us with a more effective mental model to proceed with in the discussion. It is important to note that the overwhelming evidence supports the claim that the efficient market hypothesis is the prevailing force most of the time, although in “normal” market conditions, behavioral finance will skew pricings with some frequency. On the rare occasion, during extreme times of manias and panics, when investor emotions take over and create systematic errors, behavioral finance gets the upper hand.

Between “perfect” market efficiency at one extreme and manias and panics on the other, there is a blending of the two forces. The blue area in the middle of Figure 7.17 represents the efficient market hypothesis at its strongest, while the red area on either ends of the curve shows where behavioral factors have the stronger pull. As we discuss earlier, stock prices rarely deviate far from their intrinsic value; however, on some occasions, investor behavior creates a systematic bias that takes control, causing the discounts or premiums to intrinsic value to go to an extreme.