Reading: Lesson 7 - Behavioral Finance

6.7.A - Behavioral Finance

1. Market Bubbles and Behavioral Finance

1. A large body of evidence in the field of psychology shows that people often behave irrationally, but in predictable ways. The field of behavioral finance focuses on irrational, but predictable, financial decisions. The following sections examine applications of behavioral finance to market bubbles and to other financial decisions.
   
2. Psychologists have learned that many people focus too closely on recent events when predicting future events, a phenomenon called anchoring bias. Therefore, when the market is performing better than average, people tend to think it will continue to perform better than average. When anchoring bias is coupled with overconfidence, investors can become convinced that their prediction of an increasing market is correct, thus creating even more demand for stocks. This demand drives stock prices up, which serves to reinforce the overconfidence and move the anchor even higher.
   
3. There is another way that an increasing market can reinforce itself. Studies have shown that gamblers who are ahead tend to take on more risks (i.e., they are playing with the house’s money), whereas those who are behind tend to become more conservative. If this is true for investors, we can get a feedback loop: when the market goes up, investors have gains, which can make them less risk averse, which increases their demand for stock, which leads to higher prices, which starts the cycle again.
   
4. Herding behavior occurs when groups of investors emulate other successful investors and chase asset classes which are doing well. For example, high returns in mortgage-backed securities during 2004 and 2005 enticed other investors to move into that asset class. Herding behavior can create excess demand for asset classes that have done well, causing price increases which induce additional herding behavior. Thus, herding behavior can inflate rising markets.
   
5. Sometimes herding behavior occurs when a group of investors assumes that other investors are better informed—the herd chases the “smart” money. But in other cases herding can occur even when those in the herd suspect that prices are overinflated. For example, consider the situation of a portfolio manager who believes that bank stocks are overvalued even though many other portfolios are heavily invested in such stocks. If the manager moves out of bank stocks and they subsequently fall in price, then the manager will be rewarded for her judgment. But if the stocks continue to do well, the manager may well lose her job for missing out on the gains. If instead the manager follows the herd and invests in bank stocks, then the manager will do no better or worse than her peers. Thus, if the penalty for being wrong is bigger than the reward for being correct, it is rational for portfolio managers to herd even if they suspect the herd is wrong.
   
6. Researchers have shown that the combination of overconfidence and biased self-attribution can lead to overly volatile stock markets, short-term momentum, and long-term reversals. We suspect that overconfidence, anchoring bias, and herding can contribute to market bubble.
   

2. Other Applications of Behavioral Finance

1. Psychologists Daniel Kahneman and Amos Tversky show that individuals view potential losses and potential gains very differently. If you ask an average person whether he or she would rather have $500 with certainty or flip a fair coin and receive $1,000 if it comes up heads and nothing if it comes up tails, most would prefer the certain $500 gain, which suggests an aversion to risk—a sure $500 gain is better than a risky expected $500 gain. However, if you ask the same person whether he or she would rather pay $500 with certainty or flip a coin and pay $1,000 if it’s heads and nothing if it’s tails, most would indicate that they prefer to flip the coin, which suggests a preference for risk—a risky expected $500 loss is better than a sure $500 loss. In other words, losses are so painful that people will make irrational choices to avoid sure losses. This phenomenon is called “loss aversion.”
   
2. One way that people avoid a loss is by not admitting that they have actually had a loss. For example, in many people’s mental bookkeeping, a loss isn’t really a loss until the losing investment is actually sold. Therefore, they tend to hold risky losers instead of accepting a certain loss, which is a display of loss aversion. Of course, this leads investors to sell losers much less frequently than winners even though this is suboptimal for tax purposes.
   
3. Many corporate projects and mergers fail to live up to their expectations. In fact, most mergers end up destroying value in the acquiring company. Because this is well known, why haven’t companies responded by being more selective in their investments? There are many possible reasons, but research by Ulrike Malmendier and Geoffrey Tate suggests that overconfidence leads managers to overestimate their abilities and the quality of their projects.36 In other words, managers might know that the average decision to merge destroys value, but they are certain that their decision is above average.
   

3. The CAPM and Market Efficiency: Implications for Corporate Managers and Investors

1. A company is like a portfolio of projects: factories, retail outlets, R&D ventures, new product lines, and the like. Each project contributes to the size, timing, and risk of the company’s cash flows, which directly affect the company’s intrinsic value. This means that the relevant risk and expected return of any project must be measured in terms of its effect on the stock’s risk and return. Therefore, all managers must understand how stockholders view risk and required return in order to evaluate potential projects.
   
2. Stockholders should not expect to be compensated for the risk they can eliminate through diversification, but only for the remaining market risk. The CAPM provides an important tool for measuring the remaining market risk and goes on to show how a stock’s required return is related to the stock’s market risk. It is for this reason that the CAPM is widely used to estimate the required return on a company’s stock and, hence, the required returns that projects must generate to provide the stock’s required return.
   
3. Is the CAPM perfect? No. First, we cannot observe beta but must instead estimate beta. Estimates of beta are not precise. Second, we saw that small stocks and stocks with high B/M ratios have returns higher than the CAPM predicts. This could mean that the CAPM is the wrong model, but there is another possible explanation. If the composition of a company’s assets were changing over time with respect to the mix of physical assets and growth opportunities (involving, e.g., R&D or patents), then this would be enough to make it appear as though there were size and B/M effects. In other words, even if the returns on the individual assets conform to the CAPM, changes in the mix of assets would cause the firm’s beta to change over time in such a way that the firm would appear to have size and book-to-market effects.
   
4. Regarding market efficiency, our understanding of the empirical evidence suggests it is very difficult, if not impossible, to beat the market by earning a return that is higher than justified by the investment’s risk. This suggests that markets are reasonably efficient for most assets for most of the time. However, we believe that market bubbles do occur and that it is very difficult to implement a low-risk strategy for profiting when they burst.