1. Chapter 22 Behavioral Finance: Implications for Financial Management
Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved.
McGraw-Hill/Irwin

2. Key Concepts and Skills
Identify behavioral biases and understand how they impact decision-making
Understand how framing effects can result in inconsistent and/or incorrect decisions
Understand how the use of heuristics can lead to suboptimal financial decisions
Recognize the shortcomings and limitations to market efficiency from the behavioral finance viewpoint
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3. Chapter Outline Introduction to Behavioral Finance Biases
Framing Effects
Heuristics
Behavioral Finance and Market Efficiency
Market Efficiency and the Performance of Professional Money Managers
Lecture Tip: In the chapter opener, the issue of market bubbles is presented, providing the specific example of the internet bubble in the late 1990s. Other similar situations exist; for example, consider the run in commodities (oil, gold, etc.) in 2007 and early However, it is good to point out to students that the same condition may occur on the downside as well. So, just as investors may “herd” into an asset, they may also herd out of an asset. Being able to identify these biased actions may provide an “unbiased” investor (if one exists) a competitive advantage.
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4. Poor Outcomes
A suboptimal result in an investment decision can stem from one of two issues:
You made a good decision, but an unlikely negative event occurred
You simply made a bad decision (i.e., cognitive error)
Lecture Tip: Sometimes events occur that are beyond our control, but, nonetheless, we have previously discussed ways to review the potential disaster of such occurrences. Thus, it may be helpful to remind students about activities such as sensitivity and scenario analysis, as well as simulation techniques. This chapter, hopefully, will help alleviate (or at lest reduce) the second cause of poor decisions.
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5. Overconfidence
Example: 80 percent of drivers consider themselves to be above average
Business decisions require judgment of an unknown future
Overconfidence results in assuming forecasts are more precise than they actually are
Lecture Tip: Overconfidence is tied to self-attribution bias, which leads people to attribute success to their own skill, while poor results are attributed to bad luck.
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6. Overoptimism
Example: overstating projected cash flows from a project, resulting in a high NPV
Overestimate the likelihood of a good outcome
Not the same as overconfidence, as someone could be overconfident of a negative outcome (i.e., “overpessimistic”)
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7. Confirmation Bias
More weight is given to information that agrees with a preexisting opinion
Contradictory information is deemed less reliable
Lecture Tip: Considering placing students in the role of a military general. Ask them what would happen if they exhibited confirmation bias when making a strategic military decision. Although the loss of life is more drastic than a poor business decision, they are both potentially detrimental.
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8. Framing Effects
How a question is framed may impact the answer given or choice selected
Loss aversion (or break-even effect)
Retain losing investments too long (violation of the sunk cost principle)
House money
More likely to risk money that has been “won” than that which has been “earned” (even though both represent wealth)
Lecture Tip: Historically, employees who were offered a company-sponsored retirement plan (such as a 401(k)) were required to opt into the plan. Thus, the default option was no participation. Under this approach, less than 1/3 of employees participated. The government changed the allowable approach earlier this decade, allowing companies to default employees into the plan, while offering the choice to opt out. While there was no change to the choice the employees faced, the way in which the situation was framed significantly changed the participation rate, with over 2/3 of employees participating. This is also an example of what is referred to as the “endowment” effect.
Lecture Tip: Terrance Odean, in “Are Investors Reluctant to Realize Their Losses” (Journal of Finance, 1998), finds that investors sell winning stocks to early and retain poorly performing stocks too long, resulting in a lower portfolio return (i.e., a type of disposition effect).
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9. Heuristics Rules of thumb, mental shortcuts The “Affect” Heuristic
Reliance on instinct or emotions
Representativeness Heuristic
Reliance on stereotypes or limited samples to form opinions of an entire group
Representativeness and Randomness
Perceiving patterns where none exist
Lecture Tip: Consider a firm that makes a successful investment in the country of Russia, then assumes that all future investments in the country will be successful. Ask students what could be different about subsequent investments that would render this assertion incorrect.
Lecture Tip: Remind students about the implication of market efficiency for technical analysis, then discuss how representativeness may increase the attractiveness of such an approach.
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10. The Gambler’s Fallacy
Heuristic that assumes a departure from the average will be corrected in the short-term
Related biases
Law of small numbers
Recency bias
Anchoring and adjustment
Aversion to ambiguity
False consensus
Availability bias
Suppose a coin is flipped five times in a row, each resulting in a head. Someone experiencing gambler’s fallacy will be prone to believe it is more likely that the next flip will be a tail, even though it is an independent event with a 50/50 chance.
Lecture Tip: In 2008, there was a great amount of flooding across the Midwest, with many areas experiencing “500-year” floods. Ask what an individual exhibiting recency bias would be prone to do regarding flood insurance (take out added coverage). Contrast this to someone who is displaying signs of gambler’s fallacy (reduce coverage).
Law of small numbers – small sample always represents long run distribution
Recency bias – recent events are given more importance
Anchoring and adjustment – unable to appropriately account for new information
Aversion to ambiguity – shy away from the unknown
False consensus – believe other people have the same opinions as your own
Availability bias – put too much weight on information that is easily available
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11. Behavioral Finance and Market Efficiency
Can markets be efficient if many traders exhibit economically irrational (biased) behavior?
The efficient markets hypothesis does not require every investor to be rational
However, even rational investors may face constraints on arbitraging irrational behavior
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12. Limits to Arbitrage Firm-specific risk Noise trader risk
Reluctant to take large positions in a single security due to the possibility of an unsystematic event
Noise trader risk
Keynes: “Markets can remain irrational longer than you can remain insolvent.”
Implementation costs
Transaction costs may outweigh potential arbitrage profit
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13. Bubbles and Crashes
Bubble – market prices exceed the level that normal, rational analysis would suggest
Crash – significant, sudden drop in market-wide values; generally associated with the end of a bubble
Some examples of crashes:
October 29, 1929
October 19, 1987
Asian crash
“Dot-com” bubble and crash
Lecture Tip: Although the 1987 crash still represents the largest single day percentage drop, the largest point drop (on the Dow) occurred on September 29, 2008 (778 points) following the House of Representatives refusal to pass the “bailout” package associated with the mortgage and credit crisis.
Lecture Tip: The first bubble was recorded in 1637, as the Dutch experienced “Tulip Mania,” pushing the price of the rarest tulip bulb to 20 times that of a skilled craftsman’s yearly wage. The market suddenly collapsed after approximately six months.
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14. Money Manager Performance
If markets are inefficient as a result of behavioral factors, then investment managers should be able to generate excess return
However, historical results suggest that passive index funds, on average, outperform actively managed funds
Even if markets are not perfectly efficient, there does appear to be a relatively high degree of efficiency
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15. Quick Quiz
Describe the similarities and differences between overconfidence and overoptimism.
How might the framing effect impact a company conducting market research.
What are heuristics, and why might they lead to incorrect decisions?
Why does the existence of cognitive error not necessarily make the market inefficient?
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16. Ethics Issues
Consider a political election with two competing candidates, one who is pro-life and the other who is pro-choice.
How might a pollster representing one side frame a survey question differently than someone from the competing political camp?
What does this say for the potential accuracy of reported survey results?
How might this situation apply to a company?
Notice that both sides use the prefix pro to frame the position in a positive light.
This issue has a similar application for a company that is researching consumer opinions regarding its product. I.e., make sure that questions are not worded (framed) so as to elicit a biased response.
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17. Comprehensive Problem
Warren Buffett, CEO of Berkshire Hathaway, is often viewed as one of the greatest investors of all time. His strategy is to take large positions in companies that he views as having a good, understandable product but whose value has been unfairly lowered by the market.
What behavioral biases is Buffett attempting to identify?
If he successfully identifies these, will he be able to outperform the market?
How might we analyze whether Buffett has, in fact, outperformed the market?
Buffett looks for downside overconfidence (or negative bubble). If he is correct, he should outperform.
A comparison of BRK-A to the S&P500 index over the last five years provides the following percentage returns:
BRK-A SPY
% 28.2%
% 10.7%
% 4.8%
% 15.2%
% 5.1%
Assuming equivalent risk to the market, in three years BRK underperformed and in two years BRK outperformed. The average return is 14.8% and 12.8% respectively; however, given the small data set we can not explicitly state this is a significant difference, particularly since we have not explicitly controlled for risk differentials.
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18. End of Chapter
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