Richard Thaler is the world’s foremost theorist on behavioural finance, a field that was developed by Daniel Kahneman (Nobel Prize-winner in 2002) and Amos Tversky from the 1970s. Most of them, and another top researcher, Cass Sunstein, have worked together at some stage or another to generate fabulous insights into how irrational out behaviour is. Behavioural economics, and its subset behavioural finance, of course, is designed to show that human beings are not rational while making decisions.
Thaler’s previous book was
Nudge, with Cass Sunstein, which discussed how public and private organisations can help people make better choices in their daily lives.
His recent book Misbehaving: The Making of Behavioural Economics is a fascinating history of how this new discipline developed into such a major force over the past four decades and has come to dominate the mainstream economic thinking.
After all, Thaler is now the president of the American Economic Association, a position held by luminaries from conventional economic stream, such as Milton Friedman, JK Galbraith and Amartya Sen.
The book starts with a fascinating example of how irrational we are. For a mid-term exam, Thaler had set up a test that was designed to distinguish between three broad groups of students: those who really mastered the concepts taught; those who grasped the basic concepts; and those who just did not understand. For this, the exam had to have some questions that only the top students would get right, which meant that the test was tougher than usual. Thaler writes that “the exam succeeded in my goal—there was a wide dispersion of scores—but when the students got their results, they was an uproar among them. Their principal complaint was that the average score was only 72 points out of a possible 100.” As happens, this reaction was irrational. The average numerical score had absolutely no effect on the grades since the average grade was a B or B+, and only a few got grades below C.
Thaler writes that “I had anticipated the possibility that a low average numerical score might cause some confusion on this front, so I had reported how the numerical scores would be translated into actual grades in the class. Anything over 80 would get an A or A-, scores above 65 would get some kind of B, and only scores below 50 were in danger of getting a grade below C. The resulting distribution of grades was not different from normal, but this announcement had no apparent effect on the students’ mood. They still hated my exam, and they were none too happy with me either. As a young professor worried about keeping my job, I was determined to do something about his, but I did not want to make my exams any easier. What to do?”
For the next exam, Thaler, the behavioural expert, made the total number of points available 137 instead of 100! This exam was harder than the first, with students getting only 70% of the answers right (as opposed to 72% in the first); but the average numerical score was a cheery 96 points.
He says, “The students were delighted! No one’s actual grade was affected by this change but everyone was happy. From that point on, whenever I was teaching this course, I always gave exams a point total of 137, a number I chose for two reasons. First, it produced an average score well into the 90s, with some students even getting scores above 100, generating a reaction approaching ecstasy. Second, because dividing one’s score by 137 was not easy to do in one’s head, most students did not seem to bother to convert their scores into percentages. Lest you think I was somehow deceiving the students, in subsequent years, I included this statement, printed in bold type, in my course of syllabus: ‘Exams will have a total of 137 points rather than the usual 100. This scoring system has no effect on the grade you get in the course, but it seems to make you happier.’ And indeed, after I made that change, I never got a complaint that my exams were too hard.” This book is full of stories like these.
Behaviourial Finance Meets Quants
One of the more interesting applications of behaviourial finance is, surprisingly, in clarifying a knotty investment conundrum which is the domain of quants. For instance, academics have intensely debated an anomaly called the ‘equity premium puzzle’, first announced by Raj Mehra and Edward Prescott in a 1985 paper.
Prescott is a staunch member of the ‘rational expectations’ school, whose work in ‘real business cycles’, later won him a Nobel Prize. As Thaler writes, “unlike me, Prescott did not have declaring anomalies as part of his agenda. I suspect he found this one to be a bit embarrassing given his world view, but he and Mehra knew they were on to something interesting.” This was similar to the momentum anomaly, that has embarrassed the ‘Efficient Market Hypothesis’ school which I have discussed on page 40.
Now ‘equity risk premium’ is the difference in returns between equities (stocks) and some risk-free assets such as government bonds. The premise is that investors demand a premium for buying equities over risk-free securities because equities are risky. The question is: How big is this premium? We can look at history but the answer depends on the period used and various definitions. Mehra and Prescott studied the period 1889-1978 and concluded that the equity premium was about 6% per year.
“In many economics articles, the analysis would stop at that point,” writes Thaler. “The theory predicts that one asset will earn higher returns than another because it is riskier, the authors find evidence confirming this prediction, and the result is scored as another win for economic theory. What makes the analysis by Mehra and Prescott special is that they went beyond asking whether economic theory can explain the existence of an equity premium, and asked if economic theory can explain how large the premium actually is.”
Mehra and Prescott went ahead and developed a model to predict equity risk premium. The largest value they could predict from their model was 0.35%, far lower than the actual historical figure of 6%! This made no sense. Stocks are highly risky. Why would investors take the risk of buying stocks when all they get is 0.35% over risk-free returns?
The model and its conclusions were controversial. Mehra and Prescott took six years to get the paper published. Once it was published, many economists came forward to offer their explanations. Thaler and his associate Shlomo Benartzi offered theirs: people behave differently over short term and long term. While Mehta and Prescott arrived at a 6% figure reward figure for the risk one took to buy stocks, it is worth asking why don’t more people go for that reward? Why do investors hold more bonds than stocks? The answer: they were taking too short-term a view of their investments. Humans are not rational.
To test this, Thaler and Shlomo ran an experiment using employees at the University of Southern California which had a defined contribution retirement plan. Under the plan, employees have to decide how to invest their retirement funds. They told each employee to imagine that there were only two investment options in this retirement plan, a riskier one with higher expected returns and a safer one with lower expected returns.
They were shown charts showing the distribution of 68-year returns. They were not told of the asset classes, to avoid any preconceptions they might have about stocks and bonds. The trick was in what the charts revealed. In one version, the subjects were shown the distribution of annual rates of return; in another, they were shown the distribution of simulated average annual rates of return for a 30-year horizon.
“The first version captures the returns people see if they look at their retirement statements once a year, while the other represents the experience they might expect from a thirty-year invest-and-forget-it strategy. Employees who were shown the annual rates of return chose to put 40% of their hypothetical portfolio in stocks, while those who looked at the long-term averages elected to put 90% of their money into stocks,” writes Thaler.
The more often people look at their portfolios, the less willing they will be to take on risk, because if you look more often, you will see more losses. Thaler later explored this idea in a paper with Kahneman and Tversky, the only paper that these three stalwarts published together and that too after Taversky passed away. This is a book full of interesting stories from the world of behavioural finance that will keep you engrossed if you are interested in this discipline, as every investor should be. The casual reader, too, will find these fascinating.
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