Category Archives: Behavorial Economics

Why Smart People Are Stupid

We all know humans suffer from cognitive biases that cloud our judgment and how we perceive the world. We also know that smarter people tend to have meta-cognition, or the ability to think about what we are thinking. Logically, we would expect that better thinkers would be more aware of when they are subject to these cognitive biases and be less susceptible to them.

One researcher decided to test this hypothesis:

The results were quite disturbing. For one thing, self-awareness was not particularly useful: as the scientists note, “people who were aware of their own biases were not better able to overcome them.” This finding wouldn’t surprise Kahneman, who admits in “Thinking, Fast and Slow” that his decades of groundbreaking research have failed to significantly improve his own mental performance. “My intuitive thinking is just as prone to overconfidence, extreme predictions, and the planning fallacy”—a tendency to underestimate how long it will take to complete a task—“as it was before I made a study of these issues,” he writes.

Perhaps our most dangerous bias is that we naturally assume that everyone else is more susceptible to thinking errors, a tendency known as the “bias blind spot.” This “meta-bias” is rooted in our ability to spot systematic mistakes in the decisions of others—we excel at noticing the flaws of friends—and inability to spot those same mistakes in ourselves. Although the bias blind spot itself isn’t a new concept, West’s latest paper demonstrates that it applies to every single bias under consideration, from anchoring to so-called “framing effects.” In each instance, we readily forgive our own minds but look harshly upon the minds of other people.

And here’s the upsetting punch line: intelligence seems to make things worse. The scientists gave the students four measures of “cognitive sophistication.” As they report in the paper, all four of the measures showed positive correlations, “indicating that more cognitively sophisticated participants showed larger bias blind spots.” This trend held for many of the specific biases, indicating that smarter people (at least as measured by S.A.T. scores) and those more likely to engage in deliberation were slightly more vulnerable to common mental mistakes. Education also isn’t a savior; as Kahneman and Shane Frederick first noted many years ago, more than fifty per cent of students at Harvard, Princeton, and M.I.T. gave the incorrect answer to the bat-and-ball question.

Continue reading here.

 

 

Luck vs. Skill In Financial Markets

Nassim Taleb on mistaking luck as skill in financial markets:

There is one world in which I believe the habit of mistaking luck for skill is most prevalent – and most conspicuous – and that is the world of markets. By luck or misfortune, that is the world in which I have operated most of my adult life. It is what I know best. In addition, economic life presents the best (and most entertaining) laboratory for the understanding of these differences. For it is the area of human undertaking where the confusion is greatest and its effects the most pernicious. For instance, we often have the mistaken impression that a strategy is an excellent strategy, or an entrepreneur a person endowed with “vision,” or a trader a talented trader, only to realize that 99.9% of their past performance is attributable to chance, and chance alone. Ask a profitable investor to explain the reasons for his success; he will offer some deep and convincing interpretation of the results. Frequently, these delusions are intentional and deserve to bear the name “charlatanism.”

Contrast participants in financial markets to another group of people with similar characteristics: professional poker players. Poker games, like financial markets, are zero-sum in that one participant cannot do better without making another player worse off. Poker players and investors must make decisions under situations of incomplete and imperfect information. And most importantly, there is a tremendous amount of short-term luck involved in poker — often times one can make the right decision but end up losing money.

Consider the best starting hand in Texas Hold’Em versus one of the statistically worst starting hands: pocket aces versus seven two off-suit. Even in this most favorable situation possible, the pocket aces has only a 87 percent chance of winning the hand. And these are one of the easiest hands to play correctly! Most situations involve considerable uncertainty: top pair versus a flush draw, for example. The role of luck in poker is so large that paradoxically, both players can be making the right move (in that they have positive expected value) by staying in the hand. Thus, the consensus among poker players is that anyone reaching the final table in a tournament has had a great deal of short-term luck. Often times, due to the increasing blind structure, the remaining players in a tournament must survive multiple all-in, coin flip situations.

Acknowledging that poker has a great deal of short-term luck, why is it that poker is generally recognized as a game of skill? From my own personal study of the game, I can confidently conclude that, at least for low-limit games, poker can be beat through dedicated study.

So why can’t the same be said about financial markets? Economists generally agree that investors cannot achieve consistent market-beating returns and recommend an indexing approach instead.

Daniel Kahneman, recipient of the Nobel Prize in Economics, presents a plausible explanation in a @Google Talk. Watch (at the very least) 14:00 to 18:00.

Kahneman suggests that only in the regular world — a world defined by rules and similar, repeated experiences — can intuitive expertise be developed. He presents an interesting example: an anesthesiologist versus a radiologist. An anesthesiologist often sits by a patient’s head throughout a surgery, constantly monitoring a wide variety of patient vital signs. The anesthesiologist gets excellent and clear feedback when something goes wrong or right. The radiologist, on the other hand, lives in what Kahneman describes as the chaotic world. When diagnosing whether a patient has a tumor, for example, the radiologist does not know whether he was right in his diagnosis until much later. In other words, the radiologist isn’t faced with enough repeated experiences to develop intuitive expertise.

Kahneman goes on to say that he believes participants in financial markets live in the chaotic world and thus skill in stock picking cannot be developed. Could it be that financial markets are so dynamic that there aren’t enough repeated experiences for market participants to truly gain expertise?

When Monkeys Use Money

What would happen is, you would bring Felix from the big cage to the little cage down here and you would give him a coin. Felix would take the coin; he would try to sniff it and eat it. When he could see that it’s not edible or that he couldn’t have sex with it, he’d get rid of it. Then what you would have to do is you would give the monkey a coin and offer some food. The monkey would take the food and then you would take the coin out of the monkey’s other hand. It took on average about six months for these seven monkeys to learn that if you give a coin then in exchange you get food — you can buy food.

Nassim Taleb: On The Difference Between Noise And Information

Nassim Taleb advocates minimal exposure to the media as a guiding principle (read the previous post in this series here) because humans inherently are unable to tell the difference between noise and information.

Realizing this inability can lead to more generalized conclusions. First, we examine the difference between noise and information through an example borrowed from the investment world:

Let us manufacture a happily retired dentist, living in a pleasant, sunny town. We know a priori that he is an excellent investor, and that he will be expected to earn a return of 15% in excess of Treasury bills, with a 10% error rate per annum (what we call volatility). It means that out of 100 sample paths, we expect close to 68 of them to fall within a band of plus and minus 10% around the 15% excess return, i.e. between 5 and 25% (to be technical; the bell-shaped normal distribution has 68% of all observations falling between -1 and 1 standard deviations). It also means that 95 sample paths would fall between -5% and 35%.

Clearly, we are dealing with a very optimistic situation. The dentist builds for himself a nice trading desk in his attic, aiming to spend every business day there watching the market, while sipping decaffeinated cappuccino.

The dentist is clearly a skilled investor. With a mean return of 15 percent and a volatility of 10 percent, the dentist can expect to be successful 93 percent of the time in a given year. That is, the dentist will achieve positive excess returns. When examined on extremely short time scales, however, the dentist will have only a marginal chance of achieving positive excess returns. On a given second, for example, the dentist has a 50.02 percent chance of success:

Over the very narrow time increment, the observation will reveal close to nothing. Yet the dentist’s heart will not tell him that. Being emotional, he feels a pang with every loss, as it shows in red on his screen. He feels some pleasure when the performance is positive, but not in equivalent amount as the pain experienced when the performance is negative.

At the end of every day the dentist will be emotionally drained. A minute-by-minute examination of his performance means that each day (assuming eight hours per day) he will have 241 pleasurable minutes against 239 unpleasurable ones. These amount to 60,688 and 60.271, respectively, per year. Now realize that if the unpleasurable minute is worse in reverse pleasure than the pleasurable minute is in terms, then the dentist incurs a large deficit when examining his performance at a high frequency.

Here Taleb draws upon one of the fundamental assumptions of economics and finance — humans are risk adverse in that they fear losses more than they like gains. On  a short time scale, investors observe the variance of the portfolio, not the returns. This variance contains little information of value, and in fact, observing a portfolio at any time scale always contains a combination of returns and variance. Furthermore, human emotions are unable or unwilling to understand the difference between the returns and variance of a portfolio. Undue reliance on short term fluctuations in a portfolio can be very damaging to an investors mental health:

Finally, this explains why people who look too closely at randomness burn out, their emotions drained by the series of pangs they experience. Regardless of what people claim, a negative pang is not offset by a positive one (some psychologists estimate the negative effect for an average loss to be up to 2.5 the magnitude of a positive one); it will lead to an emotional deficit.Now that you know that the high-frequency dentist has more exposure to both stress and positive pangs, and that these do not cancel out, consider that people in lab coats have examined some scary properties of this type of negative pangs on the neural system (the usual expected effect: high blood pressure; the less expected: chronic stress leads to memory loss, lessening of brain plasticity, and brain damage). To my knowledge there are no studies investigating the exact properties of trader’s burnout, but a daily exposure to such high degrees of randomness without much control will have physiological effects on humans (nobody studied the effect of such exposure on the risk of cancer). What economists did not understand for a long time about positive and negative kicks is that both their biology and their intensity are different. Consider that they are mediated in different parts of the brain — and that the degree of rationality in decisions made subsequent to a gain is extremely different from the one after a loss.

Restricting oneself to noise (either to the media or the short-term fluctuations in one’s portfolio) can lead to more rational investing decisions. “Silence is far better,” writes Taleb.

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Why Do Investors Believe They Can Do Better Than The Market?

Why do Wall Street traders have such faith in their powers of prediction, when their success is largely down to chance?

Lately I have been seeing several articles on Daniel Kahneman’s new book, Thinking, Fast and Slow in which he examines how cognitive illusions skew our view of financial markets.

Some years ago I had an unusual opportunity to examine the illusion of financial skill up close. I had been invited to speak to a group of investment advisers in a firm that provided financial advice and other services to very wealthy clients. I asked for some data to prepare my presentation and was granted a small treasure: a spreadsheet summarising the investment outcomes of some 25 anonymous wealth advisers, for each of eight consecutive years. Each adviser’s score for each year was his (most of them were men) main determinant of his year-end bonus. It was a simple matter to rank the advisers by their performance in each year and to determine whether there were persistent differences in skill among them and whether the same advisers consistently achieved better returns for their clients year after year.

To answer the question, I computed correlation coefficients between the rankings in each pair of years: year 1 with year 2, year 1 with year 3, and so on up through year 7 with year 8. That yielded 28 correlation coefficients, one for each pair of years. I knew the theory and was prepared to find weak evidence of persistence of skill. Still, I was surprised to find that the average of the 28 correlations was 0.01. In other words, zero. The consistent correlations that would indicate differences in skill were not to be found. The results resembled what you would expect from a dice-rolling contest, not a game of skill.

No one in the firm seemed to be aware of the nature of the game that its stock pickers were playing. The advisers themselves felt they were competent professionals doing a serious job, and their superiors agreed. On the evening before the seminar, Richard Thaler and I had dinner with some of the top executives of the firm, the people who decide on the size of bonuses.

We asked them to guess the year-to-year correlation in the rankings of individual advisers. They thought they knew what was coming and smiled as they said “not very high” or “performance certainly fluctuates”. It quickly became clear, however, that no one expected the average correlation to be zero.

You may also be interested in a short interview of Kahneman on CNBC below:


Continue reading here. Buy Thinking, Fast and Slow here.

Last Place Aversion

One might think that with the 2008 recession, potential double-dip recession, and the Occupy Wall Street movement gaining popularity, society as a whole would approve of redistribution of income: moving wealth from the wealthy (the top 1%) to the poor. After all, this is what Occupy Wall Street is about, isn’t it? Protesting against the unfair distribution of income?

Survyes, however, paradoxically show that support for redistribution has declined during the past few years. Academic research has a potential explanation:

How does last-place aversion play out with regard to redistribution? In our surveys, we asked Americans whether they supported an increase to the minimum wage, currently $7.25 per hour. Those making $7.25 or below were very likely to support the increase – after all, they would be immediate beneficiaries. In addition, people making substantially more than $7.25 were also fairly positive towards the increase. Which group was the most opposed? Those making just above the minimum wage, between $7.26 and $8.25. We might expect people who make just below and just above $7.25 to have similar lifestyles and policy attitudes – but in this case, while those making below $7.25 would benefit if the minimum wage were raised to, say, $8.25, those making just above $7.25 would run the risk of falling into a tie for last place.

We’ve also found evidence of last place aversion in laboratory experiments. In one, we created an artificial income distribution by endowing individuals with different sums of money and showing them their “rank”– with each rank separated by $1. We then gave them an additional $2, which they had to give to either the person directly below or directly above them in the distribution. In this income distribution, of course, giving $2 to the person below you means he will jump ahead of you in rank. In our experiments, most people still give to the person below them – after all, the alternative is to give $2 to a person who already has more money than you. People in second-to-last place, however, who would fall to last place when giving the money to the person below them, are the least likely to do so: so strong is their desire to avoid last place that they choose to give the money to a wealthier person (the person above them) nearly half the time. If Americans behave like people in our experiments, then it could be challenging to unite those in the bottom of the income distribution to support redistribution.

Continue reading here.