Lecture 4: Psychology, Heuristics, and Biases

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Master of Arts programin Applied Finance

Nattawut Jenwittayaroje, Ph.D, CFANIDA Business SchoolNational Institute of Development Administration

Behavioral Finance

Limits to Arbitrage Psychology

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Psychology and Finance• An old Wall Street adage states that greed and fear move the

market.• Although true, the human mind is so complex that greed and fear

do not adequately describe the psychology that affects people as they make investment decisions “Beyond Greed and Fear”.

• Traditional finance developed the tools that investors use to optimize expected return and risk, yielding tools such as asset pricing models, portfolio theories, and option pricing.

• However, psychological biases inhibit one’s ability to make good investment decisions (e.g., using such tools in their decisions).– “The investor’s chief problem, and even his worst enemy, is

likely to be himself” Benjamin Graham

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Psychology and Finance• In the world of behavioural finance, people are normal rather than

rational. They do commit cognitive error. They do experience pain of regret. They do possess less than perfect control over their actions.

• By learning about your psychological biases, we can overcome them and increase your wealth (at least by avoiding common mistakes due to our psychological biases).

• The evidence that psychology and emotions influence financial decisions became more and more convincing over the last two decades – the 2002 Nobel Prize in Economics awards to psychologist Daniel

Kahneman and (experimental economist) Vernon Smith.• Psychological biases have been considered in the behavioural

finance literature and have been used to explain a number of the anomalies in the finance literature.

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Psychology and Finance• Normal humans are imperfect, and information requirements for

some models are too much.– CAPM assumes that investors are capable of studying the universe of

securities in order to come up with all required model inputs (e.g., expected returns and variances for all securities, as well as covariancesamong all securities. Only then is the investor able to make appropriate portfolio decisions.

• In reality, however, people make decisions with limited time and information in a world of uncertainty.

• Because too much information is difficult to deal with, people have developed shortcuts or heuristics in order to come up with reasonable decisions.– Unfortunately, sometimes these heuristics lead to bias, especially when

used outside their natural domains.

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Figure 1: Optical Illusion: Which looks longer?

Similar to vision, assessing financial data is a complex problem which can lead to the primary mental mistakes of over- and underreaction to new company information due to shortcuts.

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A range of psychological biases

• Overconfidence• Familiarity• Ambiguity aversion• Representativeness• Hot Hands• Gambler’s fallacy• Availability biases• Anchoring• Conservatism

Overconfidence

• Extensive evidence shows that people are overconfident in their judgements.

• Firstly, overconfidence relates to the confidence intervals people assign to their estimates of quantities. Generally, the intervals are far too narrow.

• The second aspect of overconfidence is that people are poorly calibrated when estimating probabilities: – events they think are certain to occur actually occur only around

80% of the time, and;– events they deem impossible occur approximately 20% of the

time [Fischhoff, Slovic and Lichtenstein (1977)].

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• Overconfidence may in part stem from two other biases, self-attribution bias and hindsight bias.

• Self-attribution bias refers to people’s tendency to ascribe any success they have in some activity to their own talents, while blaming failure on bad luck.

• For example, investors might become overconfident after several quarters of investing success [Gervais and Odean(2001)].

• Hindsight bias is the tendency of people to believe, after an event has occurred, that they predicted it before it happened.

Overconfidence

Enter the range (minimum and maximum) for which you are 90% certain that the answer lies within.

Min Max1. How long (in miles) is the Grand Canyon? _____ _____

2. In what year did Michael Angelo finish painting the ceiling of the Sistine Chapel ? _____ _____

3. How many countries were members of the United Nations in 2004? _____ _____

4. How many species of spiders have been identified in the world? _____ _____

5. How many hair follicles are on an average adult human? _____ _____

6. In what year was the Great Pyramid of Giza built? _____ _____

7. How many new copyrights were registered at the US Copyright Office in 2003? _____ _____

8. How many planets the size of the Earth would fit into the sun? _____ _____

9. On average, how many magnitude 3.0 and higher earthquakes are located worldwide annually by the US Geological Survey? _____ _____

10. What was the 2000 Census estimate for the US population? _____ _____

Source: Hirschey and Nofsinger (2010) _____ _____

Answers

1. How long (in miles) is the Grand Canyon? 277 miles

2. In what year did Michael Angelo finish painting the ceiling of the Sistine Chapel? 1512

3. How many countries were members of the United Nations in 2004? 191 countries

4. How many species of spiders have been identified in the world? 38,432 species

5. How many hair follicles are on an average adult human? 5 million hairs

6. In what year was the Great Pyramid of Giza built? 2560 BC

7. How many new copyrights were registered at the US Copyright Office in 2003? 514,121

8. How many planets the size of the Earth would fit into the sun? 1.3 million

9. On average, how many magnitude 3.0 and higher earthquakes are located worldwide annually by the US Geological Survey? 14,000 earthquakes/year

10. What was the 2000 Census estimate for the US population? 280 million people

Did you get 9 out of 10?• Did you get 9 out of 10?• Most people miss five or more• Range is too narrow usually the higher number is too low.

– Overconfident about level of knowledge– We are too certain about your answers (i.e., desire to be

“accurate”), even when you have no information or knowledge about the topic.

• This illustrates that people have difficulty evaluating the precision of their knowledge and information.

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One more question

On October 1, 1928, the “modern era” began for the Dow Jones Industrial Average (DJIA) began when the component list was expanded to 30 from 20 stocks, and several substitutions were made. A divisor was also introduced to adjust for the effect of stock splits, stock distributions, and stock substitutions.

In 1929, the year began with the DJIA at 300. At the end of 2007, the DJIA was at 13,265.

The DJIA is a price-weighted average. Dividends are omitted from the index.

What would the DJIA average be at the end of 2007 if dividends were reinvested each year? Also write down a low guess and a high guess so that you feel 90% confident the true answer lie in your guesses.

If dividends were reinvested in the DJIA, the average would

have been 327,011 at the end of 2007!

• If you are 90% sure, then you should be correct for this question.• Anchoring

– Mental attachment to a specific number or price– People tend to anchor on the current DJIA and try to add an

appropriate amount to compensate for the dividends investors anchor on their stock’s buying price and the recent highest stock price.

• Overconfidence– Even after learning that most people set their range too narrowly

in their prediction and experiencing the problem first hand, most people continue to do it.

Heuristics

• Heuristics or rules-of-thumb: decision-making shortcuts.– A heuristic is a decision rule that utilized a subset of the

information set.– This is because in every case, people must economize and

cannot analyze all contingencies, we use heuristics instead (and without even realizing it).

• Necessary because the world, being a complicated place, must be simplified in order to allow decisions to be made.

• Heuristics often make sense but falter when used outside of their natural domain.

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Familiarity and Related Heuristics• A series of related heuristics that induce people to exhibit

preferences unrelated to objective considerations.• People are comfortable with the familiar.• People dislike ambiguity and normally look for ways to avoid

unrewarded risk.• People tend to stick with what they have rather than

investigate other options (i.e., status quo).• All of these result from a tendency to seek comfort.

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Familiarity• People are more likely to accept a gamble if they feel they

have a better understanding of the relevant context, that is, if they feel more competent.

• Heath and Tversky conducted the following experiment.– A series of general knowledge multiple choice questions (with

four choices) were tested.– With four possible choices, confidence of 25% indicated pure

guessing.– Let’s say that a particular participant had a self-assessed

confidence rating of 60% (averaged over all questions).– She would then be offered a choice of two gambles: one where a

payoff was randomly obtained with a 60% probability, and a second where a payoff was received if one of her randomly selected answers was correct.

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Familiarity• There exists a positive relationship between

• 1) judged probability of being right on the questions and • 2) the percentage choosing the competence bet.

• That is, when people felt that they had some competence on the questions, they were more likely to choose a gamble based on this competence rather than a random lottery.

• Note that whatever the self perceived level of knowledge, the probability of success on the bet was viewed by participants as identical between the two alternatives (according to their own statements).

• The logical conclusion is that people have a preference for the familiar.

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Ambiguity aversion• In experiments by Ellsberg (1961), suppose there are two bags.

– The 1st bag that have 50 black balls and 50 red balls.– The 2nd bag contained 100 balls of black and red balls in unknown

proportions.• Then they are asked to choose one of the following two

gambles.A1) A ball is drawn from Bag 1, $100 if black, $0 if red.A2) A ball is drawn from Bag 2, $100 if black, $0 if red.

• Then they are asked to choose another of the following two gambles.B1) A ball is drawn from Bag 1, $100 if red, $0 if black.B2) A ball is drawn from Bag 2, $100 if red, $0 if black.

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Ambiguity aversion• People are more willing to choose A1 and B1!!• However, the choice of A1 implies that we expect fewer than 50%

of the balls in Bag 2 are black, while the choice of B1 implies the opposite.

• Lesson: people do not like situations where they are uncertainabout the probability distribution of a gamble. That is, they are more comfortable with risk than uncertainty (i.e., ambiguity)“ambiguity aversion”

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Application of Familiarity and Ambiguity aversion• A large body of evidence suggests that investors diversify their

portfolio holdings much less than is recommended by normative models of portfolio choice.

• First, investors exhibit a “home bias”.– French and Poterba (1991) report that investors in the US, Japan, and

UK allocate 94%, 98%, and 82% of their overall equity investments, respectively, to domestic equities.

• Second, some studies found an analog to home bias within countries.– In Finland, Grinblatt and Keloharju (2001) find that investors are much

more likely to hold and trade stocks of Finnish firms which are located close to them geographically, which use their native tongue in company reports, and whose CEOs share their cultural background.

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Investor international holdings

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Source: French, K. R., and J. M. Poterba, 1991, "Investor diversification and international equity markets," American Economic Review 81, 222-26.

Application of Familiarity and Ambiguity aversion Potential home bias explanations

• Excessive optimism about prospects of domestic market.

• Another behavioral explanation -> comfort-seeking and familiarity.– People tend to favor what is familiar. What is familiar is good

(i.e., a good investment)

• Institutional restrictions: (likely plays a very minor role)– Capital movement restrictions– Differential trading costs– Differential tax rates

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Application of Familiarity and Ambiguity aversion• Ambiguity and familiarity offer a simple way of understanding

the different examples of insufficient diversification.• Investors may find their national stock markets more familiar –

or less ambiguous - than foreign stock indices.• They may find firms geographically close to them more

familiar• They may find their employer’s stock more familiar than other

stocks.• Their portfolios therefore appear insufficiently diversified.

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Bayes’ rule

• pr(A|B) = pr(A) * [pr(B|A) / pr(B)]• This is a way of updating your probability estimate based on new

information.

• Example: You have a barometer that predicts weather.• Here is the numbers:

• pr(rain) = pr(R) = 40%• pr(dry) = pr(D) = 60%• pr(rain predicted | rain) = pr(RP|R) = 90%• pr(rain predicted | dry) = pr(RP|D) = 2.5%

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Bayes’ rule• Best prediction of tomorrow’s weather without looking at

barometer is prior (i.e., base rate) distribution: you would say 40% chance of rain.

• What should you predict when barometer predicts rain? That is, what is probability of rain conditional on rain being predicted?• pr(R|RP) = pr(R) * [pr(RP|R) / pr(RP)]

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Bayes’ rule• We first need to work out pr(RP).• This equals:

pr(RP R) + pr(RP D)• Use conditional probability rule:

pr(RP|R) = pr(RP R) / pr(R) • Re-arrange:

pr(RP R) = pr(RP|R) * pr(R) = .9 * .4 = .36

• Next work out pr(RP D).• Begin with conditional probability:

pr(RP|D) = pr(RP D) / pr(D) • Re-arrange:

pr(RP D) = pr(RP|D) * pr(D) = .025 * .6 = .015• Therefore pr(RP) = .36 + .015 = .375

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Using Bayes’ rule

• Best prediction of tomorrow’s weather without looking at barometer is prior (base rate) distribution: you would say 40% chance of rain.

• What should you predict when barometer predicts rain?pr(R|RP) = pr(R) * [pr(RP|R) / pr(RP)]

= .4 * (.9 / .375) = .96

• Base rate underweighting would imply that you believe there is a higher than .96 chance of rain conditional on rain being predicted.

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Representativeness

• Kahneman and Tversky (1974): representativeness heuristic. • People judge probabilities “by the degree to which A is

representative of B, that is, by the degree to which A resembles or reflects the essential characteristics of B.”– A can be sample and B a population OR A can be a person and B

a group OR A can be an event/effect and B a process/cause• Behaviors associated with representativeness:

– Base rate neglect/underweighting– Hot hand– Gambler’s fallacy

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Representativeness• To understand why reliance on representativeness can lead to

underweighting of base rate information, consider one of the experiments by Kahneman and Tversky (1973).

• Subjects were shown personality sketches, allegedly from a group of professionals made up of engineers and lawyers.

• In one treatment, subjects were told that 70% are engineers: 30% lawyers.

• In another, they were told that 30% are engineers: 70% lawyers.• Consider the following sketch that was presented:

“James is a 30 year old man. He is married with no children. A man of high ability and high motivation, he promises to be quite successful in his field. He is well liked by his colleagues.”

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Representativeness• At the end of the description of James, subjects were asked to fill

in a probability: “The probability that James is an engineer in the sample of 100 is ___%.”

• Actually, this sketch is designed to be neutral.• Indeed, subjects saw this description as neutral, with about 50%

saying James was a lawyer and 50% saying James was an engineer.

• However, the problem was that the answer is still 50%, regardless of whether they had been previously told that 70% of the sample were engineers or 70% of the sample were lawyers!!

• In other words, subjects were ignoring the base rate (i.e., base rate neglect).

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Representativeness• In the experiment just described, the “base rate,” or Bayesian prior,

corresponds to the relative proportions of engineers to lawyers. The description of James corresponds to “singular information.”

• Bayes’ rule requires that base rate information and singular information be properly combined when judging the probability that James is an engineer, but Kahneman and Tversky found that most people focused on singular information, severely underweighting base rate information.

• In other words, people formed their assessment of probabilities that James is an engineer or lawyer by overweighting the extent to which the description of James is representative of their stereotype of an engineer or lawyer and underweighting the information about the proportions of engineers and lawyers in the population.

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Representativeness: Extrapolation in Stock Markets• If investors use stereotypes, they may see patterns that do not

exist. • Barberis, Shleifer, and Vishney give the example of investors

in the stock market who classify some stocks as growth stocks based on a history of consistent earnings growth.

• Investors ignore the fact that there are very few companies whose earnings just keep growing.

• Lesson: investors do not follow Bayes’ rule when they update probabilities with new information arriving.

Hot hand phenomenon• Based on representativeness, sometimes they feel that

distribution/population should look like sample “law of small numbers”

• That is, when people do not initially know the data generating process, they will tend to infer it too quickly on the basis of too few data points.• For example, a financial analyst with four good stock picks is talented

(because four successes are not representative of a bad analyst).• A basketball player who had made three shots in a row is on a hot

streak and will score again “Hot Hand” phenomenon.• Gilovich, Vallone and Tversky show that there is no evidence of a hot

hand in the data (basketball), i.e., p(hit / 3 hits) is even lower than p(hit / 3 misses).

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Gambler’s fallacy• Gambler’s fallacy may apply if people are fairly sure about

nature of population.• They think even small samples should always look like

population. So if you flip coin 9 times getting 6 heads and 3 tails, these

people would say that a tail is more likely to come next… “We are due for tails.” Since they believe that even a short sample should be

representative of the fair coin, there have to be more tails to balance out the large number of heads.

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Availability Biases• When judging the probability of an event – the likelihood of getting

mugged in Chicago, say – people often search their memories for relevant information.

• Although this is a perfectly sensible procedure, it can produce biased estimates because not all memories are equally retrievable or “available”.

• Tversky and Kahneman (1974) more recent events and more salient events (e.g., the mugging of a close friend) will weigh more heavily and distort the estimate.

• Availability bias can causes investors to over-react to “very positive” or “very negative” events.

• Barber and Odean (2002): people typically buy a stock that has caught their attention.• i.e., stocks that experienced extreme past performance or on the front

cover of the newspaper are much easier to be recalled or easily available)

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Anchoring• People tend to make estimates by starting from an initial value and

adjusting it to generate a final estimate (People are initially anchored on their prior belief.) However, often the adjustment is insufficient.

• Quickly multiply these eight numbers:

1 * 2 * 3 * 4 * 5 * 6 * 7 * 8

• Most people will come up with a low estimate (i.e., 512 in an experimental setting vs the true answer of 40,320): anchored on product of first 4 or 5 numbers. A bit better (but still too low) with:

8 * 7 * 6 * 5 * 4 * 3 * 2 * 1

The median answer was 2,250. still use the product of the first few numbers as an anchor, without regard to the sequence length.

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Anchoring bias: Example of anchoring to irrelevant information• Wheel with numbers 1-100 was spun. Subjects were then asked two (sequential) questions:

(1) Is the percentage of African nations in the UN more or less than wheel number?(2) What is the percentage of African nations in the UN? ***

Obviously this percentage has nothing to do with the result of the wheel spin, but the answers were highly influenced by wheel:

• Median answer was 25 for those seeing 10 from wheel.• Median answer was 45 for those seeing 65 from wheel.

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Anchoring: Real estate appraisal study

• Two randomly selected groups of real estate agents were taken to a house and asked to appraise it.

• Same information set, including house’s (purported) list price.

• Only difference between the two groups was that the first group was given a list price of $65,900, while the second group was given a list price of $83,900 -- $18,000 more.

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List prices and appraisals

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Source: Northcraft, G. B., and M. A. Neale, 1987, "Experts, amateurs and real estate: An anchoring-and-adjustment perspective on property pricing decisions,“Organizational Behavior and Human Decision Processes 39, 84-97.

Anchoring: Real estate appraisal study

• Average appraisal price of the first group came in at $67,811 – second group was at $75,190.

• If we take the mid-point of these values ($71,500.50) as our best estimate of the true appraisal value, the gaps between the two appraisal averages was a full 10%.

• Agents were anchored on list prices that they were exposed to – despite the fact that only 25% mentioned list price as one of the factors that they considered.

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Anchoring vs. representativeness

• Anchoring says new information is discounted.• Representativeness (base rate neglect variety) says people are

too influenced by latest information.• Potential conflict between anchoring and representativeness in

how people deal with new evidence.

• Which is right?– Perhaps both depending on situation…

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Anchoring vs. representativeness• It is argued that people are “coarsely calibrated.” That is, they

see things in black and white.• Suppose morning forecast is for sun. Day starts sunny. You go

on a picnic. Some dark clouds start to move in You are anchored to prior view and discount clouds More dark clouds. Continue to discount them…… ……………Even more dark clouds. Now you coarsely transition – thinking that “it’s going to rain for

sure!” What is reality? Never 0% or 100%. New information should alter

probabilities but a complete change of position does not make sense. Coarse calibration has been used to explain tendency for prices to

trend (anchoring -> under-reaction) and eventually reverse (base rate underweighting/representativeness over-reaction).

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Conservatism• While representativeness leads to an underweighting of base rates, there

are situations where base rates are over-emphasized relative to sample evidence (“conservatism”)

• Once people have formed a probability estimate, they are often quite slow to change the estimate when presented with new information. This slowness to revise prior probability estimates is known as “conservatism” (Phillips & Edwards, 1966).

• Bodie, Kane and Marcus (2005) “A conservatism bias means that investors are too slow (too conservative) in updating their beliefs in response to recent evidence. This means that they might initially underreact to news about a firm, so that prices will fully reflect new information only gradually. Such a bias would give rise to momentum in stock market returns.”

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The implications of conservatism (and anchoring) • Edwards (1968) illustrates the impact of conservatism (and

anchoring):– Imagine we have 2 bags each of which contains 10 balls.– The first bag contains 3 black balls and 7 red balls.– The second bag contains 7 black balls and 3 red balls.

• One of the bags is chosen at random.

Question 1: What probability would you assign to the event that the selected bag contains predominantly red balls (i.e., the first bag)?

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The implications of conservatism (and anchoring) • Now imagine that a random draw of 12 balls, with

replacement, from one of the two bags yields 8 reds and 4 blacks.

• Would you use the new information about the drawing of balls to revise your probability that the selected bag contains predominantly red balls (i.e., the first bag)?

Question 2: What new probability would you assign to the event that the selected bag contains predominantly red balls (i.e., the first bag)?

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• This problem is very similar to the tasks faced by financial analysts.

• The bag is like a company that in the future may operate in the black (good future earnings) or in the red (poor future earnings).

• Analysts start out with information that leads them to form their initial beliefs. (question 1)

• Once they have formed their initial beliefs how will they react to negative earnings announcements (question 2)?

• That they will not revise their earnings estimates enough to reflect the new information.

The implications of conservatism (and anchoring)

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• At first sight, the evidence of conservatism appears at odds with representativeness.

• However, there may be a natural way in which they fit together.

• If a data sample is representative of an underlying model, then people overweight the data (i.e., ignoring the base rate).

• However, if the data is not representative of any salient model, people react too little to the data and rely too much on their priors (base rates).– In the bag experiment, the draw of 8 reds and 4 blacks is not

particularly representative of either bag, possibly leading to an overreliance on prior information.

Conservatism vs Representativeness