Psychology of Investing Success LAPERS Conference Presentations/Swaminathan.pdfAgenda Efficient markets vs. behavioral finance Psychology of individual behavior Heuristics and cognitive

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Psychology of Investing Success

LAPERS ConferenceNew Orleans

September 18, 2018

Dr. Bhaskaran Swaminathan, Ph.D.

Partner & Director, Research

LSV Asset Management

(c) 2018 Dr. B. Swaminathan, LSV Asset Management

>> Agenda

Efficient markets vs. behavioral finance

Psychology of individual behavior

Heuristics and cognitive biases

Two systems of thinking

Cognitive reflection test (CRT)

Various behavioral biases: Optimism, Overconfidence, Anchoring,

Loss aversion, Law of small numbers, Disposition effect, etc.

Intuitions vs. Formulas

Quantitative investment strategies to avoid cognitive biases

Conclusions

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>> Efficient markets hypothesis implies Price = Value. Is Price = Value?

Behavioral finance argues that price may not always equal value due to the actions of irrational investors (see Barberis and Thaler (2003)).

It traces its roots to the path-breaking work of psychologists Daniel Kahneman and Amos Tversky.

– Kahneman and Tversky (1973, 1974, 1979), Kahneman (2011) (Thinking, Fast and Slow).

Fischer Black (1986) refers to irrational investors as noise traders.

– Noise traders believe they are trading on information although they are trading on noise.

– If they all act in the same direction and there are limits to arbitrage then they can cause prices to deviate from intrinsic value.

Security analysis and active money management attempt to arbitrage the mispricing caused by noise traders.

Efficient Markets vs. Behavioral Finance

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People are Expected Utility maximizers:

– People’s utility or happiness increases with their wealth (people prefer more to less). Suggests people with equal wealth should be equally happy.

– People are risk averse and they demand a risk premium for taking risk. This implies people’s happiness increases at a decreasing rate (an extra million dollars is worth more to us than to Bill Gates).

People are Bayesians:

– They update their prior probabilities of events, given new

information, using Bayes Theorem.

Investor behavior that doesn’t follow these norms is referred to as irrational behavior.

Let us explore the common behavioral biases that lead individuals to exhibit irrational behavior.

Rationality in Economics

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>> Investor Behavior

“The investor’s chief problem—and even his worst enemy—is likely to be himself.”

- Benjamin GrahamConsidered the father of value investing

“Investing is simple, but not easy.”

“Success in investing doesn’t correlate with IQ

once you are above the level of 100….what you

need is the temperament to control the urges

that get other people into trouble in investing.”

- Warren Buffett

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Good investing is about making good predictions.

Making good predictions is about understanding probability and

statistics.

People are good intuitive grammarians.

Are they good intuitive statisticians?

Are they good at estimating probabilities?

Psychology of Investing Success

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A Heuristic is a short-cut or rule of thumb that helps us make difficult decisions or answer

difficult questions.

– Examples: Educated guess, Common sense, Gut

feeling.

–Naïve diversification: (1/N) strategy.

We often use heuristics when estimating

probabilities or making decisions.

These heuristics can give rise to systematic biases

in our judgments and decision-making.

Heuristics and Cognitive Biases

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Psychologists* refer to two different

systems embedded in our minds to

explain the way our brains work.

– System 1 is the Dr. McCoy (M) System

which is emotional, intuitive, reflexive,

effortless and fast.

– System 2 is the Spock (S) System which is rational, logical, and analytical, but also

slow and lazy.

The M system can give rise to cognitive

biases. The S system attempts to monitor and control the M system, but is not

always successful.

Dr. McCoy and Spock

*Keith Stanovich and Richard West (2000), See Montier (2010) for the analogy to Dr. McCoy and Spock. 8


>> How Do They Work?

This is not automatic. McCoy is useless here. This requires

Spock.

Recognizing the sad face and the smiley face is automatic,

which is the work of the M

system.

What is 95 × 78?

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There is a division of labor between the two systems.

M System:

– Our brain’s go-to system for routine decisions and is always on.

– Takes over in emergencies, reacts automatically to dangers and

challenges, and deals with familiar situations.

– Doesn’t understand logic and statistics.

S System:

– Continuously monitors the M system and tries to keep it out of trouble.

– Called to action only when the M system runs into difficulty (95 × 78).

– Only one that can follow rules and exercise executive control, but it has

limited resources.

From Neuroscience: Parts of the brain associated with the M

system are evolutionarily older than those of the S system.

M system vs. S system

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>> Amygdala vs. Prefrontal Cortex

Amygdala:

– It is an ancient brain network that is

present even in primitive animals

like mice and rats. It evolved before

the cortex.

– It is responsible for our fears,

anxieties, survival instincts and fight-

or-flight responses. It lets us react to

threatening events well before our

rational brain has time to process

things.

Prefrontal Cortex

– Responsible for thinking, planning

and decision making.

– Responsible for what I am doing

right now: speaking, explaining,

thinking.

Thalamus: Receives incoming

stimuli and sends signals to both

the Amygdala and Cortex.

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Shane Frederick (2005) has designed a Cognitive Reflection Test

(CRT) consisting of 3 questions that measures susceptibility to the

M system.

– The score is 3 for answering all questions correctly and 0 for answering

none of them correctly.

– A low score suggests greater influence by the emotional M system and

a high score suggests lesser influence.

– The test was administered to various groups (see the next page) and

only 17% got all correct and1/3rd of the participants got none of the

answers correct!

This suggests that most of us are influenced by the emotional M

system.

Are We Dr. McCoy or Spock?

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>> CRT Test Scores

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>>

Answer the following questions in 90 seconds or 30 seconds per

question (no cheating!)

(Answers provided on the last page.)

The CRT Test

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According to Psychologists M system is influential:

– When the S system is cognitively busy or impaired

» People’s behavior after a few drinks, a sleepless night, or a long, stressful meeting.

– When the problem is ill-structured and complex.

– When information is incomplete, ambiguous, and changing.

– When goals are ill-defined, shifting, or competing.

– When stress is high due to time constraints or high stakes.

– When decisions depend on interacting with other people!!

All of this more or less applies to investment decisions!

Quantitative investment strategies attempt to minimize the influence of the more emotional M system and enhance the role of the more rational S system.

When Are We Likely to Rely on the M System?

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Optimism bias

Overconfidence and illusion of confidence

The law of small numbers

Regression to the mean

Anchoring

Prospect Theory and Loss Aversion

Confirmation bias, and Familiarity bias

Availability Heuristic and Hindsight bias

Affect Heuristic and Halo effect

Base Rate Neglect and Representativeness


Various Behavioral Biases

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Time permitting

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Consider these questions:

– What is a new restaurant owner’s estimate of chance of success?

– What is an entrepreneur’s estimate of chance of success?

– What is a CEO’s estimate of value gain to her/his shareholders from a

recent acquisition?

60% of new restaurants are out of business in 3 years. The chances

of a small business surviving for 5 years in the U.S. are about 35%.

Optimistic CEOs take on too much debt, overpay for targets, and

engage in value-destroying mergers (Roll, 1986: “Hubris

Hypothesis”; Malmendier and Tate, 2008).

Lench and Ditto (2008): People believe more positive than

negative life events will occur to them (which is a good thing).

Optimism Bias

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Optimism likely has an evolutionary advantage and psychologists have shown that it is largely inherited.

Optimists are the inventors, the entrepreneurs, and successful political and military leaders. They are cheerful, resilient, healthier, and live longer. They are necessary for the economic development of a society. The key though is to be optimistic without losing track of reality.

While optimism may be good for the society and a good life strategy, it is not necessarily a good investment strategy.

– Forecasts of Dow 36,000 in 1999 during the dot-com bubble.

– Over-optimism during the 2001-2006 Housing Bubble.

– Forecasts of peak oil a few years ago.

Be skeptical in evaluating optimistic forecasts and investment recommendations.

Engage in premortem to overcome groupthink and over-optimism.

Optimism Bias

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>> Overconfidence

Psychologist David Myers coined the term “The Lake Wobegon

Effect” to illustrate the idea of overconfidence.

Lake Wobegon is an illusory town from the show A Prairie Home

Companion, “where all the women are strong, all the men are good looking, and all the children are above average.”

The idea that everyone thinks they are above average captures

the notion of overconfidence.

Overconfidence leads people to overestimate their abilities and

forecasting skills.

It leads to confidence intervals that are too narrow and

judgments that are too certain, which can give rise to extreme

actions and excessive trading.

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Doctors vs. Weathermen (Montier, 2010):

– Doctors think they are right 90% of the time on their diagnoses based on case

notes but are correct only 15% of the time!

– Weathermen think they are right 50% of the time on their predictions of weather

patterns and they are right 50% of the time. Why the difference?

In a survey, CFOs predicted 1-year S&P 500 returns and a 10 percentile

low estimate and a 90 percentile high estimate.

– 11,600 forecasts collected from March 2001 to February 2010 (Ben-David, Graham, and Harvey, 2013). Zero correlation between CFO forecasts and realized returns.

– Their confidence intervals are too narrow indicating overconfidence in their forecasting skill!

Overconfidence

S&P 500 Realizations falling in various intervals

% below 10th

percentile% between 10th

and 90th

percentile

% above 90th

percentile

All Forecasts 35.6 32.8 31.6

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>> Evidence on Overconfidence

Overconfidence is universal*:

– 63% of Americans consider themselves to be above average in intelligence. 71% of Men and 57% of Women consider themselves above average!

– 70% of Canadians think they have above average intelligence.

– 69% of Swedish college students estimate they are above average drivers.

– 75% of U.S. chess players believe they were underrated relative to U.S. chess federation ratings.

– 66% of students at Cornell who took a sense-of-humor test thought they had an above-average sense of humor.

Self-attribution bias: Success is due to our ability and failure is due to external circumstances.

– Makes people more overconfident over time.

*Chabris and Simons (2010) 21


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The least skilled tend to be the most overconfident:

– Chess players who were in the bottom half of the rating scale are the ones who considered themselves the most under-rated.

– Students in the bottom 25% of a sense-of-humor test overestimated their class rank and thought they had an above-average sense of humor.

– The incompetent face two hurdles. One, they are below average in ability. Two, they are unaware they are below average. This is the Illusion of Superiority commonly referred to as the Dunning-Kruger effect (1999).

– Improving the skills of the incompetent does reduce their overconfidence.

People who are highly competent suffer from underconfidencebecause they assume everyone else is equally competent.

– Students in the top 25% of the humor test underestimated their class rank and thought they were less funny than their scores indicated!

Confidence is not an indicator of ability although people often mistake confidence for ability. Chabris and Simons (2010) refer to this as the Illusion of Confidence.

Skill and Confidence

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Barber and Odean (2000) examine data from a large discount

brokerage firm over 1991 to 1996 and find:

– Investors who trade the most earn the lowest annual returns (11%) net

of transaction costs compared to those who trade the least (19%).

– Investors who switch to online trading, trade more actively,

speculatively, and less profitably than before (Barber and Odean,

2002); Online trading reduces their returns from 2% above the market

before to 3% below the market after.

– Men trade 45% more actively than women and underperformed

women by about 1% a year (Barber and Odean, 2001).

– Overconfidence is the possible culprit.

Investors should be aware of overconfidence in their forecasting

skills and excessive trading that might result from it.

Overconfidence Among Investors

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>>

Which one of these earnings

sequences appear predictable

and how would you characterize

the performance of these firms?

Annual Earnings Patterns

Firm A Firm B Firm C

Year 1 Decrease Increase Decrease

Year 2 Increase Increase Increase

Year 3 Decrease Increase Increase



The Law of Small Numbers

The law of large numbers says that the average of results obtained

from a large sample of data should be close to the population

average (flipping coins many times to estimate probability of tails).

People, however, behave as if the law of large numbers should apply

to small samples as well (flipping coins only a few times). This

behavioral bias is referred to “tongue-in-cheek” as the law of small

numbers. A (fair) coin has no memory - Gamblers fallacy.

In small samples, random processes produce many sequences that

appear to people to be not random at all – Illusion of Pattern. 24


>> Sports Illustrated Jinx:

– February 29, 2016: Conor McGregor on the SI cover

– March 5, 2016: 2nd round loss to Nate Diaz at UFC 196

No jinx! Regression to the mean and bad luck.

Period 1 growth:

– Firm 1: Above average growth = Above average performance + Above average luck

– Firm 2: Below Average growth = Below average performance + Below Average luck

Period 2 growth

– Firm 1: Above average performance + Average luck

– Firm 2: Below average performance + Average luck

Extreme past performance tends to mean-revert.

Ignoring this can give rise to the extrapolation bias. Be aware of this in evaluating the past performance of firms, money managers, etc.

Regression to the Mean*

*Sir Francis Galton (1886) "Regression towards Mediocrity in Hereditary Stature"

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Visitors to the San Francisco Exploratorium were asked one of the following sets of questions:

– Set 1: (1) Is the height of the tallest redwood tree more or less than 1,200feet? (2) What is the height of the tallest redwood?

– Set 2: (1) Is the height of the tallest redwood tree more or less than 180 feet? (2) What is the height of the tallest redwood?

The first group estimated 844 feet on average and the second estimated 282 feet. The fact is that the height of the tallest redwood tree is only about 380 feet.

This is anchoring. People anchor to the value in the question when estimating an unknown quantity. Anchors that are random can be as effective as potentially informative anchors.

– Anchoring Index: (844-282)/(1,200-180) = 55%. This value is typical.

In negotiations, the party that is the first mover can use anchors to its advantage. If you are the counter-party, focus attention on arguments against the anchor.

Anchoring

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>>

The expected utility theory in economics says

people’s happiness is a function only of their

wealth.

It suggests people with equal wealth should be

equally happy. Are people with equal wealth

equally happy?

– Kahneman and Tversky (1979)

Prospect Theory

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Today Jack and Jill have a wealth of $5 million.

– Yesterday, Jack had $1 million and Jill had $9 million.

– Are they equally Happy?

Consider these two problems:

– Problem 1: Which do you choose?

» Get $900 for sure or 90% chance to get $1,000.

– Problem 2: Which do you choose?

» Lose $900 for sure or 90% chance to lose $1,000?

Two conclusions:

– Happiness is determined by recent change in wealth not just the currentlevel of wealth (because Jack and Jill have different reference points). Expected utility theory lacks a reference point.

– People are loss averse because the sure loss in Problem 2 is aversive. People dislike losing more than they like winning.

Prospect Theory and Loss Aversion

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The curve is S-shaped.

People are risk averse over

gains and risk-seeking over losses.

The response to losses is

stronger than the response

to gains.

– A $100 loss brings more pain

than the a $100 gain brings

pleasure.

– This is loss aversion.

Loss Averse Utility Function

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Disposition effect: People sell winners too early and hold on to losers too long (Shefrin and Statman, 1985).

– In the housing market, when prices are falling, sellers set their asking prices too high and hold on to their homes for too long.

– Brokerage customers in the U.S. exhibit disposition effect (Odean, 1998).

– Investors in the Finnish stock market exhibit disposition effect (Grinblatt and Keloharju (2001).

This is consistent with loss aversion:

– People don’t want to sell the asset and turn a paper loss into a sure loss. Instead they hold on to the asset and the risk, hoping either the loss will become smaller or that it might turn into a gain.

This is inconsistent with rational behavior:

– Tax considerations would predict: sell losers and hold on to winners.

– This is not information-based trading: Stocks that individuals sell perform better than those they do not.

Disposition Effect: Evidence of Loss Aversion

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Confirmation bias: We test our beliefs and hypotheses by

searching for confirming evidence, contrary to the

recommended practice which is to test hypotheses by trying to refute them.

– If you have an investment thesis, try looking for evidence that will refute

your thesis: Talk to colleagues who are likely to question your thesis.

Familiarity bias: Things that appear familiar also appear to be true,

e.g, a statement or an answer that sounds familiar.

– Repetition makes it easy for people to believe in falsehoods because

familiarity is not easily distinguished from truth. Psychologists refer to this

as the Illusion of Truth (Chabris and Simon, 2010).

– 10% of the brain myth, Subliminal advertising, Baby Einstein.

Confirmation Bias and Familiarity Bias

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Availability Heuristic: Judging frequencies and probabilities by the “ease with which examples come to mind.” It is heavily influenced by media coverage.

– Death by accidents was judged 300 times more likely than death by diabetes, but the true ratio is 1:4.

– Disaster insurance purchases shoot up after a disaster.

– Investors’ estimates of the probability of a crash probably increased in 2009 after observing the crash of 2008/09 even though crashes are rare.

Hindsight bias: “I knew it all along”

– The tendency to revise the history of one’s beliefs in light of what actually happened; Common among stock market pundits.

– The worse the consequence, the greater the hindsight bias (second-guessing).

– Assess the quality of a decision by the process, not by the outcome.

Availability Heuristic and Hindsight Bias

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Affect Heuristic*: People let their positive or negative emotional

response or “affect” to words, people, and events influence their

decision making process regardless of objective evidence.

– For instance Death Tax vs. Estate Tax, Taxes vs. User fees.

– Health campaigns that rely on “fear appeals.”

Halo effect is the tendency to make broad positive judgments

about a person’s unobserved characteristics based on positive

first impressions.

– Viewing someone who is attractive as likely to be successful, smart, and

popular.

– If a CEO is charismatic, good-looking and good at making powerful first

impressions, an investor may be too attached to the company stock

even if the data doesn’t support it.

Affect Heuristic and Halo Effect

*Paul Slovic and others (2000) 33


>>

Consider this question:

“Tom is a graduate student at your state university. Please rank the

following nine fields of graduate studies in order of the likelihood that Tom is now a student in each of these fields (1 is most likely, 9 is least

likely): business administration, computer science, engineering,

humanities and education, law, medicine, library science, physical

and life sciences, social science and social work.”

– The answer depends on the proportion of graduate students enrolled in

the different fields, statistics that should be readily available from the

university.

– The proportion of students in a field is referred to as the base rate. Since

more students typically enroll in humanities and education than in

computer science, it is more likely that Tom is a humanities student than

a computer science student.


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The following is a personality sketch of Tom written during Tom’s senior year in high school by a psychologist, on the basis of psychological tests of uncertain validity:

“Tom is of high intelligence, although lacking in true creativity. He has a need for order and clarity, and for neat and tidy systems in which every detail finds its appropriate place. His writing is rather dull and mechanical, occasionally enlivened by somewhat corny puns and flashes of imagination of sci-fi type. He has a strong drive for competence. He seems to have little feeling and little sympathy for other people, and does not enjoy interacting with others. Self-centered, he nonetheless has a deep moral sense.”

Now, please rank the following nine fields of specialization (same as on the previous page) in order of the likelihood that Tom is now a graduate student in each of these fields.

Which field do you think most people pick as the most likely now for Tom?


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Most people pick Computer Science, ignoring base rates. This is base rate neglect. The choice of computer science is based on the representativeness heuristic.

Representativeness is the act of judging probabilities by similarity of the description to stereotypes, ignoring both the base rates and the doubts about the accuracy of the description.

Since Tom’s description sounds similar to that of a nerd, people wrongly conclude that he is likely a computer science student. What is needed is evidence that he took advanced classes in Math/Computer science in school. The description is not informative.

The correct way to evaluate the probability that Tom is a graduate student in any of those fields is to use Bayes’ Theorem(named after Reverend Thomas Bayes, an eighteenth century English mathematician and Presbyterian minister).


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Psychologist Paul Meehl (1954) concluded Statistical predictions (S system) were more accurate than the subjective Clinical predictionsof trained professionals (M system).

– Meehl reviewed 20 studies involving prediction of academic success of freshmen, psychiatric prognosis, criminal recidivism, success of naval trainees, parole outcomes etc.

Subsequent studies have shown that algorithms match or exceedthe accuracy of experts in environments involving a significant degree of uncertainty:

– Length of hospital stays, diagnosis of cardiac disease, susceptibility of babies to SID syndrome, evaluating new born infants (Apgar score), college admissions, prospects of new business success, credit risk evaluation by banks, winners of football games, future prices of Bordeaux wine, odds of recidivism among juvenile offenders, etc.

Algorithms are less biased and more consistent than experts.

Quantitative/rules-based investment strategies might be superior to subjective recommendations of investment experts.


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>>

Momentum Life Cycle Hypothesis (MLC)

From: Lee and Swaminathan (2000)

Quantitative strategies combine value and

momentum.

Lakonishok, Shleifer, and

Vishny (1994): value

strategies work due to

extrapolation bias.

Investors extrapolate past

performance too far into

the future.

Investors are overconfident

in their ability to predict the

future based on the past.

Glamour winners

Expensive with Positive Momentum

Winners

Glamour StocksLow B/M, E/P, C/P, High Trading Volume,

and High growth

Glamour losersExpensive with Negative Momentum

Losers

Value losersInexpensive withNegative Momentum

Value StocksHigh B/M, E/P, C/P, Low Trading Volume,

and Low growth

Value winners

Inexpensive withPositive Momentum

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An Intuitive Model of Investing


>> Be skeptical of over-optimistic forecasts and recommendations.

Be skeptical of your own forecasting skill, overconfidence lurks.

Engage in a pre-mortem to overcome overconfident over-optimism.

Beware of the recommendations of stock market experts. They suffer from extreme overconfidence and hindsight bias.

Beware of the anchoring effects when you are a buyer.

Beware of the extrapolation bias and be mindful of regression to the mean when evaluating past performance.

As an individual investor:

– Beware of excessive trading in the short-term and focus on the long-term.

– Winnie-the-Pooh: “Don’t underestimate the value of doing nothing.”

– Don’t invest in strategies or assets that you don’t understand.

– Focus on valuation, switch off the noise and keep a real-time investment diary to learn from mistakes.

Beware of the confirmation bias. Look for the “outside” view.

Simple algorithms/formulas/check lists can often trump subjective expert opinion.

Quantitative or rule-based investment strategies might represent a way to avoid these psychological traps.

Conclusions and Takeaways

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>> Answers to the CRT

1. 5 cents

2. 5 Minutes

3. 47 days

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Documents

Psychology of Investing Success LAPERS Conference Presentations/Swaminathan.pdfAgenda Efficient markets vs. behavioral finance Psychology of individual behavior Heuristics and cognitive