Nature, Nurture, and Financial Decision-Making

Why Do Individuals Exhibit Investment Biases?

University of Michigan-Dearborn

Betty F. Elliott Initiative for Academic Excellence:

Financial Literacy

April 9, 2012

Henrik Cronqvist Claremont McKenna College

Stephan SiegelUniversity of WashingtonArizona State University

The Origins and Remediation of Human Inequality (James Heckman)

This research is rooted in economics but goes well outside of traditional analyses to integrate research in psychology, demography, neuroscience and biology.

A complete theory of human behavior (Andy Lo (2010))

Can we develop a complete theory of human behavior that is predictive in all contexts?

Effectiveness of policy initiatives (Doug Bernheim (2009))

“The discovery of a patience gene could shed light on the extent to which correlations between the wealth of parents and their children reflect predispositions rather than environmental factors that are presumably more amenable to policy intervention”

“Economics is a branch of biology broadly interpreted.”

Alfred Marshall (1920)

Nature, Nurture, and Financial Decision-Making

Genoeconomics*

• Study the sources of variation in economic behaviors and outcomes

• Understand how institutions or environments moderate or amplify genetic differences

• Education lowers genetic variation of health outcomes• Teacher quality increases genetic variation of reading skills

• Identify specific genes that predict behaviors/outcomes. Design interventions for those at “genetic” risk

• Reduce omitted variable bias by including genetic markers

* Benjamin, Chabris, Glaeser, Gudnason, Harris, Laibson, Launer, and Purcell (2007)

Research Methods

• Twin and Adoption Studies

• Molecular Genetics Studies

• Candidate gene studies

• Genome-wide association studies (GWAS)

• Rotterdam Study• Framingham Heart Study• AGES/Reykjavik Study

• Behrman and Taubman (1976)• Ashenfelter and Krueger (1994)• Sacerdote (2002)• Bjoerklund et al. (2006)• Cesarini et al. (2009, 2010)

• Kuhnen and Chiao (2009)• Dreber et al. (2009)

• Beauchamp et al. (2011)• Glaeser, Laibson, et al.

(ongoing)

Sample Studies

Our Contributions, so far . . .

Large Scale Twin Studies using Swedish Data (1999 – 2007)

• Risk Aversion and Financial Risk Taking(Barnea, Cronqvist, and Siegel (2010))

• Discount Rates, Impatience, and Wealth Accumulation(Cronqvist and Siegel (2011))

• Preferences over Homeownership and Home Location(Cronqvist, Muenkel, and Siegel (2012))

Investment Biases

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

Benjamin Graham

Investment BiasesLong list of investment behaviors that cannot be explained by standard preferences and belief formation

Underdiversify Prefer local securities Avoid realizing losses Chase past performance Trade a lot Prefer lottery-type stocks

Behaviors have been shown to be: Wide-spread, even present among professional traders/investors Potentially costly Generally linked to fundamental psychology construct

But, degrees vary across investors

Born with biases Preferences and belief formation as outcome of natural selection

Jack Hirshleifer (1977), Becker (1976) Robson (1996, 2001), Netzer (2009), Robson and Samuelson (2009)

Nature selects behaviors that maximize fitness Depending on environment, biases can emerge

Loss Aversion: McDermott, Fowler, Smirnov (2008) [in biology: e.g. Caraco (1980)] Over-confidence: Johnson and Fowler (2011) Probability Matching: Brennan and Lo (2009)

Environmental conditions Parenting Information and education Institutions Incentives

Why Do Individuals Exhibit Investment Biases?

Quantify the importance of different sources

Models of natural selection require some genetic variation

Understand whether education, experience, or incentives affect the importance of different sources In particular, what conditions moderate genetic predispositions

Improve policy design

Invest in gene and genome wide association studies

Objectives

Existing Evidence

Capuchin monkeys exhibit loss aversion Capuchin monkeys prefer gambles with good outcomes framed as bonuses over

identical pay-off gambles with bad outcomes framed as losses Loss aversion is part of decision-making process that evolved before humans and

capuchins separated (Chen et al. (2006), Lakshminarayanan et al. (2011))

Experimental and survey evidenceTwin Studies Overconfidence: Cesarini et al. (2009) Conflicting evidence for several biases: Cesarini et al. (2011) and Simonson and Sela

(2011)Gene Association Studies Different genes associated with risk attitudes over gains and losses (Zhong et al. (2010)) Neuro-scientific Studies Different brain activity for realized vs. unrealized gains (Frydman, Barberis, Camerer,

Bossaerts, and Rangel (2011))

No empirical evidence based on “real world” financial decisions

Mary Kate and AshleyOlsonElin and Josefin

Nordegren

Our Research Methodology

Identical TwinsFraternal Twins

Intuition of Methodology

Use identical & fraternal twins to decompose variation:

Identical twins have 100% of their genes in common

Fraternal twins on average have 50% of their genes in common

Twins who grew up in same family have a common environment

Each twin has his or her individual-specific environment

If genes matter, then identical twins should be more similar than fraternal twins in terms of their behavior.

Methodology Random effect model with genetic effect a, common effect

c and individual-specific effect e:

Covariance structure implied by genetic theory: MZ

DZ

Methodology, cont’d Estimate parameters σ2

a, σ2c, and σ2

e via maximum likelihood estimation (MLE) with bootstrapped standard errors

Derive the variance components:2

2 2 2a

a c

2

2 2 2c

a c

2

2 2 2a c

A-share – genetic component:

C-share – common environment(parenting):

E-share – individual environment & measurement error:

Data Twins from the Swedish Twin Registry.

Matched with annual financial data (including holdings of assets and sales transactions) and socioeconomic data from Statistics Sweden (1999 – 2007, no transactions in 2001/02)

Require: At least 18 years old Both twins hold some equities (directly or indirectly) in one year Average all variables over the years that individual is in sample

All Twins

Male Female Total

Same Sex: Male

Same Sex:

FemaleOpposite

Sex Total

Number of twins (N ) 30,416 4,066 5,206 9,272 4,522 5,326 11,296 21,144

Fraction (%) 100% 13% 17% 30% 15% 18% 37% 70%

Identical Twins Fraternal Twins

Socioeconomic Characteristics and Equity Portfolio Characteristics D E T A I L

All TwinsVariable N Mean Median Std. Dev. Mean Median Std. Dev.

30,416 47.08 48.00 17.64 53.06 55.00 15.5130,416 0.15 0.00 0.35 0.20 0.00 0.4030,416 0.22 0.00 0.41 0.26 0.00 0.4430,416 0.58 1.00 0.49 0.47 0.00 0.5030,416 0.06 0.00 0.23 0.06 0.00 0.2417,395 11.22 11.00 3.26 11.11 11.00 3.2930,416 0.46 0.00 0.50 0.54 1.00 0.5030,416 31,379 25,476 27,592 35,203 27,678 35,44930,416 40,759 14,537 155,296 48,062 17,342 442,29830,416 124,351 71,883 252,478 142,603 83,504 576,19830,416 31,802 16,020 68,330 30,396 13,759 149,77830,416 92,549 42,961 223,277 112,207 56,417 516,66530,416 3.56 2.33 3.80 3.62 2.25 3.9730,416 16,841 3,662 109,292 24,815 4,159 663,77312,378 3.32 1.89 3.91 3.42 1.89 4.1512,378 22,558 2,825 163,360 29,218 2,819 543,59623,870 2.41 1.89 1.84 2.34 1.80 1.8623,870 7,018 2,059 20,160 7,788 2,292 17,304

Value of Stocks and Equity Mutual Number of StocksValue of Stocks (USD)Number of Equity Mutual FundsValue of Equity Mutual Funds (USD)

Financial Assets (USD)Total Assets (USD)

Number of Stocks and Equity Mutual

No Education Data availableYears of EducationMarried

Less than High School High SchoolCollege or more

Total Debt (USD)

Age

Identical Twins Fraternal Twins

Net Worth (USD)

Disposable Income (USD)

Measuring Investment Biases

Home Bias Proportion of equity portfolio held in Swedish equity

Disposition Effect Conceptually: PGR – PLR (Odean (1998), Dhar and Zhu (2006), Campbell et al. (2009)) Based raw return in years with at least one sales transaction

Turnover Annual sales volume (SEK) divided by value of portfolio at beginning of year.

Performance Chasing Fractions of stocks acquired with raw returns in top two deciles

Skewness Preference Fraction of lottery stocks in portfolio (Kumar (2009))

Investment Biases: Summary Statistics

All TwinsN Mean Median Std. Dev. Mean Median Std. Dev.

Stocks Home Bias 12,378 0.94 1.00 0.16 0.94 1.00 0.15Turnover 11,508 0.20 0.03 0.35 0.17 0.02 0.33Disposition Effect 2,268 0.05 0.03 0.41 0.07 0.03 0.41Performance Chasing 6,672 0.15 0.00 0.22 0.14 0.00 0.22Skewness Preference 12,378 0.04 0.00 0.10 0.03 0.00 0.10

Identical Twins Fraternal Twins

Evidence from Correlations (Stocks)

-0.15

-0.05

0.05

0.15

0.25

0.35

0.45

0.55

HomeBias

Turnover Disposition Effect

PerformanceChasing

Skewness Preference

Figure 1Correlations by Genetic Similarity

Identical Twins Fraternal Twins Fraternal Twins - Same SexFraternal Twins - Opposite Sex Random Match

Variance Decomposition: StocksHome Bias Turnover

Disposition Effect

Performance Chasing

Skewness Preference

Intercept 0.955 0.134 0.132 2.313 0.004Male 0.004 0.062 -0.007 0.062 0.008Age 0.004 0.031 0.011 0.092 0.015Age - squared 0.000 -0.005 0.000 -0.008 -0.002High School -0.001 0.000 -0.010 -0.117 0.001College or More -0.012 0.022 -0.032 -0.156 0.005No Education Data Available -0.026 0.037 -0.025 -0.002 0.010Married -0.001 -0.001 -0.054 -0.051 0.002Second Net Worth Quartile Indicator -0.001 -0.005 -0.056 0.122 0.003Third Net Worth Quartile Indicator 0.001 -0.011 -0.006 0.200 -0.002Highest Net Worth Quartile Indicator -0.010 -0.025 -0.007 0.294 -0.004Log of Disposable Income -0.001 -0.002 -0.004 0.117 0.000Number of Trades (Sales) 0.003Number of Holdings -0.003

A Share 0.453 0.257 0.297 0.311 0.281

0.052 0.029 0.077 0.090 0.051C Share 0.000 0.000 0.000 0.096 0.000

0.027 0.008 0.041 0.065 0.028E Share 0.547 0.743 0.703 0.593 0.719

0.037 0.027 0.052 0.038 0.034

R 2 0.010 0.014 0.020 0.009 0.000

N 12,378 11,508 2,268 6,672 12,378

Mutual Fund and Large Investors

Repeat analysis combining direct stockholdings and mutual fund investments

Results are essentially the same

Diversification measure (mutual fund / all risky financial assets) has A component of about 39%

Repeat analysis for investors that hold at least 20% of assets in risky financial assets

Genetic component increases by typically 10 to 20% points

Robustness Same sex twin only

Model Misspecification Allowing for negative variance components Nonlinear models

Communication between twins Identical twins communicate more with one another Financial decisions are influenced by communication (e.g. Shiller and

Pound (1998), Hong, Kubik, and Stein (2004)) Sort pairs into 10 communication intensity bins and randomly drop

identical/fraternal pairs until both types are equally often present per bin.

Estimate model across all 10 bins A component slightly reduced, but generally robust

Moderating Genetic Predisposition Evidence that experience, education, and wealth affect

investment biases and trading behavior (e.g. Vissing-Jorgensen (2003), Dhar and Zhu (2006), Calvet et al. (2009), Graham et al. (2009))

Environment can enhance or constrain genetic predisposition

Heritability of reading ability increases with quality of teacher (Taylor et al. (2010))

Education seems to reduce genetic variation of health status (Johnson et al. (2009))

Examine (for a sub sample) how years of education interact with different sources of variation

We find no significant effect of years of education on size of genetic variance

G x E Interaction in Presence of G x E Correlation

Bias/BehaviorYears of Education

AM CM EM AU CU EU

M

B

aM cM eM

ac+ ac M

ec+ ec Mcc+ cc M aU+ aU M

eU+ eU M

cU+ cU M

Figure 2 Gene-Environment Interaction

Moderator: Years of EducationHome Bias

Loss Aversion Performance Chasing

Turnover

0

0.005

0.01

0.015

0.02

0.025

0.03

8 10 12 14 16

Var(A)

Var(C)

Var(E)

0

0.02

0.04

0.06

0.08

0.1

0.12

8 10 12 14 16

Var(A)

Var(C)

Var(E)

0

0.05

0.1

0.15

0.2

0.25

8 10 12 14 16

Var(A)

Var(C)

Var(E)

0

0.01

0.02

0.03

0.04

0.05

0.06

8 10 12 14 16

Var(A)

Var(C)

Var(E)

Twins with Financial Experience in their Jobs

Model N A - Share C - Share E - Share

Diversification 622 0.000 0.222 0.7780.104 0.090 0.069

Home Bias 622 0.000 0.206 0.7940.088 0.082 0.073

Turnover 582 0.000 0.110 0.8900.106 0.067 0.088

Performance Chasing 562 0.026 0.106 0.8680.102 0.068 0.078

Skewness Preference 622 0.187 0.000 0.8130.091 0.042 0.079

Variance Components

Behavior across different domains is often correlated If genetic factors matter, source of the correlation should be genetic Correlate Home Bias with

Distance to birthplace Indicator whether spouse is from same home state

Sources of Behavioral Consistency

Home Bias

Distance to Birthplace

Home Bias

Spouse from Home Region

A - Share 0.455 0.400 0.364 0.1460.059 0.085 0.116 0.092

C - Share 0.000 0.210 0.000 0.1920.039 0.061 0.066 0.067

E - Share 0.545 0.389 0.636 0.6620.031 0.036 0.081 0.041

Model I Model II

Behavior across different domains is often correlated If genetic factors matter, source of the correlation should be genetic Correlate Home Bias with

Distance to birthplace Indicator whether spouse is from same home state

Sources of Behavioral Consistency

Home Bias

Distance to Birthplace

Home Bias

Spouse from Home Region

Correlation

Genetic Correlation

Correlation of Common Environment

Correlation of Individual Environment

N

-0.069

-0.0310.009

Model I Model II

0.031

0.000 0.000

2,56612,180

0.0100.022

0.2400.239

0.0350.021

-0.1060.036

Conclusions Why do investor exhibit investment biases? We show that to a

large existent biases, such as Home Bias, Disposition Effect, Turnover, Performance Chasing, as well as Skewness Preference are innate

Our findings are consistent with recent theoretical models that argue that biases are the outcome of natural selection

While genetic effects are important, they are not destiny:

A large part of the cross-sectional variation appears to be related to individual experiences and circumstances

General educational achievement does not seem to moderate genetic predispositions

For twins with occupational experience in finance genetic factors seem to matter less

END