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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 Biases
Long 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 σ2a, σ2
c, and σ2e 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 0.051
C Share 0.000 0.000 0.000 0.096 0.0000.027 0.008 0.041 0.065 0.028
E Share 0.547 0.743 0.703 0.593 0.7190.037 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
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