Nature, Nurture, and Financial Decision-Making Why Do Individuals Exhibit Investment Biases?

1. Nature, Nurture, and Financial Decision-MakingWhy 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 Siegel University of Washington Arizona State University

2. Nature, Nurture, and Financial Decision-Making “Economics is a branch of biology broadly interpreted.” • Alfred Marshall (1920) • 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”

3. 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)

4. Research Methods Sample Studies • Twin and Adoption Studies • Behrman and Taubman (1976) • Ashenfelter and Krueger (1994) • Sacerdote (2002) • Bjoerklundet al. (2006) • Cesarini et al. (2009, 2010) • Molecular Genetics Studies • Candidate gene studies • Genome-wide association studies (GWAS) • Rotterdam Study • Framingham Heart Study • AGES/Reykjavik Study • Kuhnen and Chiao (2009) • Dreber et al. (2009) • Beauchamp et al. (2011) • Glaeser, Laibson, et al. (ongoing)

5. 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))

6. Investment Biases The investor’s chief problem and even his worst enemy is likely to be himself. Benjamin Graham

7. 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

8. 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?

9. 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

10. 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 evidence Twin 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

11. Our Research Methodology Identical Twins Fraternal Twins Mary Kate and Ashley Olson Elin and Josefin Nordegren

12. 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.

13. MZ DZ Methodology • Random effect model with genetic effecta, common effectc and individual-specific effecte: • Covariance structure implied by genetic theory:

14. Methodology, cont’d Estimate parameters σ2a, σ2c, and σ2e via maximum likelihood estimation (MLE) with bootstrapped standard errors Derive the variance components: A-share – genetic component: C-share – common environment (parenting): E-share – individual environment & measurement error:

15. 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

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

17. 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))

18. Investment Biases: Summary Statistics

19. Evidence from Correlations (Stocks)

20. Variance Decomposition: Stocks

21. 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

22. 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

23. 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

24. G x E Interaction in Presence of G x E Correlation Years of Education Bias/Behavior

25. Moderator: Years of Education Turnover Home Bias Loss Aversion Performance Chasing

26. Twins with Financial Experience in their Jobs

27. Sources of Behavioral Consistency • 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

28. Sources of Behavioral Consistency • 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

29. 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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