Henrik Cronqvist and Stephan Siegel
The Genetics of Investment Biases
Journal of Financial Economics | Volume 113, Issue 2 (Aug 2014), 215–234

Why do some investors have an internationally well-diversified portfolio while others invest solely in their home country? Are investment biases primarily learned from others or are they genetic? Does work experience in finance moderate the impact of genetic determinants? What about education?

In an effort to better understand why investors exhibit investment biases, and the degree to which they are genetic or can be influenced by external factors such as education and upbringing, Cronqvist-Siegel (JFE 2014) conduct a research study using data from the world's largest twin registry, the Swedish Twin Registry. Until 2007, taxpayers in Sweden were subject to a wealth tax. Prior to the abolishment of this tax, all Swedish banks, brokerage firms, and other financial institutions were required by law to report to the Swedish Tax Authority information about individuals' portfolios (i.e., stocks, bonds, mutual funds, derivatives, and other securities) held as of December 31 and also all sales transactions during the year. We matched 15,208 adult twin pairs from the Swedish Twin Registry with their portfolio and sales transaction data from the Tax Authority between 1999 and 2007, which gave us a detailed picture of their investment behavior.

Using empirical methodology adopted from quantitative behavioral genetics research (Neale-Maes (Kluwer 2004)), which has recently been used also in finance research, we match data from the twin registry with detailed data on the twins' investment behaviors and decomposed their differences into genetic and environmental components. We base our methodology on an intuitive insight: Identical twins share 100% of their genes, while the average proportion of shared genes is only 50% for fraternal twins. If identical twins exhibit more similarity with respect to investment biases than do fraternal twins, then this is evidence that these behaviors are influenced, at least in part, by genetic factors.

A long list of investment biases

We look at the following investment biases: Diversification, Home Bias (favoring home-country stocks), Turnover, Disposition Effect (reluctance to sell losers), Performance Chasing, and Skewness Preference (preferring `lottery' or more volatile stocks). We measure Diversification for direct stock holdings as the number of distinct stocks held in an individual's portfolio at the end of a given year. For holdings of stocks and mutual funds, we follow Calvet-Campbell-Sodini (AER 2009) and define diversification as the proportion of equity investments invested in mutual funds as opposed to individual stocks. To reduce measurement error, we calculate the equally weighted average diversification across all years the individual is in the data set.

We measure Home Bias by the average proportion invested in Swedish securities. We measure Turnover, i.e. an individual's propensity to trade and turnover the portfolio, following Barber-Odean (JF 2000, QJE 2001). Specifically, for direct stock holdings, we divide, for each individual investor and year, the sales volume (in Swedish krona) during the year by the value of directly held stocks at the beginning of the year. Since we do not have sales prices for mutual funds, we also construct a Turnover measure using the number of sales transactions during the year divided by the number of equity securities in the investor's portfolio at the beginning of the year. For each measure, we compute the average turnover using all years with available data. We measure the Disposition Effect in the spirit of Odean (JF 1998) and Dhar-Zhu (MS 2006). Specifically, at the end of each year during which we observe a sales transaction, we classify securities in an investor's portfolio as winners or losers based on the security's price relative to the approximate price at which the investor acquired the security. Using data across all years with sales transactions, we calculate for each investor the proportion of gains realized to the total number of realized and unrealized gains (PGR) as well as the proportion of losses realized to total losses (PLR). The larger the difference between PGR and PLR, the more reluctant a given investor is to realize losses.

We measure Performance Chasing by an individual's propensity to purchase securities that have performed well in the recent past. More specifically, each year we sort stocks and equity mutual funds separately into return deciles using the returns during the year. For each investor and year with net increases in holdings of stocks or mutual funds, we calculate the fraction of purchased securities with returns in the top two deciles. The higher that fraction, the more the individual chases performance by overweighting securities with higher recent performance.

We measure an individual's Skewness Preference as in Kumar (JF 2009). For each investor and year we calculate the proportion of the portfolio that is invested in “lottery” securities, ie, securities with a below median price as well as above median idiosyncratic volatility and above median skewness. Skewness preference is the fraction of lottery security securities averaged over all years with portfolio data.

1: Correlations of Investment Biases by Genetic Similarity
cronqvist-siegel
This figure contrasts correlations between identical twins with those between fraternal twins for several investment behaviors. Interpretation: Identical twins behave in more similar ways compared to fraternal twins.

Genetic differences explain up to 45% of the variation in investment biases across investors

Figure 1 illustrates one of the key findings of the study and shows that twins in an identical pair display much more similar investment biases compared to twins in a fraternal pair. This finding suggests that investment biases are partly genetic.

The importance of environmental influences

While our results are consistent with several behavioral genetic studies that have shown significant heritability of human behavior, they provide the first direct evidence from real-world, non-experimental data that persistent investment biases are to a significant extent, determined by genetic factors. Our results also show that even genetically identical investors who grew up in the same family environment can differ substantially in terms of their investment behaviors. Therefore, individual-specific environments, experiences, or events also play an important role in shaping individuals' investment behaviors. Our findings suggest that at least 55% of the unexplained variation in investment behaviors is due to environmental factors.

Among the environmental factors we explore are work experience in finance and education. We would like to know whether work experience in, for example, a bank or a corporate treasury department might reduce the influence of genetic tendencies towards investment biases. We were able to identify twins with finance-related work experience, and when we analyze their portfolio data we find that the relative importance of genetic factors on their investment biases is substantially smaller.

Education is another potentially important moderator of genetic effects. Here, we find that an increase in general education seems to have little effect on reducing genetic tendencies towards investment biases.

What explains the genetic effects we find and what are the implications of our findings?

Our evidence is consistent with evolutionary arguments of behavior as in Brennan-Lo (QJF 2011). Nature selects fitness maximizing behaviors, i.e., behaviors associated with a reproductive advantage relative to alternative behaviors. What in finance research is referred to as “biases” may indeed be manifestations of fitness maximizing psychological mechanisms. Consistent with this view of investment biases as partly innate features of human behavior, we find that the genetic factors that influence investment biases also affect behaviors in other, non-investment, domains.

Indeed, recent research in behavioral genetics has related specific genes to several of the psychological mechanisms that may manifest themselves as investment biases. That is, some individuals are endowed with genes related to familiarity (e.g., Chew-Ebstein-Zhong, JRU 2011), overconfidence (e.g., Cesarini-Johannesson-Lichtenstein-Wallace [JEEA 2009], or sensation-seeking (e.g., Derringer-etal [PS 2010]), and these genes may manifest themselves in the individual's investment behavior as well as the individual's behavior in other, non-investment domains.

An additional explanation for some of our results, which is consistent with recent work in finance (e.g., Grinblatt-Keloharju-Linnainmaa, JF 2011, JFE 2012), is that variation in IQ is genetic which results in genetic variation in investment biases.

Finally, our results have implications for the design of public policy in the domain of financial literacy. Specifically, the evidence suggests that policy should be designed accounting for the existence of genetic predispositions to investment biases and considering the challenges in reducing such biases.


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