As the behavioral geneticists like to say, everything is heritable: "The genetics of investment biases", Cronqvist & Siegal 2014 (via http://marginalrevolution.com/marginalrevolution/2014/07/the-genetics-of-investment-biases.html ); excerpts:
"For a long list of investment "biases", including lack of diversification, excessive trading, and the disposition effect, we find that genetic differences explain up to 45% of the remaining variation across individual investors, after controlling for observable individual characteristics. The evidence is consistent with a view that investment biases are manifestations of innate and evolutionary ancient features of human behavior. We find that work experience with finance reduces genetic predispositions to investment biases. Finally, we find that even genetically identical investors, who grew up in the same family environment, often differ substantially in their investment behaviors due to individual-specific experiences or events.
These behaviors have been partially attributed to various psychological mechanisms: Ambiguity aversion and familiarity for lack of diversification (Ellsberg, 1961; Heath and Tversky, 1991), overconfidence and sensation-seeking for excessive trading (Griffin and Tversky, 1992), loss aversion and mental accounting for the reluctance to realize losses (Kahneman and Tversky, 1979; Thaler, 1985), representativeness and the hot hands fallacy for excessive extrapolation of past returns (Tversky and Kahneman, 1974), and cumulative prospect theory for skewness preferences (Tversky and Kahneman, 1992). 1 While the referenced studies have shown that individual investors, on average, exhibit these investment biases, little research has been devoted to uncovering the origins of these investment biases and the differences across investors. Are investors genetically endowed with certain predispositions that manifest themselves as investment biases? Or do investors exhibit biases as a result of parenting or individual-specific experiences or events?
Our data set from the world's largest twin registry, the Swedish Twin Registry (STR), matched with detailed data on the twins' investment behaviors, enables us to decompose differences across individuals into genetic versus environmental components.
We can summarize our results as follows. First, for a long list of investment biases, we find that genetic differences explain up to 45% of the remaining variation across individual investors, after controlling for observable individual characteristics. Consistent with a view that investment biases are manifestations of innate and evolutionary ancient features of human behavior, we find that the genetic factors that influence investment biases also affect behaviors in other, non-investment, domains. For example, we show that the correlation between a preference for familiar stocks and familiarity preferences in other domains is due to shared genetic 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 endowments. Such evidence provides support for evolutionary arguments that behaviors which manifest themselves as investment biases in today's financial markets have survived because they were advantageous in evolutionary ancient times (e.g., Rayo and Becker, 2007; Brennan and Lo, 2011).
The relative importance of genetic relative to environmental factors is found to vary across different investors. Most importantly, among investors with work experience with finance, we find a significant reduction of the relative amount of genetic variation, which is consistent with practical experience in finance moderating genetic predispositions. We cannot rule out, though, that the selection of profession reduces the relevant genetic variation in this subsample. Controlling for selection, we also investigate the role of general education, measured as years of education, in moderating the relative importance of genetic factors. We do not find that general education reduces the relative importance of genetic factors in explaining investment biases.
Finally, we find that even genetically identical investors who grew up in the same family environment differ substantially in terms of their investment behaviors. Individual-specific environments, experiences, or events must therefore play an important role in shaping individuals' investment behaviors. Examining differences between investment biases of genetically identical investors, we show how genetically informed data, such as twin data used in this study, can be used to better establish the causal impact of individual-specific factors, such as education.
Zygosity is based on questions about intra-pair similarities in childhood. One of the questions was: Were you and your twin partner during childhood "as alike as two peas in a pod" or were you "no more alike than siblings in general" with regard to appearance? STR has validated this method with DNA analysis as having 98% accuracy on a subsample of twins. For twin pairs for which DNA has been collected, zygosity status is based on DNA analysis.
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 have matched the twins with portfolio and sales transaction data between 1999 and 2007, providing us with detailed information on investment behavior. For each individual, our data set contains all securities held at the end of the year (identified by each security's International Security Identification Number (ISIN)), the number of each security held, the dividends received during the year, and the end of the year value. We also have data on which securities were sold over the year, and in the case of stocks, the number of securities sold and the sales price. 7 Security-level data have been collected from several sources, including Bloomberg, Datastream, Morningstar, SIX Telekurs, Standard & Poor's, and the Swedish Investment Fund Association.
We have 15,208 adult twin pairs in which each twin has at least one year of non-missing equity investment data. Panel A of Table 2 reports summary statistics for our data set, which includes 30,416 individuals. Opposite-sex twins are the most common (37%); identical male twins are the least common (13%). The distribution in the table is consistent with what would be expected from large samples of twins (e.g., Bortolus, Parazzini, Chatenoud, Benzi, Bianchi, and Marini, 1999).
For direct stock holdings, we measure Diversification 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, and Sodini (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. Summary statistics in Table 3 show that the average investor with direct holdings of stocks holds about three stocks, while across all investors about 70% of their equity portfolio is invested in mutual funds. The standard deviations of about 4% for the number of stocks and about 40% for the proportion invested in mutual funds are evidence of substantial cross-sectional variation.
We measure Home bias by the average proportion invested in Swedish securities. Table 3 shows that for individual stocks the average home bias is 94%, but drops to about 50% once we include mutual fund investments. Cross-sectional variation exists again in both cases, with standard deviations of about 15% in case of individual stocks and 30% in case of all equity investments. We measure Turnover, i.e., an individual's propensity to trade and turnover the portfolio, following Barber and Odean (2000, 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. Table 3 reports that for the average investor in our data who holds individual stocks, annual (sales) turnover is about 20%, a magnitude similar to that reported by Agnew, Balduzzi, and Sundén (2003) for a large set of retirement savings accounts in the U.S., and Grinblatt and Keloharju (2009) for a large sample of individual investors in Finland. Even though many investors in our data trade relatively little, substantial variation exists, as indicated by the crosssectional standard deviation of about 33%. Some of the investors in our data set therefore likely trade too much, as in, e.g., Odean (1999). That is, they trade more than what is needed to rebalance their portfolios or to satisfy liquidity needs. To control for cross-sectional variation in such reasons to trade, we follow Grinblatt and Keloharju (2009) and control for socioeconomic characteristics that may correlate with rebalancing needs and liquidity demands. The remaining variation may then be considered variation in "excessive" trading.
We measure the Disposition effect in the spirit of Odean (1998) and Dhar and Zhu (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. 10 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.
Table 3 reveals that we are able to calculate the Disposition effect only for a subset of investors. The reduction in sample size is due to missing information on purchase prices for securities that are present in an investor's portfolio before 1999, the first year of our sample period, as well as infrequent trading by some investors. The average investor exhibits a disposition effect of about 4% with respect to direct equity holdings and of about 2% when including mutual funds. Most importantly, given that the PGR À PLR difference is bounded by À 1 and þ1, the standard deviation of about 40% shows that there is significant variation across individuals with respect to the reluctance 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. Performance chasing is the average fraction over all years with net acquisitions of equity securities. Table 3 shows, on average, about 10-15% of the securities acquired have shown relatively strong recent performance. Since not all investors make net acquisitions during our sample period, Performance chasing is only available for a subset of investors.
We measure an individual's Skewness preference as in Kumar (2009). For each investor and year we calculate the proportion of the portfolio that is invested in "lottery" securities, i.e., securities with a below median price as well as above median idiosyncratic volatility and above median skewness. Skewness preference is the fraction of lottery securities averaged over all years with portfolio data. Table 3 shows that, on average, about 3-4% of an investor's portfolio is held in lottery securities. To reduce the dimensionality of some of our analysis, we also construct an index that summarizes the above investment behaviors for each investor with holdings of individual stocks. Specifically, for each of the investment behaviors, we assign a value of zero (no bias), one, or two (most biased), depending on the observed level. For example, for the Disposition effect, we assign two to investors with a disposition effect over 40% (one standard deviation above zero), one to investors with a strictly positive disposition effect, and zero otherwise. Appendix Table A1 provides a detailed description of the construction of the Investment bias index. If for a given investor, a behavior is missing, we use the median behavior to assign the bias index component (zero, one, or two). An individual's Investment bias index is the sum across all the investment behaviors and takes on values between zero and 12.
For each investment behavior defined previously, Fig. 1 reports correlations between identical twins as well as same- and opposite-sex fraternal twins. We draw several conclusions from the evidence. First, for each measure, we find that the correlation is significantly greater between identical relative to fraternal twins. This difference indicates that to some extent, investors display more or less of a given investment bias due to their genetic make-up. On average, the correlation between identical twins is about twice the correlation between fraternal twins. Second, the correlations for same-sex fraternal twins is generally larger than those for opposite-sex twins. This result suggests that gender affects investment behaviors. In our empirical models below, we will therefore control for gender. In addition, we will provide a robustness check that excludes opposite-sex twins. Finally, we note that the correlation for identical twins is between 25% and 50%, i.e., significantly different from one, suggesting that individual-specific experiences and events are also important for the understanding of why investors exhibit investment biases.
Genetic factors seem to be particularly influential in determining Diversification and Home bias, where they account for around 45% of the unexplained variation. For the remaining behaviors, genetic variation still accounts for between a quarter and a third of the variation. That is, individuals are to a significant extent born with predispositions that later in life and under the conditions typically experienced by an investor in our data set manifest themselves in the investment biases we examine in this paper. The findings also suggest that at least 55% of the unexplained variation in investment behaviors is due to environmental factors, represented by the C [common or shared environment] and E [individual-specific environmental effects / nonshared environment] components.
...The C component is insignificant suggesting that upbringing or other aspects of the common environment do not affect investment biases. That is, the notion that children learn investment biases from their parents is inconsistent with the data. 12
5.3.3. Model assumptions
Equal Environments Assumption (EEA): If parents or others in an individual's environment treat identical twins more similarly than fraternal twins (along dimensions that are relevant for the investment behaviors we study), then A may be upward biased. This is a well-recognized problem in twin research, and substantial resources have been devoted to tests of the EEA. 15 From research on IQ and personality, where the EEA has to date been tested most rigorously, the evidence suggests that any bias from violations of the EEA is not of first-order importance (e.g., Bouchard, 1998). Specifically, researchers have studied twins reared apart, i.e., twins separated at birth or early in life, for which there is no common parental environment. Such studies often produce heritability estimates similar to those using twins who were reared together (e.g., Bouchard, Lykken, McGue, Segal, and Tellegen, 1990). Perhaps even more convincingly, recent progress in genotyping has enabled researchers to construct DNA-based measures of pairwise genetic relatedness, which were then related to different outcomes, e.g., IQ (Jian, Benyamin, McEvoy, Gordon, Henders, Nyholt, Madden, Heath, Martin, Montgomery, Goddard, and Visscher, 2010; Davies, Tenesa, Payton, Yang, Harris, Liewald, Ke, Le Hellard, Christoforou, Luciano, McGhee, Lopez, Gow, Corley, Redmond, Fox, Haggarty, Whalley, McNeill, and Goddard, 2011). Differently from twin studies, these studies use unrelated subjects and show without relying on any assumptions such as the EEA that at least 50% of the variation in the studied outcomes is due to genetic variation. At the same time, twin researchers continue to test the EEA. One concern has been that the matched physical appearance of identical twins results in more similar treatment by those who are a part of these individuals' environments, in the end causing more similar outcomes. Using a novel research design, Segal (2013) studies unrelated look-alike individuals, and finds that their correlations for personality measures are much lower than for identical twins, suggesting that identical twins' similarity with respect to personality mostly reflects similarity in their genes, and not similar treatments by others. Finally, a concern relevant for this paper is that inheritances that twins receive from their parents might be more similar (for example, with respect to the asset composition) for identical than fraternal twins. Since by law, children in Sweden inherit any assets only after the death of the last parent, we perform a robustness check by excluding twins for whom both parents are deceased from the samples used in Table 5. Re-estimating all six models, we find estimates (not tabulated) that are very similar to those reported in Table 5. We conclude that inheritances do not seem to confound our results.
We report several results. First, variation in familiarity in other, non-investment domains reflects significant genetic differences: 40% for home location and 15% for choice of spouse. Second, Home bias and Distance to birthplace are significantly negatively correlated, suggesting that those with relatively more local stocks also have a stronger preference for a home location close to their birth place. Finally, and most importantly, the significantly negative genetic correlation between both behaviors suggests that the genetic factors affecting Home bias also affect Distance to birthplace. While we do not find an overall correlation between Home bias and Spouse from home region, we find a large, though statistically not significant, positive genetic correlation between both behaviors.
These results have implications for the design of public policy in the domain of financial literacy (e.g., Lusardi and Mitchell, 2007; Van Rooij, Lusardi, and Alessie, 2011). 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. Some contemporaneous research has reached similar conclusions. For example, Bhattacharya, Hackethal, Kaesler, Loos, and Meyer (2012) show in a large field study that investors who are offered unbiased investment advice often are not interested in the advice and even those who are interested generally do not follow the advice."
#genetics #psychology #investing
"For a long list of investment "biases", including lack of diversification, excessive trading, and the disposition effect, we find that genetic differences explain up to 45% of the remaining variation across individual investors, after controlling for observable individual characteristics. The evidence is consistent with a view that investment biases are manifestations of innate and evolutionary ancient features of human behavior. We find that work experience with finance reduces genetic predispositions to investment biases. Finally, we find that even genetically identical investors, who grew up in the same family environment, often differ substantially in their investment behaviors due to individual-specific experiences or events.
These behaviors have been partially attributed to various psychological mechanisms: Ambiguity aversion and familiarity for lack of diversification (Ellsberg, 1961; Heath and Tversky, 1991), overconfidence and sensation-seeking for excessive trading (Griffin and Tversky, 1992), loss aversion and mental accounting for the reluctance to realize losses (Kahneman and Tversky, 1979; Thaler, 1985), representativeness and the hot hands fallacy for excessive extrapolation of past returns (Tversky and Kahneman, 1974), and cumulative prospect theory for skewness preferences (Tversky and Kahneman, 1992). 1 While the referenced studies have shown that individual investors, on average, exhibit these investment biases, little research has been devoted to uncovering the origins of these investment biases and the differences across investors. Are investors genetically endowed with certain predispositions that manifest themselves as investment biases? Or do investors exhibit biases as a result of parenting or individual-specific experiences or events?
Our data set from the world's largest twin registry, the Swedish Twin Registry (STR), matched with detailed data on the twins' investment behaviors, enables us to decompose differences across individuals into genetic versus environmental components.
We can summarize our results as follows. First, for a long list of investment biases, we find that genetic differences explain up to 45% of the remaining variation across individual investors, after controlling for observable individual characteristics. Consistent with a view that investment biases are manifestations of innate and evolutionary ancient features of human behavior, we find that the genetic factors that influence investment biases also affect behaviors in other, non-investment, domains. For example, we show that the correlation between a preference for familiar stocks and familiarity preferences in other domains is due to shared genetic 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 endowments. Such evidence provides support for evolutionary arguments that behaviors which manifest themselves as investment biases in today's financial markets have survived because they were advantageous in evolutionary ancient times (e.g., Rayo and Becker, 2007; Brennan and Lo, 2011).
The relative importance of genetic relative to environmental factors is found to vary across different investors. Most importantly, among investors with work experience with finance, we find a significant reduction of the relative amount of genetic variation, which is consistent with practical experience in finance moderating genetic predispositions. We cannot rule out, though, that the selection of profession reduces the relevant genetic variation in this subsample. Controlling for selection, we also investigate the role of general education, measured as years of education, in moderating the relative importance of genetic factors. We do not find that general education reduces the relative importance of genetic factors in explaining investment biases.
Finally, we find that even genetically identical investors who grew up in the same family environment differ substantially in terms of their investment behaviors. Individual-specific environments, experiences, or events must therefore play an important role in shaping individuals' investment behaviors. Examining differences between investment biases of genetically identical investors, we show how genetically informed data, such as twin data used in this study, can be used to better establish the causal impact of individual-specific factors, such as education.
Zygosity is based on questions about intra-pair similarities in childhood. One of the questions was: Were you and your twin partner during childhood "as alike as two peas in a pod" or were you "no more alike than siblings in general" with regard to appearance? STR has validated this method with DNA analysis as having 98% accuracy on a subsample of twins. For twin pairs for which DNA has been collected, zygosity status is based on DNA analysis.
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 have matched the twins with portfolio and sales transaction data between 1999 and 2007, providing us with detailed information on investment behavior. For each individual, our data set contains all securities held at the end of the year (identified by each security's International Security Identification Number (ISIN)), the number of each security held, the dividends received during the year, and the end of the year value. We also have data on which securities were sold over the year, and in the case of stocks, the number of securities sold and the sales price. 7 Security-level data have been collected from several sources, including Bloomberg, Datastream, Morningstar, SIX Telekurs, Standard & Poor's, and the Swedish Investment Fund Association.
We have 15,208 adult twin pairs in which each twin has at least one year of non-missing equity investment data. Panel A of Table 2 reports summary statistics for our data set, which includes 30,416 individuals. Opposite-sex twins are the most common (37%); identical male twins are the least common (13%). The distribution in the table is consistent with what would be expected from large samples of twins (e.g., Bortolus, Parazzini, Chatenoud, Benzi, Bianchi, and Marini, 1999).
For direct stock holdings, we measure Diversification 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, and Sodini (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. Summary statistics in Table 3 show that the average investor with direct holdings of stocks holds about three stocks, while across all investors about 70% of their equity portfolio is invested in mutual funds. The standard deviations of about 4% for the number of stocks and about 40% for the proportion invested in mutual funds are evidence of substantial cross-sectional variation.
We measure Home bias by the average proportion invested in Swedish securities. Table 3 shows that for individual stocks the average home bias is 94%, but drops to about 50% once we include mutual fund investments. Cross-sectional variation exists again in both cases, with standard deviations of about 15% in case of individual stocks and 30% in case of all equity investments. We measure Turnover, i.e., an individual's propensity to trade and turnover the portfolio, following Barber and Odean (2000, 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. Table 3 reports that for the average investor in our data who holds individual stocks, annual (sales) turnover is about 20%, a magnitude similar to that reported by Agnew, Balduzzi, and Sundén (2003) for a large set of retirement savings accounts in the U.S., and Grinblatt and Keloharju (2009) for a large sample of individual investors in Finland. Even though many investors in our data trade relatively little, substantial variation exists, as indicated by the crosssectional standard deviation of about 33%. Some of the investors in our data set therefore likely trade too much, as in, e.g., Odean (1999). That is, they trade more than what is needed to rebalance their portfolios or to satisfy liquidity needs. To control for cross-sectional variation in such reasons to trade, we follow Grinblatt and Keloharju (2009) and control for socioeconomic characteristics that may correlate with rebalancing needs and liquidity demands. The remaining variation may then be considered variation in "excessive" trading.
We measure the Disposition effect in the spirit of Odean (1998) and Dhar and Zhu (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. 10 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.
Table 3 reveals that we are able to calculate the Disposition effect only for a subset of investors. The reduction in sample size is due to missing information on purchase prices for securities that are present in an investor's portfolio before 1999, the first year of our sample period, as well as infrequent trading by some investors. The average investor exhibits a disposition effect of about 4% with respect to direct equity holdings and of about 2% when including mutual funds. Most importantly, given that the PGR À PLR difference is bounded by À 1 and þ1, the standard deviation of about 40% shows that there is significant variation across individuals with respect to the reluctance 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. Performance chasing is the average fraction over all years with net acquisitions of equity securities. Table 3 shows, on average, about 10-15% of the securities acquired have shown relatively strong recent performance. Since not all investors make net acquisitions during our sample period, Performance chasing is only available for a subset of investors.
We measure an individual's Skewness preference as in Kumar (2009). For each investor and year we calculate the proportion of the portfolio that is invested in "lottery" securities, i.e., securities with a below median price as well as above median idiosyncratic volatility and above median skewness. Skewness preference is the fraction of lottery securities averaged over all years with portfolio data. Table 3 shows that, on average, about 3-4% of an investor's portfolio is held in lottery securities. To reduce the dimensionality of some of our analysis, we also construct an index that summarizes the above investment behaviors for each investor with holdings of individual stocks. Specifically, for each of the investment behaviors, we assign a value of zero (no bias), one, or two (most biased), depending on the observed level. For example, for the Disposition effect, we assign two to investors with a disposition effect over 40% (one standard deviation above zero), one to investors with a strictly positive disposition effect, and zero otherwise. Appendix Table A1 provides a detailed description of the construction of the Investment bias index. If for a given investor, a behavior is missing, we use the median behavior to assign the bias index component (zero, one, or two). An individual's Investment bias index is the sum across all the investment behaviors and takes on values between zero and 12.
For each investment behavior defined previously, Fig. 1 reports correlations between identical twins as well as same- and opposite-sex fraternal twins. We draw several conclusions from the evidence. First, for each measure, we find that the correlation is significantly greater between identical relative to fraternal twins. This difference indicates that to some extent, investors display more or less of a given investment bias due to their genetic make-up. On average, the correlation between identical twins is about twice the correlation between fraternal twins. Second, the correlations for same-sex fraternal twins is generally larger than those for opposite-sex twins. This result suggests that gender affects investment behaviors. In our empirical models below, we will therefore control for gender. In addition, we will provide a robustness check that excludes opposite-sex twins. Finally, we note that the correlation for identical twins is between 25% and 50%, i.e., significantly different from one, suggesting that individual-specific experiences and events are also important for the understanding of why investors exhibit investment biases.
Genetic factors seem to be particularly influential in determining Diversification and Home bias, where they account for around 45% of the unexplained variation. For the remaining behaviors, genetic variation still accounts for between a quarter and a third of the variation. That is, individuals are to a significant extent born with predispositions that later in life and under the conditions typically experienced by an investor in our data set manifest themselves in the investment biases we examine in this paper. The findings also suggest that at least 55% of the unexplained variation in investment behaviors is due to environmental factors, represented by the C [common or shared environment] and E [individual-specific environmental effects / nonshared environment] components.
...The C component is insignificant suggesting that upbringing or other aspects of the common environment do not affect investment biases. That is, the notion that children learn investment biases from their parents is inconsistent with the data. 12
5.3.3. Model assumptions
Equal Environments Assumption (EEA): If parents or others in an individual's environment treat identical twins more similarly than fraternal twins (along dimensions that are relevant for the investment behaviors we study), then A may be upward biased. This is a well-recognized problem in twin research, and substantial resources have been devoted to tests of the EEA. 15 From research on IQ and personality, where the EEA has to date been tested most rigorously, the evidence suggests that any bias from violations of the EEA is not of first-order importance (e.g., Bouchard, 1998). Specifically, researchers have studied twins reared apart, i.e., twins separated at birth or early in life, for which there is no common parental environment. Such studies often produce heritability estimates similar to those using twins who were reared together (e.g., Bouchard, Lykken, McGue, Segal, and Tellegen, 1990). Perhaps even more convincingly, recent progress in genotyping has enabled researchers to construct DNA-based measures of pairwise genetic relatedness, which were then related to different outcomes, e.g., IQ (Jian, Benyamin, McEvoy, Gordon, Henders, Nyholt, Madden, Heath, Martin, Montgomery, Goddard, and Visscher, 2010; Davies, Tenesa, Payton, Yang, Harris, Liewald, Ke, Le Hellard, Christoforou, Luciano, McGhee, Lopez, Gow, Corley, Redmond, Fox, Haggarty, Whalley, McNeill, and Goddard, 2011). Differently from twin studies, these studies use unrelated subjects and show without relying on any assumptions such as the EEA that at least 50% of the variation in the studied outcomes is due to genetic variation. At the same time, twin researchers continue to test the EEA. One concern has been that the matched physical appearance of identical twins results in more similar treatment by those who are a part of these individuals' environments, in the end causing more similar outcomes. Using a novel research design, Segal (2013) studies unrelated look-alike individuals, and finds that their correlations for personality measures are much lower than for identical twins, suggesting that identical twins' similarity with respect to personality mostly reflects similarity in their genes, and not similar treatments by others. Finally, a concern relevant for this paper is that inheritances that twins receive from their parents might be more similar (for example, with respect to the asset composition) for identical than fraternal twins. Since by law, children in Sweden inherit any assets only after the death of the last parent, we perform a robustness check by excluding twins for whom both parents are deceased from the samples used in Table 5. Re-estimating all six models, we find estimates (not tabulated) that are very similar to those reported in Table 5. We conclude that inheritances do not seem to confound our results.
We report several results. First, variation in familiarity in other, non-investment domains reflects significant genetic differences: 40% for home location and 15% for choice of spouse. Second, Home bias and Distance to birthplace are significantly negatively correlated, suggesting that those with relatively more local stocks also have a stronger preference for a home location close to their birth place. Finally, and most importantly, the significantly negative genetic correlation between both behaviors suggests that the genetic factors affecting Home bias also affect Distance to birthplace. While we do not find an overall correlation between Home bias and Spouse from home region, we find a large, though statistically not significant, positive genetic correlation between both behaviors.
These results have implications for the design of public policy in the domain of financial literacy (e.g., Lusardi and Mitchell, 2007; Van Rooij, Lusardi, and Alessie, 2011). 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. Some contemporaneous research has reached similar conclusions. For example, Bhattacharya, Hackethal, Kaesler, Loos, and Meyer (2012) show in a large field study that investors who are offered unbiased investment advice often are not interested in the advice and even those who are interested generally do not follow the advice."
#genetics #psychology #investing
Olá meu amigo que tem um amigoJul 4, 2014
