Value versus Growth Investing:

Why Do Different Investors Have Different Styles?∗

Henrik Cronqvist, Stephan Siegel, and Frank Yu†

This draft: December 14, 2013

Abstract

We find that several factors explain an investor’s style, in the sense of the value versus growth
orientation of the investor’s stock portfolio. First, an investor’s style has a biological basis –
a preference for value versus growth stocks is partially ingrained in an investor already from
birth. Second, investors who a priori are expected to take more financial risk (e.g., men and
wealthier individuals) have a preference for growth, not value, which may be surprising if the
value premium reflects risk. Finally, an investor’s style is explained by life course theory in that
experiences, both earlier and later in life, are related to investment style. Investors with ad-
verse macroeconomic experiences (e.g., growing up during the Great Depression or entering the
job market during an economic downturn) and those who grew up in a lower status socioeco-
nomic rearing environment have a stronger value orientation several decades later in their lives.
Our research contributes a new perspective to the long-standing value/growth debate in finance.

∗We are thankful for comments from seminar participants at Cheung Kong Graduate School of Business, China
Europe International Business School, Shanghai Advanced Institute of Finance, Tsinghua University (School of
Economics and Management), and for discussions with Colin Camerer, Henry Cao, David Hirshleifer, Samuli Knüpfer,
Augustin Landier, Feng Li, Juhani Linnainmaa, Jun Liu, Pedro Matos, Stefan Nagel, Jun Qian, Neil Pearson, Meir
Statman, Mathijs van Dijk, Tan Wang, and Lu Zhang. We thank S&P Capital IQ’s China and Hong Kong teams (in
particular Rick Chang) for help with various data issues. We also thank Wenqian Hu, Lucas Perin, and Nancy Yao
for capable research assistance. This project has been supported by generous research funding from China Europe
International Business School (CEIBS). Statistics Sweden and the Swedish Twin Registry (STR) provided the data
for this study. STR is supported by grants from the Swedish Research Council, the Ministry of Higher Education,
AstraZeneca, and the National Institute of Health (grants AG08724, DK066134, and CA085739). This project was
pursued in part when Cronqvist was Olof Stenhammar Visiting Professor at the Swedish Institute for Financial
Research (SIFR), which he thanks for its support. Any errors or omissions are our own.
†Cronqvist and Yu: China Europe International Business School (hcronqvist@ceibs.edu and fyu@ceibs.edu); Siegel:

University of Washington, Michael G. Foster School of Business (ss1110@uw.edu).



I think Warren [Buffett] captured the idea himself in his 1964 article “The Superinvestors of Graham
and Doddsville” and in it he talks about – value investing is like an inoculation – you either get it
right away, or you never get it. And I think it’s just true. I actually think there’s just a gene for
this stuff, whether it’s a value investing gene or a contrarian gene.

— Seth Klarman, in an interview with Charlie Rose, 2011.

I Introduction

The concepts of “value” and “growth” investing have a long history in financial economics. Today,

there exist some 2,050 value funds and 3,200 growth funds catering to investors with preferences for

these investment styles.1 For more than two decades, Morningstar has provided a Value-Growth

Score to help investors choose a fund with their preferred style. Fidelity, the world’s largest provider

of employer-sponsored retirement plans such as 401(k) plans, prominently features a description

of value and growth funds on their Learning Center website.2 There are best-selling books about

both value and growth strategies, and countless business magazine articles boast recommendations

about the “Best Value Funds” and/or the “Best Growth Funds.” Wall Street professionals are

educated about value and growth investing already in business school, with many MBA programs

today offering, e.g., Value Investing courses. Most importantly from the perspective of academic

research, one of the most debated issues in the past several decades is the differential returns of

investments in value versus growth stock portfolios – the value premium debate (e.g., De Bondt and

Thaler (1985), Fama and French (1992, 1993, 1996), Lakonishok, Shleifer, and Vishny (1994), and

Daniel and Titman (1997)).3

Despite all this attention to value and growth investing, very little research has attempted to

explain the determinants of an individual’s investment style. That is, why are some investors value

oriented, while others are growth oriented? In this paper, we argue that differences in investment

styles across individuals, in principle, may stem from either of two non-mutually exclusive sources.

1Morningstar.com.
2https://www.fidelity.com/learning-center/mutual-funds/growth-vs-value-investing.
3The value premium debate has not been limited to only the U.S. stock market, but extends to several international

stock markets (e.g., Chan et al. (1991), Fama and French (1998), and Daniel et al. (2001)), and also to other asset
classes (e.g., Asness et al. (2013)). We refer to Fama and French (2012) for recent empirical evidence on the prevalence
of a value premium in international stock markets.

1



First, these differences may be biological, in the sense that a genetic predisposition results in a

preference for a specific investment style. In recent years, individual characteristics of first-order

importance for portfolio choice, e.g., the propensity to take financial risk, have indeed been shown

to be partly explained by an individual’s genetic composition (e.g., Cesarini et al. (2009), Barnea,

Cronqvist, and Siegel (2010) and Cesarini et al. (2010)). As a result, we hypothesize that an

individual’s investment style has a biological basis, i.e., a preference for value versus growth stocks

is partially ingrained in an investor from birth.

Second, based on life course theory, an approach to research in social psychology,4 which has

recently made its way into finance research (e.g., Oyer (2006, 2008), Kaustia and Knüpfer (2008),

Malmendier and Nagel (2011, 2013), and Schoar and Zuo (2013)), we hypothesize that an individual’s

specific life experiences affect behaviors, including the individual’s investment style, later in life.

We consider several potentially relevant, and likely exogenous, life experiences of individuals. More

specifically, we analyze whether experiencing an adverse and significant macroeconomic event, e.g.,

growing up during the Great Depression, affects an individual’s value versus growth orientation.

We also examine the impressionable years during an individual’s life course, e.g., the economic

conditions when an individual entered the job market for the first time. Finally, we also examine

the socioeconomic status of the rearing environment in which the individual grew up.

Benjamin Graham and T. Rowe Price, Jr., constitute a colorful illustration of some of our

hypotheses. Graham is commonly dubbed the “Father of Value Investing” because he preferred

stocks with comparatively low valuation ratios and other characteristics that subsequently came to

define value investing. Price, the founder of the large money management company with his name,

is often referred to as the “Father of Growth Investing” because of his preference for companies

characterized by strong earnings growth, R&D intensity, and innovative technology. Their different

investment styles may very well have a biological basis, but this is not possible to examine without

data on their genetic differences. Interestingly, Graham grew up very poor, his father passing away

unexpectedly when he was young, and his mother losing the family’s savings in the stock market

crash known as the “Panic of 1907.” Among his brothers, Graham was often tasked with “bargain

4For further details and references, see, e.g., Giele and Elder (1998) and Elder et al. (2003).

2



hunting” at different grocery stores (e.g., Carlen (2012)). In comparison, Price had a privileged

upbringing, his father being an medical doctor who served as a surgeon his entire professional career

for a rapidly expanding railroad company, a growth company at that time. We hypothesize that

such differences in life experiences may contribute to differences in investment styles.

Our research contributes a new perspective to the long-standing value/growth debate in finance.

First, an investor’s style has a biological basis – a preference for value versus growth stocks is

partially ingrained in an investor already from birth. We estimate that genetic differences across

individuals explain 18% of the cross-sectional variation in value versus growth orientation, if using

P/E ratios as an investment style measure, and 25% if using Morningstar’s Value-Growth Score.

Second, investors who a priori are expected to take more financial risk have a preference for growth,

not value, investing. This result is consistently found in data for exogenous proxies for risk taking

propensity (e.g., gender and age) and also other individual characteristics (e.g., net worth). If

value is riskier than growth, it may be surprising that those who are expected to take more (less)

financial risk prefer growth (value) stock portfolios. Finally, an investor’s style is explained by life

course theory in that experiences, both earlier and later in life, are related to investment style. In

particular, investors with adverse macroeconomic experiences have stronger preferences for value

investing later in life. For example, those who grew up during the Great Depression have portfolios

with average P/E ratios that are 2.2 (or about 10% at the median) lower, controlling for individual

characteristics, several decades later in life. Consistent with an impressionable years hypothesis,

those who enter the job market for the first time during an economic downturn are also more value

oriented later on. We also find that those who grew up in a lower status socioeconomic rearing

environment have a stronger value orientation later in life.

The paper is organized as follows. Section II reviews related research. Section III introduces

our data and empirical methodology. Section IV reports our results. Section V discusses potential

implications of our results, and Section VI concludes.

3



II Related Research

A Biological Predispositions and Investment Style

A.1 Risk Preferences

A well-established empirical result is that a value/growth risk factor, referred to as HML (“high

minus low”) following the seminal work by Fama and French (1993), is a significant determinant of

cross-sectional returns of stock portfolios. Different stocks and portfolios have different exposures

to this HML factor, and as a result, different expected returns. If value-oriented portfolios have

outperformed growth-oriented portfolios historically because of differences in risk, we may expect

investors with a propensity to take more (less) financial risk to prefer value (growth) stocks and

mutual funds.5

Several recent studies have shown that a significant portion, or about 30%, of the cross-sectional

variation in financial risk preferences is explained by biological predispositions. In those studies

risk taking is not defined from the perspective of exposure to a value/growth risk factor, but risk

preferences are either elicited from experiments (e.g., Cesarini et al. (2009)) or involve measures

such as the share in equities (e.g., Barnea, Cronqvist, and Siegel (2010) and Cesarini et al. (2010)).

Some research in the intersection of finance and neuroscience has even identified specific candidate

genes involved in explaining differences in financial risk taking across individuals (e.g., Kuhnen and

Chiao (2009), Dreber et al. (2009), and Zhong et al. (2009)).6

The aforementioned studies all suggest a relation between, on the one hand, biological predispo-

sitions, and on the other hand, risk preferences. As a result, if we find that genetic differences across

investors explain their value versus growth orientation, differential genetic propensities to financial

risk taking is a potential explanation which would be consistent with a risk-based explanation for

the value premium.

5For a theoretical model of a possible relation between risk and the value premium, see, e.g., Zhang (2005), and for
related empirical evidence, see, e.g., Petkova and Zhang (2005) and Chen, Petkova, and Zhang (2008).

6The even deeper question is why risk preferences are genetic in the first place. Some of the theoretical work by
Robson (2001b) addresses very fundamental questions in economics such as why nature has endowed individuals with
utility functions. For further details and references, we refer to Robson (2001a).

4



A.2 Behavioral Biases

While there is consensus among most financial economists that value stocks historically have

produced higher returns than growth stocks, there is less agreement about why this is the case.7

Daniel and Titman (1997) show that the return premium on value stocks does not arise because of

the comovement of these stocks with a risk factor. Rather, it is the stocks’ characteristics that explain

the cross-sectional variation in stock returns. For example, Lakonishok, Shleifer, and Vishny (1994)

argue that the return of growth, or “glamour,” stocks is the result of investor sentiment and provide

evidence that value strategies produce higher returns because they exploit some of the behavioral

biases of investors, not because of risk. In other behavioral finance models, the value premium may

reflect positive feedback trading (e.g., De Long, Shleifer, Summers, and Waldmann (1990), Hong and

Stein (1999), and Barberis and Shleifer (2003)), conservatism and representativeness (e.g., Barberis,

Shleifer, and Vishny (1998)), or overconfidence (e.g., Daniel, Hirshleifer, and Subrahmanyam (1998)

and Daniel, Titman, and Wei (2001)).

Recent research suggests a relation between, on the one hand, biological predispositions, and

on the other hand, behavioral biases (e.g., Cesarini et al. (2012) and Cronqvist and Siegel (2013)).

As a result, if we find that genetic differences across investors explain their value versus growth

orientation, differential genetic behavioral biases is a potential explanation which would be consistent

with behavioral finance models.8

B Life Course Theory and Investment Style

In addition to biological predispositions we also hypothesize that differences in life experiences may

contribute to differences in investment styles across individuals.

7For a review of empirical evidence related to value and growth investing, see, e.g., Chan and Lakonishok (2004).
8Some of the theoretical work by Rayo and Becker (2007) and Brennan and Lo (2011) addresses why behaviors,

even if not rational as defined in standard economic models, may survive human evolution. For further details and
references, see, e.g., Cosmides and Tooby (1994), Chen et al. (2006), and Santos and Chen (2009).

5



B.1 Macroeconomic Experiences

Experiencing an adverse and significant macroeconomic event may have pervasive effects on an

individual’s behaviors later in life.9 In their “Depression Babies” study, Malmendier and Nagel

(2011) show that individuals who have experienced relatively low stock market returns in their lives

subsequently do not participate in the stock market and they take significantly less financial risk if

they do participate. Other economists have also found that macro events have long-term effects on

individual preferences. For example, Alesina and Fuchs-Schüendeln (2005) that post-reunification

East Germans (particularly older cohorts) have stronger preferences for, e.g., redistribution than

West Germans. Malmendier and Nagel (2013) show that differences in life experiences of high or

low inflation predict differences in subjective inflation expectations.

If an adverse macroeconomic experience results in less risk taking later in life, and if value is

riskier than growth investing, we would expect investors who grew up during the Great Depression

to prefer growth investing. An alternative hypothesis, not based on a risk explanation, is that

those who have more salient experiences of difficult economic conditions develop a value-oriented

investment style, with a preference for stocks that seem relatively “cheaper.”

B.2 Impressionable Years

A growing number of studies in social psychology suggest that experiences in early adulthood are

particularly important for preferences later in life (e.g., Krosnick and Alwin (1989)). An individual’s

core attitudes, beliefs, and preferences crystallize during a period of great neurological plasticity in

early adulthood – the so-called “impressionable years” – and remain largely unchanged afterwards.

For example, Giuliano and Spilimbergo (2013) show that experiencing an economic downturn during

the impressionable years (18-25 years old) affects redistribution and political preferences later in life.

In this study, we focus on whether an individual started his or her first employment in an

economic downturn. This measure comes with the caveat that it is less exogenous compared to a

9The Great Depression is the macro event that has so far been studied most in-depth in the social sciences,
and a variety of outcomes later in life have been examined. We refer to Elder (1974) for one of the first and most
comprehensive studies of the long-term effects of the Great Depression. Many researchers have argued that the
Great Depression created a “depression generation,” whose behavior affected the macroeconomy for decades after
the depression ended. For example, Friedman and Schwartz (1963) suggested that the Great Depression “shattered”
beliefs in capitalism.

6



birth cohort measure such as the Great Depression because individuals may to some extent choose

when they enter the job market by increasing their investment in education endogenously. We still

find it informative to examine the time of an individual’s first employment because it has been

shown to be important in several studies for other economic outcomes (e.g., Oyer (2006, 2008),

Kahn (2010), Malmendier et al. (2011), Oreopoulos et al. (2012), and Schoar and Zuo (2013)). This

is also a period in their lives when many individuals start to invest in stocks and mutual funds for

the very first time.

B.3 Rearing Environment

The hypothesis that the rearing environment, and other early-life experiences, may have significant

long-term effects on an individual’s behaviors later in life has recently made its way into economic

research. Most existing studies examine outcomes such as education and earnings. For example,

economists have shown that birth order and family size (e.g., Black et al. (2005)) affect educational

attainment and earnings later in life.Relatively few studies examine outcomes of primary interest

to financial economists. An exception is Chetty et al. (2011) who report that the pre-school

(kindergarten) environment explains, e.g., retirement savings behavior later in life.10

In this study, we focus on the rearing environment within the family during an individual’s

upbringing. More specifically, we hypothesize that the socioeconomic status (SES) of the rearing

environment in which an individual grows up explains cross-sectional differences in investment style

later in life. We also consider whether parents’ life experiences transfer to their children and affect

the investment style of their children. For example, if parents grew up during the Great Depression,

it may affect not only their own investment style late in life, but potentially also their children’s

value versus growth orientation through parenting; see, e.g., Bisin and Verdier (2000, 2001) for work

related to the social transmission of preferences and behavior from parents to their children.

10Even the pre-birth, i.e., “in utero,” environment has been shown to predict subsequent economic outcomes and
behaviors; see, e.g., Black et al. (2007), Almond and Currie (2011), and Cronqvist, Previtero, Siegel, and White (2013).

7



III Data

A Individual Characteristics

To empirically decompose variation in investment styles across a large sample of individual investors,

we employ data on identical and fraternal twins.11 We construct our data set by matching a large

number of twins from the Swedish Twin Registry (STR), the world’s largest twin registry, with data

from individual tax filings and other databases. In Sweden, twins are registered at birth, and the

STR collects additional data through in-depth interviews.12 STR’s data provide us with the zygosity

of each twin pair: Identical or “monozygotic” (MZ) twins are genetically identical, while fraternal

or “dizygotic” (DZ) twins are genetically different, and share on average 50% of their genes.13

Table 1 reports summary statistics for the twins in our data set and their individual characteristics.

Panel A shows that we have data on a total of 10,490 identical twins, and 24,486 fraternal twins,

who participate in the stock market. Opposite-sex twins are the most common (38%); identical male

twins are the least common (13%). Panel B reports summary statistics for individual characteristics,

including age, education, net worth, and disposable income, which we include as controls when we

estimate models in Section IV. The average size of the portfolios in our data set, about USD 33,500,

is comparable to those in other data sets of a broad set of individual investors, e.g., EUR 24,600 in

Grinblatt and Keloharju (2009).14

11Research in economics has a long history of using data on twins; see, e.g., Behrman and Taubman (1976, 1989),
Taubman (1976), Ashenfelter and Krueger (1994), and Black et al. (2007).

12STR’s databases are organized by birth cohort. The Screening Across Lifespan Twin, or “SALT,” database
contains data on twins born 1886–1958. The Swedish Twin Studies of Adults: Genes and Environment database, or
“STAGE,” contains data on twins born 1959–1985. In addition to twin pairs, twin identifiers, and zygosity status, the
databases contain variables based on STR’s telephone interviews (for SALT), completed 1998–2002, and combined
telephone interviews and Internet surveys (for STAGE), completed 2005–2006. For further details about STR, we
refer to Lichtenstein et al. (2006).

13Zygosity is based on questions about intrapair 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 percent accuracy on a
subsample of twins. For twin pairs for which DNA has been collected, zygosity status is based on DNA analysis.

14We use the average end-of-year exchange rate 1999-2007 of 8.0179 Swedish Krona per U.S. dollar to convert
summary statistics in the table. When we estimate models in Section IV, all values are in Swedish Krona, i.e., not
converted to U.S. dollars.

8



B Measures of Investment Style

Prior to the abolishment of the wealth tax in Sweden in 2007, 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, and other securities) owned on

December 31. We have matched the individuals in our data set with portfolio data between 1999

and 2007, the entire period for which data are available. For each individual, our data set contains

all securities owned at the end of the year (identified by each security’s International Security

Identification Number (ISIN)), the number of each security owned, and the end of the year value.

Security level data have been provided by S&P CapitalIQ and Morningstar. In our data set, there

is a total of about 2,000 different stocks and about 1,000 different mutual funds.

We do not expect a dichotomous classification of value versus growth investors to be empirically

relevant, so we categorize each investor’s value versus growth “orientation” on a continuum. For

stocks, we construct two measures of value versus growth orientation using different scaled price

ratios: Price/Earnings (P/E) and Price/Book (P/B).15 For each individual, we first compute

the value-weighted average ratio for each year. We then average these ratios over those years

an individual is in the data set in order to reduce measurement error. Table 2 reports detailed

definitions for each of our investment style measures. For mutual funds, we also construct two

measures: i) Morningstar’s Value-Growth Score, which varies from -100 (value) to +400 (growth);

ii) Name-based value/growth measure, which is -1 if a fund’s name contains “value,” +1 if a fund’s

name contains “growth” or “technology,” and zero otherwise. We use the same method as for stocks

to construct an average measure for each individual.

Table 3 Panel A reveals that while identical and fraternal twins are relatively similar with

respect to these investment style measures, there is significant variation across different investors

with respect to their value versus growth orientation. It is this cross-sectional variation that we

decompose and explain in Section IV.

Finally, Panel B of Table 3 shows that all measures of the value versus growth orientation of

investors’ portfolios are significantly positively correlated, suggesting that investors indeed have a

15Following CapitalIQ’s practices, the scaled price ratios are censored at 0 and 300. Winsorizing at the 1%-level
does not change any of the reported results.

9



consistent preference for certain investment styles.

IV Results

We first examine to which extent an individual’s investment style is determined by biological vs.

environmental factors. We then investigate whether specific proxies for (financial) risk taking predict

an investor’s value vs. growth orientation. Our final set of results analyses specific environmental

experiences that according to the life course theory may influences an investor’s investment style.

A Biological Predispositions and Investment Style

In this section, we first report separate correlations for identical versus fraternal twins for each of

our measures of investment style. We then provide formal estimation results from decomposing

the cross-sectional variation in investment style into genetic and environmental components. To

do so, we use empirical methods from quantitative behavioral genetics research that have recently

been employed also in research in economics (e.g., Cesarini et al. (2009) and Barnea et al. (2010)).

The approach involves maximum likelihood estimation (MLE) of a random effects model, but relies

on an intuitive and simple insight: Identical twins share 100% of their genes, while the average

proportion of shared genes is only 50% for fraternal twins, so if identical twins are more similar with

respect to their investment styles than are fraternal twins, then there is evidence that value versus

growth orientation is partly explained by genetic predispositions. For further details, we refer to the

Appendix of the paper.

A.1 Evidence from Correlations

Figure 1 reports correlations by genetic similarity, i.e., for identical twins and fraternal twins

(separately for same- and opposite-sex twins), for measures of value versus growth orientation. Panel

A contains results for stocks and Panel B for mutual funds.

Several conclusions emerge from this evidence. First, we find that identical twins’ investment

styles are significantly more correlated compared to fraternal twins. For example, the Pearson

correlation coefficient among identical twins is 0.32 for the average P/E ratio of the stock portfolio,

10



compared to only 0.19 among fraternal twins (0.20 among same-sex fraternal twins). The correlation

among identical twins is generally about double the correlation among fraternal twins. A similar

conclusion emerges for mutual funds. For example, the Pearson correlation coefficient among

identical twins is 0.30 for the average Value-Growth Score by Morningstar for the mutual fund

portfolio, compared to only 0.14 among fraternal twins (0.16 among same-sex fraternal twins). That

is, genetically more similar investors have more similar investment styles. This evidence strongly

suggests that genetic differences affect value versus growth orientation among individual investors.

Second, we find that the correlations among identical twins are significantly below one. That is,

even genetically identical investors show significant differences with respect to their investment styles.

This evidence shows the importance of the environment (e.g., individual-specific experiences and

events) in explaining an investor’s value versus growth orientation, and emphasizes the importance

of analyzing the effect on investment style of experiences and events during an individual’s life

course.

A.2 Evidence from Variance Decomposition

Tables 4 and 5 report estimation results from decomposing investment styles into genetic and

environmental variation.16 We report the relative proportions of the cross-sectional variation in

investment styles across individuals that are explained by genetic (A), common environmental (C),

and individual-specific environmental (E) factors (for details, see equation (5) in the Appendix).

More specifically, we first regress each investment style measure on a set of individual characteristics

and then we decompose the residual variation. We include the following individual characteristics:

Gender, age, education, marriage status, disposable income, and net worth.

The evidence from the variance decomposition in Table 4 confirms the correlation evidence and

shows that variation across investors with respect to value versus growth orientation of their stock

portfolio is partially genetic.17 The estimates of the A component vary from 18% to 24%, and

16See the Appendix for details on the maximum likelihood estimation of the random effects model specified in
equation (1) of the Appendix.

17As a robustness check, we have also examined Price/Sales (P/S) and Price/Cash flow (P/C) ratios. The A
components for these measures are similar to those reported for standard measures such as P/E and P/B ratios.

11



are statistically significant for each investment style measure.18 The C component is significantly

smaller, and varies from 8% to 10%. The remaining portion of the cross-sectional variation in

investment style is explained by individual-specific experiences and events.19

While the evidence reported so far involves individual stocks, we also decompose the cross-

sectional variation in investment style using data on mutual funds. We use two measures, the

Value-Growth Score by Morningstar and a name-based value/growth measure. These measures

provide a salient way for an individual investor to choose exposure based on his or her value versus

growth preference. Our conclusions from Table 5 are similar compared to individual stocks. The

estimates of the A component vary from 16% to 25%, and are statistically significant for each

investment style measure, and the estimate of the C component is smaller, ranging from 0% to 4%.

On the one hand, Tables 4 and 5 show that it is difficult to estimate the A component for value

versus growth orientation precisely. On the other hand, it is encouraging that each of the investment

style measures paint a very consistent picture in the sense of a statistically significant A component,

for both stocks and mutual funds. It should also be emphasized that recent studies related to

individual investor behavior have had difficulties explaining even 10% of the cross-sectional variation

when including a large set of individual characteristics (e.g., Brunnermier and Nagel (2008)). Overall,

based on the reported evidence, we conclude that an individual’s investment style has a biological

basis, i.e., a preference for value versus growth stocks is partially ingrained in an investor from birth.

An assumption of the model we estimate is the so-called Equal Environments Assumption

(EEA). If an individual’s parents or others in the environment treat identical twins more similarly

than fraternal twins (along dimensions relevant for investment style), then the estimated genetic

component (A) may be upward biased. This problem has been long recognized in twin research, and

substantial effort has been devoted to addressing it. From research in behavioral genetics, where

the EEA has 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)). Researchers have studied twins reared

18One concern is that including opposite-sex twins in our analysis results in an upward bias of the relative importance
of genetic factors, as captured by A, because identical twins always have the same sex. As a robustness check, we
have re-estimated the models for same-sex twins only, and we find that our results are qualitatively and quantitatively
very similar.

19The E component is also absorbing idiosyncratic measurement error. Because our data set comes from the Swedish
Tax Agency, which obtains the data directly from financial institutions, reporting errors should be relatively rare.

12



apart for which there is no common parental environment, and these studies generally produce

estimates similar to those using twins who were reared together (e.g., Bouchard et al. (1990)).

Recent progress in genotyping has enabled researchers to construct DNA-based measures of pairwise

genetic relatedness and to compare genetic similarity to similarity with respect to, foe example,

height and IQ (Jian et al. (2010) and Davies et al. (2011)). These studies use unrelated subjects,

not twins, and show that at least 50% of the variation in the studied outcomes is due to genetic

variation, supporting existing evidence from twin studies.

B Risk Preferences and Investment Style

If value-oriented portfolios have outperformed growth-oriented portfolios historically because of

differences in risk, we may expect investors with a preference for more (less) financial risk to prefer

value (growth) stocks and mutual funds. In this section, we therefore examine whether several

proxies which a priori are expected to correlate with risk preferences also explain individuals’

investment styles. The results are reported in Table 6.

We start by examining whether gender is related to value versus growth orientation. Several

previous studies in economics have found that men generally take more risk compared to women

(see, e.g., Croson and Gneezy (2009) and Bertrand (2011) for extensive literature reviews). As a

result, if value is riskier than growth investing, we would expect that men, on average, are more

value orientated than women. Table 6 shows that we find the opposite result in data: Men have a

stronger preference for growth investing compared to women. We find that men’s stock portfolios

have a P/E ratio that is on average 0.4 higher than women’s, about 2% higher compared to the

median P/E. Another way to quantify the size of this effect is to report that the median P/E ratio

for a value fund based on the fund’s name is 15.7, compared to 21.0 for growth funds, which means

that an effect of 0.4 corresponds to about 8% of the difference between value and growth funds,

which is an economically significant effect.

We also examine age because older investors are generally found to take less financial risk

compared to younger investors (e.g., Barsky, Juster, Kimball, and Shapiro (1997) and Paulsen et al.

(2012)). A risk preference explanation would predict that older investors are more growth oriented.

13



Again, Table 6 shows that we find the opposite result in data: Older investors have a significantly

stronger preference for value investing. The average P/E ratio of the stock portfolio of a 65 year old

investor is 4.4 (or about 19% compared to the median) lower compared to a 25 year old.20

We also examine a larger set of proxies potentially related to an individual’s risk taking propensity,

including education, marriage status, disposable income, net worth, and share in equities. Table 6

first includes these variables one by one, and then all at the same time. We find that those with

a college education have more growth oriented portfolios compared to lower-education investors,

with an average P/E ratio that is 2.3 (or about 11% compared to the median) higher. Investors

who have higher disposable incomes and net worth have a stronger preference for growth. A one

standard deviation increase in the log of disposable income corresponds to an average P/E that

is about 1.4 higher. Finally, those with a higher equity share, a measure of financial risk taking

propensity, prefer growth oriented portfolios. A 10 percentage points increase in the share in equities

corresponds to an average P/E that is about 0.3 higher.

The overall conclusion from the above analysis is that investors who a priori are expected to

take more financial risk have a preference for growth investing, not value investing. This result is

consistently found in data for both more exogenous variables (e.g., gender and age) and also other

individual characteristics (e.g., net worth) we examine.

C Life Course Theory and Investment Style

In this section, we examine to what extent differential life experiences and events of individuals

explain cross-sectional differences in investment styles later in life. Based on pre-existing research in

social psychology we consider several types of potentially relevant, and exogenous, life experiences of

individuals: 1) Macroeconomic experiences, 2) Impressionable years, and 3) Rearing environment.

20Because some studies have found that the very oldest individuals in their samples do take more financial risk (e.g.,
Barsky et al. (1997)), we have checked that our conclusion is robust to excluding those over 70 years. Our results are
indeed somewhat stronger when we drop the very oldest investors in our data.

14



C.1 Macroeconomic Experiences

First, we analyze whether there is a persistent effect on an individual’s investment style of growing

up during the Great Depression. More specifically, we examine the effect on value versus growth

orientation of being born between 1920 and 1929, using the same “Depression Baby” definition as

Schoar and Zuo (2013).21 If an adverse macroeconomic experience results in less risk taking later

in life, and if value is riskier than growth investing, we may expect investors who grew up during

the Great Depression to prefer growth investing. An alternative hypothesis, not based on a risk

explanation, is that those who have more salient experiences of difficult economic conditions develop

a value-oriented investment style, with a preference for stocks that seem relatively “cheaper.” The

results in Table 7 are consistent with the latter hypothesis: Individuals who grew up during the

Great Depression show significantly more value-orientation in their stock portfolios several decades

later in life. More specifically, we find that those who grew up during the Great Depression have

portfolios with average P/E ratios that are 2.2 (or about 10% at the median) lower compared to

those of other investors. It is important to emphasize that we control for disposable income and net

worth, which may also be affected by a Great Depression experience, so our results are not simply

reflecting long-term wealth differences. As a robustness check, we also analyze only old investors,

defined as those born before 1940, i.e., we include Depression Babies with only somewhat older

and somewhat younger individuals. Table 7 shows that the results do not change significantly (the

estimated coefficient drops from -2.2 to -1.8 but is still statistically significant).

Second, as an alternative measure of macroeconomic experiences, we also analyze an individual’s

GDP growth experiences during his or her life so far. More specifically, we measure the average GDP

growth from an individual’s birth year until year 2000, i.e., the start of our data set. Experiencing

poor GDP growth may reduce an individual’s propensity to take risk later in life. A risk explanation

for investment style may then suggest that individuals with more negative GDP growth experiences

become growth investors. In contrast, we find that experiencing stronger GDP growth results in a

growth oriented investment style. We find that for a 100 basis points per year higher average GDP

growth experience during the life, the average P/E ratio of the stock portfolio of the investor is 1.2

21Sweden was affected by the Wall Street Crash of 1929, and was also the origin of the Kreuger Crash of 1932, with
adverse international macroeconomic consequences deepening the Depression in several countries, including the U.S.

15



(or about 6% at the median) higher compared to those of other investors, controlling for individual

characteristics. While economically significant, we recognize that the effect is statistically weaker

(t-statistic = 1.81) compared to the Great Depression, the most severe macro event someone in our

sample experienced.

Finally, we examine whether “Individual HML Experience,” based on the returns of HML factor

portfolios, explains subsequent investment styles (e.g., Malmendier and Nagel (2011)). That is, do

those who have experienced higher HML returns develop into more value oriented investors? We

construct our measures in a similar way to our “Individual GDP Growth Experience” measure, for

the U.S. stock market (see Fama and French (1993)) as well as for the Swedish stock market (using

data provided by Kenneth French).22 We do not find a robust and statistically significant relation

between “Individual HML Experience” and value versus growth orientation.

Our results are supportive of life course theory partially explaining an investor’s value or

growth orientation. An individual’s GDP growth experiences, particularly adverse and significant

macroeconomic experiences (e.g., the Great Depression) affect an individual’s investment style later

in life. The evidence is consistent with those who have more salient experiences of difficult economic

conditions developing a value-oriented investment style, with a preference for stocks that seem

relatively “cheaper.”

C.2 Impressionable Years

We also analyze impressionable years during an individual’s life. More specifically, we consider

whether an individual started his or her first employment in an economic downturn. There exists

no similar classification of recessions to the NBER’s business cycle database for Sweden, so we have

analyzed a broad set of alternative measures of economic downturns. First, we define a recession as

a period with a year with negative GDP growth +/– 1 years.23 Table 8 shows that individuals who

entered the job market during a recession have portfolios with an average P/E ratio that are 0.3

22We compute HML returns from the year an individual entered the job market for the first time. This increases
the sample size for the HML factor portfolio for Sweden for which we only have data starting in 1975. Our results are
also robust to using data for the HML factor portfolio for Europe (see Fama and French (2012)) for which we only
have data starting in 1990.

23Our results are stronger if we only include the years of negative GDP growth. A concern, though, is that such a
measure is more susceptible to criticism of exogeneity compared to a measure that also includes +/– 1 years.

16



lower, but the effect is not statistically significant.

Second, we also report results for the most severe economic downturns someone in our sample

experienced, corresponding to World War I, the Great Depression, and World War II. We find that

those who entered the job market in a significant economic downturn have portfolios with average

P/E ratios that are 2.2 (or about 10% at the median) lower compared to those of other investors,

controlling for individual characteristics. Our result that the preference for value investing among

those with their first employment in an economic downturn is robust to controlling for disposable

income and net worth implies that there is a direct effect of economic downturns on investment

style later in life, in addition to any indirect effect from lower income of those who entered the job

market in economic downturns (e.g., Oreopoulos et al. (2012)). We find an even stronger effect

(P/E ratios that are 3.8 lower) if we examine whether an individual experienced a severe economic

downturn when 18-25 years old, rather than examining the macroeconomic conditions during the

years of an individual’s first employment (e.g., Giuliano and Spilimbergo (2013)).

While all specifications continue to control for income as well as net-worth, we also show the

impressionable year effects remain significant when we include cohort (by decade of birth) fixed

effects. Our results are therefore supportive of the impressionable years hypothesis. The economic

conditions at the time of an individual’s first job market entry and when an individual is 18-25 years

old partially explain an individual’s investment style later in life; the more severe the economic

downturn, the more value oriented the individual is later on in life.

C.3 Rearing Environment

We also examine whether the rearing environment has significant long-term effects on an individual’s

investment style later in life. First, we examine the socioeconomic status (SES) of an individual’s

parents. We are not able to measure parents’ SES exactly when an individual grew up, so we

use parents’ net worth at the start of our data set as a proxy. The results are reported in Table

9. We find that individuals who grew up in a lower SES environment, i.e., relatively poor, show

significantly more value-orientation in their stock portfolios later in their lives. Investors at the

bottom of the parental wealth distribution (10th percentile) have portfolios with average P/E ratios

17



that are 1.0 (or about 5% at the median) lower compared to investors at the top of the distribution

(90th percentile). We show that this effect is robust within each generation by controlling for birth

cohort (decade) fixed effects, so this result is not specific to, e.g., the Great Depression cohort.

Second, we examine if parents’ life experiences transfer to their children and affect the investment

style also of the next generation. We find evidence consistent with an inter-generational transfer of

life experiences. Investors whose parents were born between 1920 and 1929, i.e., grew up during the

Great Depression, show significantly more value orientation. Comparing the Great Depression effect

on an individual’s own investment style (-2.2 in Table 7) to the one of the next generation (-1.2 in

Table 9) we conclude that about 50% of the effect is transmitted to the next generation.

The overall conclusion from the above analysis is that individuals’ investment styles are affected

also by the rearing environment in which they grew up, in particular their parents’ socioeconomic

status and their parents’ life experiences.

V Discussion

In this section, we discuss some of the potential implications of our results.

Asset prices and the value premium. First, we find that investors who a priori are expected to

take more financial risk have a preference for growth, not value, investing. If the value premium is

the result of risk (e.g., Fama and French (1993) and Petkova and Zhang (2005)), it is surprising that

risk-taking investors prefer growth stocks and mutual funds.24 Some of our results seem more consist

with a behavioral biases explanation than a risk explanation. For example, some have argued that

the value premium is a result of investor overconfidence (e.g., Daniel, Hirshleifer, and Subrahmanyam

(1998) and Daniel, Titman, and Wei (2001)), and some of the investors who have a preference for

growth stocks, e.g., men and younger investors have been found to be more overconfident (e.g.,

Barber and Odean (2001)). Second, our results imply that the overall composition of a market

may affect the demand for value versus growth stocks, and in the end potentially equilibrium asset

24Some may argue that individual investors use growth investing as a “hedging” approach. We believe that hedging
arguments are problematic. First, growth investing seems to be a costly and imprecise approach to offset human
capital risk. Second, we are not aware of any empirical evidence suggesting that individuals estimate their human
capital risk exposure and then hedge such risks in the stock market. In practice, individuals often do the opposite of
hedging (e.g., Benartzi (2001)).

18



prices. For example, our result that gender and age are significantly related to value versus growth

orientation implies that the gender and age distributions in a market may partially explain the value

premium. Similarly, the life experiences of the participants in a particular market may affect the

demand for value versus growth stocks, resulting in “legacy effects” of macro events that occurred a

long time ago also for the value premium, similar to the implications of Friedman and Schwartz

(1963) and Cogley and Sargent (2008) for the equity premium.

Suboptimal investor portfolios. One possible explanation of our findings is that individual in-

vestors do not perceive value as riskier than growth portfolios. Many mutual fund companies indeed

instill this view into investors. To provide only one example, Fidelity explains the difference between

value and growth funds to investors as follows on their website: “While growth funds are expected to

offer the potential for higher returns, they also generally represent a greater risk when compared to

value funds (emphasis added).”25 If value is riskier than growth, though, for example because asset

prices are set by institutions as the marginal investors who affect prices, individuals’ mis-calibration

imply that value (growth) investors may end up with significantly more (less) risk than they would

have done if they were appropriately calibrated (because they believe value if less risky). As a result,

many individual investors may end up with suboptimal portfolios.

Mechanisms. While we find that an individual’s investment style is explained by both biological

predispositions as well as experiences and events during the course of life, the paper does not uncover

the specific mechanisms that explain these effects. The use of gene-candidate studies or genome-wide

association studies (GWAS) to identify the specific set of gene(s) that explain value versus growth

orientation may be a productive next step for researchers. Several specific genes related to financial

risk-taking and long-short risk have recently been identified (e.g., Kuhnen and Chiao (2009) and

Zhong et al. (2009)). Examining whether the very same risk-taking genes also explain value versus

growth orientation among investors may provide additional evidence on whether the value premium

is related to risk or not. Also, as the number of studies related to finance that support life course

theory continues to increase (e.g., Oyer (2008), Malmendier and Nagel (2011), and Schoar and

Zuo (2013)), another next step for researchers is to uncover the specific mechanisms than explain

25https://www.fidelity.com/learning-center/mutual-funds/growth-vs-value-investing.

19



why macroeconomic and other events are related to financial behavior decades later in life. Recent

research on neurological development shows that, in the developing brain, the volume of gray matter

in the cortex gradually increases until about the age of adolescence, but then sharply decreases as

the brain prunes away neuronal connections that are deemed superfluous to the adult needs of the

individual (e.g., Spear (2000)). Such evidence may provide a mechanism for early life experiences

explaining an individual’s investment behavior later in life, but more research is certainly required.

VI Conclusion

We report that several factors explain an investor’s style, i.e., the value versus growth orientation

of the investor’s stock portfolio. First, an investor’s style has a biological basis – a preference for

value versus growth stocks is partially ingrained in an investor already from birth. We estimate

that genetic differences across individuals explain 18% of the cross-sectional variation in value

versus growth orientation, if using P/E ratios as an investment style measure, and 25% if using

Morningstar’s Value-Growth Score. This evidence contributes to a growing number of studies which

show that individual characteristics of importance for portfolio choice are partly explained by an

individual’s biological predispositions and genetic composition (e.g., Cesarini et al. (2009), Kuhnen

and Chiao (2009), and Barnea, Cronqvist, and Siegel (2010)).

Second, investors who a priori are expected to take more financial risk have a preference for

growth, not value, investing. This result is consistently found in data for exogenous proxies for risk

taking propensity (e.g., gender and age) and also other individual characteristics (e.g., wealth). If

value is riskier than growth, it may be surprising that those who are expected to take more (less)

financial risk prefer growth (value) stock portfolios. This evidence suggests that either value is

not riskier than growth, or that the average individual investor in mis-calibrated about the risk

of value versus growth investing. In this sense, our research contributes a new perspective to the

long-standing value/growth debate in finance (e.g., Fama and French (1992, 1993) Lakonishok,

Shleifer, and Vishny (1994)). More specifically, we provide a new perspective on the source of the

value premium by showing that individual investors’ portfolio choices do not seem to be consistent

with a risk explanation.

20



Finally, an investor’s style is explained by life course theory in that experiences, both earlier and

later in life, are related to investment style. In particular, investors with adverse macroeconomic

experiences have stronger preferences for value investing later in life. For example, those who grew

up during the Great Depression have portfolios with average P/E ratios that are 2.2 (or about

10% at the median) lower, controlling for individual characteristics, several decades later in life.

Consistent with an impressionable years hypothesis, those who enter the job market for the first

time during an economic downturn are also more value oriented later on. We also find that those

who grew up in a lower status socioeconomic rearing environment have a stronger value orientation

later in life. This evidence contributes to several recent studies which show the importance of life

experiences and events for economic behaviors later in life (e.g., Oyer (2006), Kaustia and Knüpfer

(2008), Malmendier and Nagel (2011), and Giuliano and Spilimbergo (2013)).

21



References

Alesina, A., Fuchs-Schüendeln, N., 2005. Good bye Lenin (or not?): The effect of Communism on
people’s preferences. American Economic Review 97, 1507–1528.

Almond, D., Currie, J., 2011. Killing me softly: The fetal origins hypothesis. Journal of Economic
Perspectives 25 (3), 153–172.

Ashenfelter, O., Krueger, A. B., 1994. Estimates of the economic returns to schooling from a new
sample of twins. American Economic Review 84 (5), 1157–1173.

Asness, C. S., Moskowitz, T. J., Pedersen, L. H., 2013. Value and momentum everywhere. Journal
of Finance 68 (3), 929–985.

Barber, B. M., Odean, T., 2001. Boys will be boys: Gender, overconfidence, and common stock
investment. Quarterly Journal of Economics 116 (1), 261–292.

Barberis, N., Shleifer, A., 2003. Style investing. Journal of Financial Economics 68 (2), 161–199.

Barberis, N., Shleifer, A., Vishny, R. W., 1998. A model of investor sentiment. Journal of Financial
Economics 49, 307–343.

Barnea, A., Cronqvist, H., Siegel, S., 2010. Nature or nurture: What determines investor behavior?
Journal of Financial Economics 98, 583–604.

Barsky, R. B., Juster, F. T., Kimball, M. S., Shapiro, M. D., 1997. Preference parameters and
behavioral heterogeneity: An experimental approach in the health and retirement study. Quarterly
Journal of Economics 112, 537–579.

Behrman, J. R., Taubman, P., 1976. Intergenerational transmission of income and wealth. American
Economic Review 66, 436–440.

Behrman, J. R., Taubman, P., 1989. Is schooling “mostly in the genes”? Nature-nurture decomposi-
tion using data on relatives. Journal of Political Economy 97, 1425–1446.

Benartzi, S., 2001. Excessive extrapolation and the allocation of 401 (k) accounts to company stock.
Journal of Finance 56 (5), 1747–1764.

Bertrand, M., 2011. New perspectives on gender. Handbook of Labor Economics 4, 1543–1590.

Bisin, A., Verdier, T., 2000. Beyond the melting pot: Cultural transmission, marriage, and the
evolution of ethnic and religious traits. Quarterly Journal of Economics 115 (3), 955–988.

Bisin, A., Verdier, T., 2001. The economics of cultural transmission and the dynamics of preferences.
Journal of Economic Theory 97 (2), 298 – 319.

Black, S. E., Devereux, P. J., Salvanes, K. G., 2005. The more the merrier? The effect of family size
and birth order on children’s education. Quarterly Journal of Economics 120 (2), 669–700.

Black, S. E., Devereux, P. J., Salvanes, K. G., 2007. From the cradle to the labor market? The
effect of birth weight on adult outcomes. Quarterly Journal of Economics 122 (1), 409–439.

22



Bouchard, T. J., 1998. Genetic and environmental influences on adult intelligence and special mental
abilities. Human Biology 70, 257–279.

Bouchard, T. J., Lykken, D. T., McGue, M., Segal, N. L., Tellegen, A., 1990. Sources of human
psychological differences: The Minnesota study of twins reared apart. Science 250, 223–228.

Brennan, T. J., Lo, A. W., 2011. The origin of behavior. Quarterly Journal of Finance 1, 55–108.

Brunnermier, M. K., Nagel, S., 2008. Do wealth fluctuations generate time-varying risk aversion?
Micro-evidence on individuals’ asset allocation. American Economic Review 98, 713–736.

Carlen, J., 2012. Einstein of Money: The Life and Timeless Financial Wisdom of Benjamin Graham.
Prometheus Books.

Cesarini, D., Dawes, C. T., Johannesson, M., Lichtenstein, P., Wallace, B., 2009. Genetic variation
in preferences for giving and risk taking. Quarterly Journal of Economics 124, 809–842.

Cesarini, D., Johannesson, M., Lichtenstein, P., Sandewall, O., Wallace, B., 2010. Genetic variation
in financial decision making. Journal of Finance 65, 1725–1754.

Cesarini, D., Johannesson, M., Magnusson, P. K. E., Wallace, B., 2012. The behavioral genetics of
behavioral anomalies. Management Science 58, 21–34.

Chan, L. K. C., Hamao, Y., Lakonishok, J., 1991. Fundamentals and stock returns in Japan. Journal
of Finance 46 (5), 1739–1764.

Chan, L. K. C., Lakonishok, J., 2004. Value and growth investing: Review and update. Financial
Analysts Journal, 71–86.

Chen, L., Petkova, R., Zhang, L., 2008. The expected value premium. Journal of Financial Economics
87 (2), 269–280.

Chen, M. K., Lakshminarayanan, V., Santos, L. R., 2006. How basic are behavioral biases? Evidence
from Capuchin monkey trading behavior. Journal of Political Economy 114 (3), 517–537.

Chetty, R., Friedman, J. N., Hilger, N., Saez, E., Schanzenbach, D. W., Yagan, D., 2011. How
does your kindergarten classroom affect your earnings? Evidence from Project STAR. Quarterly
Journal of Economics 126 (4), 1593–1660.

Cogley, T., Sargent, T. J., 2008. The market price of risk and the equity premium: A legacy of the
Great Depression? Journal of Monetary Economics 55 (3), 454–476.

Cosmides, L., Tooby, J., 1994. Better than rational: Evolutionary psychology and the invisible hand.
American Economic Review 84 (2), 327–332.

Cronqvist, H., Previtero, A., Siegel, S., White, R. E., 2013. Prenatal exposure to testosterone
increases financial risk-taking and reduces the gender gap. Working paper, University of Western
Ontario, Richard Ivey School of Business.

Cronqvist, H., Siegel, S., 2013. The genetics of investment biases. Forthcoming Journal of Financial
Economics.

23



Croson, R., Gneezy, U., 2009. Gender differences in preferences. Journal of Economic Literature 47,
1–27.

Daniel, K., Hirshleifer, D., Subrahmanyam, A., 1998. Investor psychology and security market
under-and overreactions. Journal of Finance 53 (6), 1839–1885.

Daniel, K., Titman, S., 1997. Evidence on the characteristics of cross sectional variation in stock
returns. Journal of Finance 52 (1), 1–33.

Daniel, K., Titman, S., Wei, K., 2001. Explaining the cross-section of stock returns in Japan: Factors
or characteristics? Journal of Finance 56 (2), 743–766.

Davies, G., Tenesa, A., Payton, A., Yang, J., Harris, S. E., Liewald, D., Ke, X., Le Hellard, S.,
Christoforou, A., Luciano, M., McGhee, K., Lopez, L., Gow, A. J., Corley, J., Redmond, P., Fox,
H. C., Haggarty, P., Whalley, L. J., McNeill, G., Goddard, M. E., 2011. Genome-wide association
studies establish that human intelligence is highly heritable and polygenic. Molecular Psychiatry
16 (10), 996 – 1005.

De Bondt, W. F. M., Thaler, R. H., 1985. Does the stock market overreact? Journal of Finance 40,
793–805.

De Long, J. B., Shleifer, A., Summers, L. H., Waldmann, R. J., 1990. Positive feedback investment
strategies and destabilizing rational speculation. Journal of Finance 45 (2), 379–395, english.

Dreber, A., Apicella, C. L., Eisenberg, D. T. A., Garcia, J. R., Zamore, R. S., 2009. The 7R
polymorphism in the dopamine receptor D4 gene (DRD4) is associated with financial risk-taking
in men. Evolution and Human Behavior 30, 85–92.

Elder, G. H., 1974. Children of the Great Depression: Social Change in Life Experience. University
of Chicago Press.

Elder, G. H., Johnson, M. K., Crosnoe, R., 2003. The emergence and development of life course
theory. Kluwer Academic Publishers, pp. 3–19.

Falconer, D. S., Mackay, T. F. C., 1996. Introduction to quantitative genetics, 4th Edition.

Fama, E. F., French, K. R., 1992. The cross-section of expected stock returns. Journal of Finance
47 (2), 427–465.

Fama, E. F., French, K. R., 1993. Common risk factors in the returns on stocks and bonds. Journal
of Financial Economics 33 (1), 3–56.

Fama, E. F., French, K. R., 1996. Multifactor explanations of asset pricing anomalies. Journal of
Finance 51 (1), 55–84.

Fama, E. F., French, K. R., 1998. Value versus growth: The international evidence. Journal of
Finance 53 (6), 1975–1999.

Fama, E. F., French, K. R., 2012. Size, value, and momentum in international stock returns. Journal
of Financial Economics 105 (3), 457–472.

24



Friedman, M., Schwartz, A. J., 1963. A Monetary History of the United States, 1867-1960. Princeton
University Press.

Giele, J. Z., Elder, G. H., 1998. Methods of life course research: Qualitative and quantitative
approaches. Sage Publications.

Giuliano, P., Spilimbergo, A., 2013. Growing up in a recession. Forthcoming Review of Economic
Studies.

Grinblatt, M., Keloharju, M., 2009. Sensation seeking, overconfidence, and trading activity. Journal
of Finance 64, 549–578.

Hong, H., Stein, J. C., 1999. A unified theory of underreaction, momentum trading, and overreaction
in assets markets. Journal of Finance 54 (6), 2143–2184.

Jian, Y., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden,
P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., Goddard, M. E., Visscher, P. M., 2010.
Common SNPs explain a large proportion of the heritability for human height. Nature Genetics
42 (7), 565 – 569.

Kahn, L. B., 2010. The long-term labor market consequences of graduating from college in a bad
economy. Labour Economics 17 (2), 303–316.

Kaustia, M., Knüpfer, S., 2008. Do investors overweight personal experience? Evidence from IPO
subscriptions. Journal of Finance 63 (6), 2679–2702.

Krosnick, J. A., Alwin, D. F., 1989. Aging and susceptibility to attitude change. Journal of Personality
and Social Psychology 57 (3), 416.

Kuhnen, C. M., Chiao, J., 2009. Genetic determinants of financial risk taking. PLoS ONE 4.

Lakonishok, J., Shleifer, A., Vishny, R. W., 1994. Contrarian investment, extrapolation, and risk.
Journal of Finance 49 (5), 1541–1578.

Lichtenstein, P., Sullivan, P. F., Cnattingius, S., Gatz, M., Johansson, S., Carlström, E., Björk, C.,
Svartengren, M., Wolk, A., Klareskog, L., de Faire, U., Schalling, M., Palmgren, J., Pedersen,
N. L., 2006. The Swedish twin registry in the third millennium: An update. Twin Research and
Human Genetics 9, 875–882.

Malmendier, U., Nagel, S., 2011. Depression babies: Do macroeconomic experiences affect risk-
taking? Quarterly Journal of Economics 126 (1), 373–416.

Malmendier, U., Nagel, S., 2013. Learning from inflation experiences. Working paper, NBER.

Malmendier, U., Tate, G., Yan, J., 2011. Overconfidence and early-life experiences: The effect of
managerial traits on corporate financial policies. Journal of Finance 66 (5), 1687–1733.

Oreopoulos, P., von Wachter, T., Heisz, A., 2012. The short-and long-term career effects of graduating
in a recession. American Economic Journal: Applied Economics 4 (1), 1–29.

Oyer, P., 2006. Initial labor market conditions and long-term outcomes for economists. Journal of
Economic Perspectives 20, 143–160.

25



Oyer, P., 2008. The making of an investment banker: Stock market shocks, career choice, and
lifetime income. Journal of Finance 63 (6), 2601–2628.

Paulsen, D. J., Platt, M. L., Huettel, S. A., Brannon, E. M., 2012. From risk-seeking to risk-averse:
The development of economic risk preference from childhood to adulthood. Frontiers in Psychology
3.

Petkova, R., Zhang, L., 2005. Is value riskier than growth? Journal of Financial Economics 78 (1),
187–202.

Rayo, L., Becker, G., 2007. Evolutionary efficiency and happiness. Journal of Political Economy
115 (2).

Robson, A. J., 2001a. The biological basis of economic behavior. Journal of Economic Literature 29,
11–33.

Robson, A. J., 2001b. Why would nature give individuals utility functions? Journal of Political
Economy 109, 900–914.

Santos, L. R., Chen, M. K., 2009. The evolution of rational and irrational economic behavior:
Evidence and insight from a non-human primate species. In: Glimcher, P., Camerer, C., Fehr, E.,
Poldrack, R. (Eds.), Neuroeconomics: Decision-Making and the Brain. Elsevier, London, U.K.,
pp. 81–94.

Schoar, A., Zuo, L., 2013. Shaped by booms and busts: How the economy impacts CEO careers and
management styles. Working paper, MIT Sloan School of Management.

Spear, D. O., 2000. Neurobehavioral changes in adolescence. Current Directions in Psychological
Science 9, 111–114.

Taubman, P., 1976. The determinants of earnings: Genetics, family, and other environments; A
study of white male twins. American Economic Review 66 (5), 858–870.

Zhang, L., 2005. The value premium. Journal of Finance 60 (1), 67–103.

Zhong, S., Israel, S., Xue, H., Ebstein, R. P., Chew, S. H., 2009. Monoamine Oxidase A gene
(MAOA) associated with attitude towards longshot risks. PLoS ONE 4.

26



Appendix

In this appendix, we describe the empirical methodology employed to decompose the cross-sectional
variation in individual investors’ investment styles into genetic and environmental components.
Specifically, we model the value/growth orientation, vgij, for twin pair i and twin j (1 or 2) as a
function of observable socioeconomic individual characteristics Xij and three unobservable random
effects, an additive genetic effect, aij, an effect of the environment common to both twins (e.g.,
upbringing), ci, and an individual-specific effect, eij, which also absorbs idiosyncratic measurement
error:

vgij = β0 + β1Xij + aij + ci + eij. (1)

In quantitative behavioral genetics research, this model is referred to as an “ACE model,” where
“A” stands for additive genetic effects, “C” for common environment, and “E” for individual-specific
environment.26 The additive genetic component aij in Equation (1) represents the sum of the
genotypic values of all “genes” that influence an individual’s behavior. Each individual has two,
potentially different, versions (alleles) of each gene (one is from each parent), and each version is
assumed to have a specific, additive effect on the individual’s behavior. The genotypic value of a
gene is the sum of the effects of both alleles present in a given individual. Consider, for example,
two different alleles A1 and A2 for a given given gene and assume that the effect of the A1 allele
on investment style is of magnitude α1, while the effect of the A2 allele is α2. An individual with
genotype A1A1 would experience the genetic effect 2α1, while genotype A1A2 would have a genetic
effect of α1 + α2.

27 We also assume that aij, ci, and eij are uncorrelated with one another and
across twin pairs and normally distributed with zero means and variances σ2a, σ

2
c , and σ

2
e , so that

the total residual variance σ2 is the sum of the three variance components (σ2 = σ2a + σ
2
c + σ

2
e ).

Identification of variation due to aij, ci, and eij is possible due to constraints on the covariance
matrices for these effects. These constraints are the result of the genetic similarity of twins and
assumptions about upbringing and other aspects of the common environment. Consider two twin
pairs i = 1, 2 with twins j = 1, 2 in each pair, where the first is a pair of identical twins and the
second is a pair of fraternal twins. The additive genetic effects are: a = (a11,a12,a21,a22)

′. Identical
and fraternal twin pairs differ in their genetic similarity, i.e., the off-diagonal elements related to
identical twins in the matrix in (2) are 1 as the proportion of shared additive genetic variation is
100% between identical twins. In contrast, for fraternal twins the proportion of the shared additive
genetic variation is on average only 50%, i.e., the off-diagonal elements related to fraternal twins in
the matrix in (2) are 1/2.28 As a result, for these two twin pairs, the covariance matrix with respect

26See, e.g., Falconer and Mackay (1996) for a more detailed discussion of quantitative behavioral genetics research.
27The extent to which the effect of two different alleles deviates from the sum of their individual effects is called

“dominance deviation.”
28For an intuitive explanation of the proportion of the shared additive genetic variation for fraternal twins as well

as non-twin siblings, consider a single gene, of which one parent has allele A1 and A2, while the other parent has
allele A3 and A4. Any of their off-spring will have one of the following combinations as they get one allele from each
parent: A1A3, A1A4, A2A3, or A2A4. Suppose one fraternal twin is of A1A3 type. The overlap with the fraternal
twin sibling will be: 1 if the sibling is of A1A3 type, 1/2 if type A1A4, 1/2 if type A2A3, and 0 if the type is A2A4.
This implies an average overlap of 1/2. For a formal derivation, see, e.g., Falconer and Mackay (1996).

27



to aij is:

Cov(a) = σ2a




1 1 0 0
1 1 0 0
0 0 1 1/2
0 0 1/2 1


 . (2)

The common environmental effects are: c = (c11,c12,c21,c22)
′. The model assumes that identical

and fraternal twins experience the same degree of similarity in their common environments (the
“Equal Environments Assumption”). That is, the off-diagonal elements related to either identical or
fraternal twins in the matrix in (3) are 1. Assuming that identical and fraternal twins experience
the same degree of similarity in their common environment, any excess similarity between identical
twins is due to the greater proportion of genes shared by identical twins than by fraternal twins. As
a result, for the two twin pairs, the covariance matrix with respect to ci is:

Cov(c) = σ2c




1 1 0 0
1 1 0 0
0 0 1 1
0 0 1 1


 . (3)

The individual-specific environmental effects are: e = (e11,e12,e21,e22)
′. These error terms

represent for example life experiences, but also idiosyncratic measurement error. That is, the
off-diagonal elements related to either identical or fraternal twins in the matrix in (4) are 0. As a
result, for the two twin pairs, the covariance matrix with respect to eij is:

Cov(e) = σ2e




1 0 0 0
0 1 0 0
0 0 1 0
0 0 0 1


 . (4)

We use maximum likelihood to estimate the model in equation (1). Finally, we calculate the
variance components A, C, and E. A is the proportion of the total residual variance that is related
to an additive genetic factor:

A =
σ2a
σ2

=
σ2a

σ2a + σ
2
c + σ

2
e

(5)

The proportions attributable to the common environment (C) and individual-specific environmental
effects (E) are computed analogously. Standard errors reported in the tables are bootstrapped with
1,000 repetitions.

28



Table 1
Summary Statistics: Individual Characteristics

Panel A: Number of Twins by Zygosity and Gender

All Twins
Male Female Total Male Female Sex Total

Number of twins (N ) 34,976 4,496 5,994 10,490 5,064 6,300 13,122 24,486

Percentage 100% 13% 17% 30% 14% 18% 38% 70%

Panel B: Individual Characteristics 

All Twins
N Mean Median Std. Dev. Mean Median Std. Dev.

34,976 47.08 48.00 17.64 53.06 55.00 15.51
34,976 22% 0% 41% 26% 0% 44%
34,976 58% 100% 49% 47% 0% 50%
34,976 6% 0% 23% 6% 0% 24%
34,976 46% 0% 50% 54% 100% 50%
34,976 87,554 39,661 210,778 103,892 51,634 480,174
34,976 31,305 25,443 24,944 34,797 27,563 33,425

College or More
High School
Age

Identical Twins Fraternal Twins

Identical Twins Fraternal Twins

Married
Net Worth (USD)
Disposable Income (USD)

No Education Data Available

Table 1 reports summary statistics for the individuals (Panel A) in our data set and their characteristics (Panel B).  The variables are defined 
in detail in Table 2. 



Table 2
Variable Definitions

Category / Variable Description

Individual Characteristics
Male An indicator variable that equals 1 if an individual is male, and 0 otherwise. Data from 

Statistics Sweden.
Age Average age across the years an individual is in the data set.  Data from Statistics 

Sweden.
College or More An indicator variable that equals 1 if an individual has attended university, and 0 

otherwise.  Data from Statistics Sweden.
High School An indicator variable that equals 1 if an individual has completed high school 

("gymnasium"), and 0 otherwise.  Data from Statistics Sweden.
No Education Data Available An indicator variable that equals 1 if no educational data are available for an individual, 

and 0 otherwise.  Data from Statistics Sweden.
Married Average of an annual indicator variable that equals 1 if an individual is married, and 0 

otherwise.  Averaged across those years an individual is in the data set.  Data from 
Statistics Sweden.

Disposable Income Average individual disposable income, i.e., the sum of income from labor, business, and 
investment, plus received transfers, less taxes, and alimony payments.  Averaged across 
those years an individual is in the data set.  Expressed in nominal Swedish Krona (SEK).  
Data from Statistics Sweden.

Net Worth Average difference between the end-of-year market value of an individual's assets and 
liabilities.  Averaged across those years an individual is in the data set.  Expressed in 
nominal Swedish Krona (SEK), unless stated otherwise.  Data from Statistics Sweden.

Share in Equities The market value of direct and indirect equity investments divided by the market value of 
all financial assets. Data from Statistics Sweden.

Investment Style
P/E The value-weighted price to earnings ratio for each year, averaged across those years an 

individual is in the data set.  Data from CapitalIQ.
P/B The value-weighted market value to book value of equity ratio for each year, averaged 

across those years an individual is in the data set.  Data from CapitalIQ.
Morningstar's Value-Growth 
Score

Morningstar's score of value/growth from -100 (value) to +400 (growth).  The variable is 
value-weighted for each year and averaged across those years an individual is in the data 
set.

Name-based Value/Growth 
Measure

A variable that equals -1 if a fund's name contains "value," equals +1 if a fund's name 
contains "growth" or "high tech," and zero otherwise.  The variable is value-weighted for 
each year and averaged across those years an individual is in the data set.

Experience and Events
Depression Baby An indicator variable that equals 1 if a individual was born 1920-1929, and otherwise 0.
Individual GDP Growth 
Experience

The average of GDP growth in Sweden from an individual's birth to year 2000.

Individual US (Sweden) HML 
Experience

The average of Fama-French's US (Sweden) HML return from the year an individual 
entered the job market for the first time (if after 1927 (1975)) to year 2000.

First Job in Recession A "Recession Year" is a year with negative GDP growth.  The variable is an indicator 
variable that equals 1 if an individual entered the job market for the first time during a 
recession year +/- 1 year, and 0 otherwise.

First Job in Severe Recession The variable is an indicator variable that equals 1 if an individual entered the job market 
for the first time during World War I, the Great Depression, or World War II, and 0 
otherwise.

Parents' Net Worth An individual's parents' net worth.
Depression Baby Parents An indicator variable that equals 1 if both parents of an individual were born 1920-1929, 

and 0 otherwise.



Table 3
Summary Statistics: Investment Style Measures

Panel A: Summary Statistics

N Mean Median Std. Dev. Mean Median Std. Dev.
Stocks 
P/E 17,952 24.2 22.8 14.6 23.0 21.3 13.1
P/B 18,337 3.3 2.8 2.4 3.2 2.5 2.3
Mutual Funds
Morningstar's Value-Growth Score 25,729 156.4 149.2 21.4 155.2 148.6 21.0
Name-Based Value/Growth Measure 27,397 0.09 0.00 0.21 0.08 0.00 0.20

Panel B: Correlations
(N  = 13,075)

P/E P/B
Morning-

star
P/B 0.45***

Morningstar's Value-Growth Score 0.07*** 0.07***

Name-Based Value/Growth Measure 0.04*** 0.30*** 0.53***

Identical Twins Fraternal Twins

Table 3 reports summary statistics for the measures of investment style.  Panel A shows the 
summary statistics and Panel B shows the correlations. The variables are defined in detail in 
Table 2. 



Table 4
Evidence from Variance Decomposition of Investment Style: Stocks

P/E P/B

A Share 0.181** 0.241**
0.076 0.118

C Share 0.101* 0.080
0.056 0.096

E Share 0.718*** 0.679***
0.030 0.046

N 10,618 10,640

Table 4 reports results from maximum likelihood estimation. The different investment style 
measures are modeled as linear functions of observable individual characteristics (gender, 
age, education, marriage status, disposable income, and net worth) and unobservable 
random effects representing additive genetic effects (A), shared environmental effects (C), 
as well as an individual-specific error (E). For each estimated model, we report the 
variance fraction of the residual explained by each unobserved effect (A Share – for the 
additive genetic effect, C Share – for common environmental effect, E Share – for the 
individual-specific environmental effect) as well as the bootstrapped standard errors (1,000 
resamples).  The variables are defined in detail in Table 2. 
 



Table 5
Evidence from Variance Decomposition of Investment Style: Mutual Funds

Morningstar's Value-Growth Score Name-Based Value/Growth Measure

A Share 0.249*** 0.159**
0.026 0.065

C Share 0.000 0.036
0.009 0.040

E Share 0.751*** 0.805***
0.022 0.031

N 17,534 17,650

Table 5 reports results from maximum likelihood estimation. The different investment style measures are 
modeled as linear functions of observable individual characteristics (gender, age, education, marriage status, 
disposable income, and net worth) and unobservable random effects representing additive genetic effects (A), 
shared environmental effects (C), as well as an individual-specific error (E). For each estimated model, we 
report the variance fraction of the residual explained by each unobserved effect (A Share – for the additive 
genetic effect, C Share – for common environmental effect, E Share – for the individual-specific 
environmental effect) as well as the bootstrapped standard errors (1,000 resamples).  The variables are 
defined in detail in Table 2. 



Table 6
Cross-Sectional Evidence: Investment Style and Proxies for Risk Preferences

(1) (2) (3) (4) (5) (6) (7) (8)
Male 0.88*** 0.41*

0.21 0.22
Age -0.07*** -0.11***

0.01 0.01
College or More 3.56*** 2.25***

0.28 0.31
High School 1.11*** 0.67**

0.31 0.32
No Education Data Available 0.6 1.73***

0.43 0.48
Married 0.16 0.66***

0.22 0.23
Log (Disposable Income) 0.78*** 2.03***

0.16 0.20
Log (Net Worth) 0.04 0.14***

0.03 0.04
Share in Equities 3.28*** 2.96***

0.3 0.29
Constant 22.85*** 26.97*** 21.26*** 23.24*** 23.28*** 13.71*** 21.44*** -1.08

0.15 0.45 0.23 0.17 0.12 2.06 0.19 2.24
N 17,952 17,952 17,952 17,951 17,952 17,943 17,009 17,001
R-squared 0.00 0.01 0.01 0.00 0.00 0.00 0.01 0.03

Table 6 reports results from linear regressions of P/E ratio onto individual characteristics. For each estimated 
model, we report the coefficient estimates as well as the corresponding standard errors. Robust standard 
errors are adjusted based on correlation between twins in a twin pair.  The variables are defined in detail in 
Table 2. 



Table 7
Life Course Theory and Investment Style: Macroeconomic Experiences

(1) (2) (3) (4) (5)

Depression Baby -2.17*** -1.79***
0.53 0.57

Individual GDP Growth Experience 1.23*
0.68

Individual US HML Experience 0.00
0.04

Individual Sweden HML Experience 0.04
0.03

Constant 2.12 16.35** -0.48 3.48 11.92
2.17 6.75 2.61 2.31 5.13

Sample All Born before 
1940

All First Job 
after 1927

First Job 
after 1975

N 17,943 4,774 17,943 16,462 5,335
R-squared 0.02 0.05 0.02 0.02 0.01

Table 7 reports results from linear regressions of P/E ratio onto macroeconomic experience 
variables as well as observable individual characteristics (gender, age, education, marriage status, 
disposable income, and net worth). For each estimated model, we report selected coefficient 
estimates as well as the corresponding standard errors. Robust standard errors are adjusted 
based on correlation between twins in a twin pair.  The variables are defined in detail in Table 2. 
 
 



Table 8
Life Course Theory and Investment Style: Impressionable Years
(N  = 16,462)

(1) (2) (3)
First Job in Recession -0.30

0.33

First Job in Severe Recession -2.18***
0.57

18-25 Years Old in Severe Recession -3.83***
0.71

Constant 3.58 3.87* 3.86*
2.32 2.32 2.31

N 16,462 16,462 16,462

R-squared 0.02 0.02 0.02

Table 8 reports results from linear regressions of P/E ratio onto impressionable years 
variables as well as observable individual characteristics (gender, age, education, marriage 
status, disposable income, and net worth). For each estimated model, we report selected 
coefficient estimates as well as the corresponding standard errors. Robust standard errors 
are adjusted based on correlation between twins in a twin pair.  The variables are defined in 
detail in Table 2. 
 



Table 9
Life Course Theory and Investment Style: Rearing Environment
(N  = 8,101)

(1) (2) (3) (4)
Log (Parents' Net Worth) 0.26** 0.27** 0.30*** 0.30***

0.11 0.11 0.11 0.11

Depression Baby Parents -1.33*** -1.20**
0.47 0.50

Constant 5.12 4.94 4.74 4.69
3.59 4.73 3.59 4.73

Birth Cohort (Decade) Fixed Effects No Yes No Yes
R-squared 0.02 0.02 0.02 0.02

Table 9 reports results from linear regressions of P/E ratio onto rearing environment variables 
observable individual characteristics (gender, age, education, marriage status, disposable income, 
and net worth). For each estimated model, we report selected coefficient estimates as well as the 
corresponding standard errors. Robust standard errors are adjusted based on correlation between 
twins in a twin pair.  The variables are defined in detail in Table 2. 
 
 



0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

P/E P/B

Figure 1 Panel A
Correlations by Genetic Similarity (Stocks)

Identical Twins Fraternal Twins Fraternal Twins‐ Same Sex Fraternal Twins ‐ Opposite Sex

Figure 1 Panel A reports Pearson correlation coefficients for investment style for different types of twin pairs.  The investment style 
measures are calculated using direct stock holdings only.  All variables are defined in Appendix Table A1. 



0.00

0.05

0.10

0.15

0.20

0.25

0.30

Morningstar's Value‐Growth Score Name‐Based Value/Growth Measure

Figure 1 Panel B
Correlations by Genetic Similarity (Mutual Funds)

Identical Twins Fraternal Twin Fraternal Twins‐Same Sex Fraternal Twins‐Opposite Sex

Figure 1 Panel B reports Pearson correlation coefficients for investment style for different types of twin pairs.  The investment style 
measures are calculated using mutual fund holdings only.  All variables are defined in Table 2.  


	all tables V4_20131214.pdf
	1.pdf
	2.pdf
	3.pdf
	4.pdf
	5.pdf
	6.pdf
	7.pdf
	8.pdf
	9.pdf
	10.pdf