Who are the Value and Growth Investors?∗
Sebastien Betermier, Laurent E. Calvet, and Paolo Sodini
First Version: October 2013This Version: April 2014

Abstract
This paper investigates the determinants of value and growth investing in a large administrative panel of Swedish residents over the 1999-2007 period. We document strong relationships
between a household’s portfolio tilt and the household’s financial and demographic characteristics. Value investors have higher financial and real estate wealth, lower leverage, lower
income risk, lower human capital, and are more likely to be female than the average growthinvestor. Households actively migrate to value stocks over the life-cycle and, at higher frequencies, dynamically offset the passive variations in the value tilt induced by market movements.
We verify that these results are not driven by cohort effects, financial sophistication, biases
toward popular or professionally close stocks, or unobserved heterogeneity in preferences. We
relate these household-level results to some of the leading explanations of the value premium.

∗ Betermier: Desautels Faculty of Management, McGill University, 1001 Sherbrooke St West, Montreal, QC H3A

1G5, Canada, sebastien.betermier@mcgill.ca. Calvet: Department of Finance, HEC Paris, 1 rue de la Libération,
78351 Jouy-en-Josas Cedex, France; calvet@hec.fr. Sodini: Department of Finance, Stockholm School of Economics,
Sveavägen 65, Box 6501, SE-113 83 Stockholm, Sweden, Paolo.Sodini@hhs.se. We thank Per Östberg for a helpful
discussion and acknowledge insightful comments from Laurent Barras, John Campbell, Chris Carroll, Luigi Guiso,
Marcin Kacperczyk, Bige Kahraman, Alex Michaelides, Ben Ranish, David Robinson, Johan Walden, and seminar
participants at HEC Montréal, HEC Paris, Imperial College Business School, McGill University, the Norges BankHousehold Finance Workshop, the Swedish School of Economics, and the University of Helsinki. We thank Statistics
Sweden and the Swedish Twin Registry for providing the data. The project benefited from excellent research assistanceby Milen Stoyanov, Pavels Berezovkis, and especially Andrejs Delmans. This material is based upon work supported
by Agence Nationale de la Recherche, BFI, the HEC Foundation, Riksbank, the Social Sciences and Humanities
Research Council of Canada, and the Wallander and Hedelius Foundation.

Electronic copy available at: http://ssrn.com/abstract=2426823

Who are the Value and Growth Investors?
A BSTRACTThis paper investigates the determinants of value and growth investing in a large administrative panel of Swedish residents over the 1999-2007 period. We document
strong relationships between a household’s portfolio tilt and the household’s financial
and demographic characteristics. Value investors have higher financial and real estate
wealth, lower leverage, lower income risk, lower human capital, and are more likely
to be female than the average growth investor. Households actively migrate to value
stocks over the life-cycle and, at higher frequencies, dynamically offset the passive
variations in the value tilt induced by market movements. We verify that these results
are not driven by cohort effects, financial sophistication, biases toward popular or professionally close stocks, or unobserved heterogeneity in preferences. We relate thesehousehold-level results to some of the leading explanations of the value premium.

JEL Classification: G11, G12.
Keywords: Asset pricing, value premium, household finance, portfolio allocation, human capital.

Electronic copy available at: http://ssrn.com/abstract=2426823

1 Introduction
A large academic and practitioner literature documents that value stocks outperform growth stocks
on average in the United States (Basu 1977, Fama and French 1992, Graham and Dodd 1934) andaround the world (Fama and French 1998).1 The economic explanation of these findings is one of
the central questions of modern finance. The value premium may be a compensation for forms ofsystematic risk other than market portfolio return risk (Fama and French 1992), such as recession
risk (Cochrane 1999, Jagannathan and Wang 1996), cash-flow risk (Campbell and Vuolteenaho
2004, Campbell, Polk, and Vuolteenaho 2010), long-run consumption risk (Hansen, Heaton, andLi 2008), or the costly reversibility of physical capital and countercyclical risk premia (Zhang
2005). The underperformance of growth stocks relative to value stocks may also be evidence thatinvestors are irrationally exuberant about the prospects of innovative glamour companies (Daniel,
Hirshleifer, and Subrahmanyam 2001, DeBondt and Thaler 1985, La Porta, Lakonishok, Shleifer,
and Vishny 1997).
The extensive empirical literature on the origins of the value premium focuses primarily on
stock returns and their relationships to macroeconomic and corporate data. Disentangling theoriesof the value premium, however, has proven to be challenging on traditional data sets that do notprovide individual trades and therefore do not permit to assess the determinants of investor decisions. In this paper, we propose to use the rich information in investor portfolios to shed lighton theoretical explanations of the value premium. We make a first step in this direction by investigating value and growth investing in a highly detailed administrative panel, which contains thedisaggregated holdings and socioeconomic characteristics of all Swedish residents between 1999
and 2007. The data set reports portfolio holdings at the level of each stock or fund, along withother forms of wealth, debt, labor income, and employment sector. We document strong empirical
relationships between a household’s socioeconomic characteristics and the household’s tilt toward
value or growth stocks. We also uncover new empirical patterns in the dynamics of the portfolio tilt
at the yearly and life-cycle frequencies, and relate our various findings to theoretical explanationsof the value premium.
The paper makes four main contributions to the literature. First, we show that the value tilt
1 See also Asness, Moskowitz, and Pedersen (2013), Ball (1978), Basu (1983), Chan, Hamao, and Lakonishok(1991), Fama and French (1993, 1996, 2012), Liew and Vassalou (2000), and Rosenberg, Reid, and Lanstein (1985).

1Electronic copy available at: http://ssrn.com/abstract=2426823

exhibits substantial heterogeneity across households. When we sort investors by the value tilt of
their risky asset portfolios, the difference in expected return is about 10% per year between the
top and bottom deciles. We document that the value tilt of household portfolios is strongly related
to financial and demographic characteristics. Value investors are substantially older, tend to havehigher financial wealth, higher real estate wealth, lower leverage, lower income risk, lower humancapital, and are also more likely to be female, than the average growth investor. By contrast,
males, entrepreneurs, and educated investors are more likely to invest in growth stocks. Thesebaseline patterns are evident both in the direct stockholdings and the mutual fund holdings ofhouseholds. Quite strikingly, new entrants exhibit the same strong relationships between the value
tilt and characteristics, even though the portfolios of new entrants are formed for the first time andcannot be impacted by past stock market investment decisions. The baseline results are also robust
to controlling for the length of risky asset market participation and other measures of financial
sophistication. Furthermore, the explanatory power of socioeconomic characteristics is especiallyhigh among the minority of households with direct investments in more than four companies, awealthy group that own the bulk of aggregate equity.
Second, we provide evidence that households actively manage their holdings of growth andvalue stocks. We report that households dynamically rebalance their exposure to the value factor
in response to passive variation in the portfolio tilt at the yearly frequency. At longer horizons,households climb the “value ladder” over the life-cycle, that is they gradually shift from growth tovalue investing as they become older, wealthier, less levered, and less dependent on human capital.
Similar patterns hold for new participants, whose initial portfolios are not passively affected bypast market returns.
Third, we document that the relationship between the portfolio tilt and investor characteristics
is unlikely to be driven by a bias toward popular or familiar stocks. Consistent with international
evidence, the median Swedish household directly invests in a small number of equities. Acrosshouseholds, direct stockholdings tend to concentrate in a limited set of popular firms, toward whichless wealthy and less educated households are especially drawn. We document, however, that theset of popular firms contains a mix of growth and value stocks, and that household portfoliosof popular firms have heterogeneous tilts. We verify that the baseline relationships between thevalue loading and characteristics hold strongly in household portfolios of popular stocks, as wellas in their portfolios of nonpopular stocks. Moreover, earlier research shows that investors have a2

propensity to invest in firms that are known to them through their jobs or neighbors (Døskeland and
Hvide 2011, Huberman 2001, Massa and Simonov 2006). Consistent with the Norwegian experience (Døskeland and Hvide 2011), professionally close stocks account in Sweden for an averageof 16% of direct stockholdings and have substantially heterogeneous weights across households.
The baseline relationships between the value tilt and characteristics, however, are observed both in
the portfolios of professionally close stocks and in the portfolios of other stocks.
Fourth, we verify the good identification of our results. As in Calvet and Sodini (2014), we use
the subsample of Swedish twins to control for latent fixed effects, such as the impact of upbringing,inheritance, or attitudes toward risk. Socioeconomic characteristics have similar coefficients in thetwin subsample with yearly twin pair fixed effects as in the general household population. The
explanatory power of the regression is of course considerably higher for twins. We also consider
various subsamples of households, such as new participants, frequently and infrequently communicating twins, public sector employees, or households employed in growth firms and value firms,
and report that investment styles are strongly related to financial and demographic characteristics
in all subsamples. Furthermore, we provide evidence that our baseline results are unlikely to be
explained by a reverse causality between wealth and the value loading, or by the misspecificationof the income process.
The present paper contributes to the growing literature on the relationship between retail investor demand and stock characteristics. Earlier research shows that individual investors prefer stocks that are familiar (Huberman 2001), geographically and culturally close (Grinblatt and
Keloharju 2001), attention-grabbing (Barber and Odean 2008), or connected to products they consume (Keloharju, Knüpfer, and Linnainmaa 2012). The dynamics of investment styles chosen
by retail investors have also been related to certain types of news and past experience (Kumar
2009, Campbell, Ramadorai, and Ranish 2014). The Swedish panel contains exceptionally highquality data on individual holdings and socioeconomic characteristics, and allows us to uncover
new micro-level patterns in the demand for value and growth stocks from the household sector.
Our portfolio results complement the extensive asset-pricing literature on the value premium,
which focuses on stock valuations, corporate data, and aggregate investor forecasts. In particular,
we provide household-level support for several leading explanations of the cross-section of returns.
Value investors tend to be older than the average participant and have low human capital, low

3income risk, low leverage, and high financial wealth. These regularities are strikingly consistent
with risk-based theories, including the life-cycle implications of the hedging motive (Merton 1973)
and the high sensitivity of value stocks to deep recessions (Campbell, Giglio, and Polk 2013,
Cochrane 1999). Other empirical regularities documented in the paper can receive complementaryrisk-based and psychological explanations. For instance, the tilt of entrepreneurs toward growthstocks can be attributed both to a high exposure to business risk (Moskowitz and Vissing-Jørgensen
2002) and a high degree of overconfidence in their decision-making skills (Busenitz and Barney1997). Overconfidence is more prevalent among men than women (Barber and Odean 2001), andcan therefore explain the preference of male investors for growth stocks. Furthermore, a majorityof direct stockholders hold a small number of popular stocks, as attention theory predicts (Barberand Odean 2008).
The Swedish data set provides highly detailed information on household financial and demographic characteristics but is somewhat less informative about behavioral biases. With this caveat,a notable conclusion of our study is that the financial circumstances of households impact theirvalue and growth investments in accordance with the predictions of risk-based theories. Wealthy
investors with safe incomes and sound balance sheets consciously hold systematic risk other thanmarket portfolio risk because they are in the best position to do so and wish to earn the value premium. Furthermore, socioeconomic variables have higher explanatory power for wealthy investors
owning stocks in more than four firms, which suggests that these relationships are conscious andrationally motivated.
The evidence reported in this paper complements the growing body of work showing that retailinvestors tend to follow the precepts of portfolio theory. Households are known to select a low
share of risky assets in their liquid financial portfolios if their labor income is risky, as measured
by self-employment (Heaton and Lucas 2000) or income volatility (Betermier, Jansson, Parlour,and Walden 2012, Calvet and Sodini 2014, Guiso, Jappelli, and Terlizzese 1996).2 Conversely,
households choose an aggressive risky share if they have high financial wealth and high humancapital (Calvet and Sodini 2014). Furthermore, a majority of investors incur modest welfare losses
due to underdiversification (Calvet, Campbell, and Sodini 2007) and actively rebalance these portfolio’s shares of liquid financial wealth in response to realized asset returns (Calvet, Campbell,
and Sodini 2009a). The present paper documents that financial theory also accounts for the cross2 See Bonaparte, Korniotis, and Kumar (2013) and Knüpfer, Rantapuska, and Sarvimäki (2013).

4

sectional and time-series properties of household portfolio value tilts.
Finally, the paper sheds light on the potential relationship between genes and value investing.
In a recent contribution, Cronqvist, Siegel, and Yu (2013) estimate a variance decomposition of
the investment decisions made by identical and fraternal twins, and infer that value investing has
a strong genetic component. The present paper replicates these results but also demonstrates theirhigh sensitivity to the frequency of communication between twins. In particular, the so-called genetic component disappears almost entirely among infrequent communicators, which suggests thatthe variance decomposition severely overestimates the impact of genes. A growing literature in
genetics, medicine, and experimental psychology documents substantial interactions between nature and nurture (Ridley 2003), and our findings confirm the fragility of statistical decompositions
that ignore these interactions. The empirical evidence in this paper indicates that value and growthinvesting are not simply encoded in the DNA of retail investors, but are also strongly driven by
their financial circumstances and interpersonal communication.
The rest of the paper is organized as follows. Section 2 reviews the portfolio implications ofsome of the leading explanations of the value premium. Section 3 presents the data and reportssummary statistics. Section 4 investigates the characteristics of value and growth investors. Section 5 discusses the economic implications of these findings. In Section 6, we show that investors
actively rebalance passive variation in their exposure to the value factor, and, at lower frequencies, actively migrate to value stocks over the life-cycle. Section 7 concludes. A supplementary
Internet Appendix (Betermier, Calvet, and Sodini 2014) presents details of data construction andestimation methodology, and reports additional results.

2 Theoretical Motivation
In this section, we review some of the leading theories of the value premium and discuss theirimplications for portfolio choice.

52.1 Determinants of the Value Premium
The value premium is one of the best documented facts in asset pricing, which has proven to
be remarkably persistent over time and across markets.3 These strong empirical findings havereceived a number of theoretical explanations.
2.1.1 Systematic Risk
Fama and French (1992, 1995) propose that the value premium is a compensation for a formof systematic risk other than market portfolio return risk. Several possibilities have been considered for the precise nature of this alternative risk (Cochrane 1999). Unlike growth stocks,
value stocks exhibit high sensitivity to aggregate labor income and consumption shocks. Conditional versions of the CAPM based on these variables have therefore had success in explaining the value premium (Jagannathan and Wang 1996, Lettau and Ludvigson 2001, Petkova andZhang 2005, Yogo 2006).4 Value stocks are also highly exposed to long-run macroeconomicrisk (Bansal, Dittmar, and Lundblad 2005, Gulen, Xing, and Zhang 2011, Hansen, Heaton, andLi 2008).5
The excess returns of value stocks over growth stocks are informative about changes in investment opportunities. Under the Intertemporal Capital Asset Pricing Model (ICAPM, Merton 1973),
a factor that forecasts the distribution of future returns also explains the cross-section of risk premia, as Campbell (1996) emphasizes. Good realizations of the factor are associated with an improvement in investment opportunities, so that assets with a negative loading on the factor provide
a hedge against worsening investment opportunities. Consistent with the logic of the ICAPM, goodrealizations of the value factor predicts high aggregate returns (Campbell and Vuolteenaho 2004)
and economic growth (Liew and Vassalou 2000) in U.S. and international data. Growth stocks can3 See for instance Asness, Moskowitz, and Pedersen (2013), Capaul, Rowley, and Sharpe (1993), Fame and French(1998, 2012), Griffin (2003), and Liew and Vassalou (2000). Some recent work also shows that the strength of thevalue premium can be improved by refining the sorting methodology (Asness and Frazzini 2013, Barras 2013, Hou,
Karolyi, and Kho 2011).
4 Eiling (2013), Jagannathan, Kubota, and Takehara (1998), Addoum, Korniotis, and Kumar (2013), and Santos
and Veronesi (2006) provide further evidence on the relationship between labor income and the value premium.
5
Other forms of countercyclical risk can contribute to explaining the value premium. For instance, the variance ofidiosyncratic labor income risk is high during recessions (Storesletten, Telmer, and Yaron 2004) and value stocks tend
to provide low dividends when the aggregate housing collateral is low (Lustig and van Nieuwerburgh 2005). Thesemechanisms motivate investors to require a premium in order to hold these stocks.

6therefore act as a hedge against low aggregate risk premia.
Fundamentals explanations of the value premium are supported by decompositions of market
portfolio returns into cash-flow news and discount-rate news (Campbell and Vuolteenaho 2004).
Value stocks have considerably higher exposure to the market’s cash-flow risk (bad beta) and lower
exposure to the market’s discount-rate risk (good beta) than growth stocks. In particular, value
stocks are strongly exposed to deep recessions and the persistent reductions in aggregate cash
flows that they entail (Campbell, Giglio, and Polk 2013). The poor performance of value strategies
during the Great Financial Crisis provides recent evidence that value strategies are indeed highly
exposed to deep recession risk. Furthermore, the value loadings of individual stocks are primarily
driven by their own cash flows, which confirms that the value premium is rooted in fundamentals(Campbell, Polk, and Vuolteenaho 2010). Overall, the empirical asset-pricing evidence suggeststhat value stocks are exposed to forms of systematic risk other than market portfolio return risk,
which can explain, at least partly, the value premium.
2.1.2 Timing of Cash Flows and Production Risks
The different sensitivities of value and growth stocks to aggregate conditions can be explained by
the timing of their cash flows and the dynamics of their production processes. It is for instancewell known that value stocks have shorter durations than growth stocks (Cornell 1999, Dechow,
Sloan, and Soliman 2004). Consequently, value stocks exhibit low sensitivity to discount-rate risk
and high sensitivity to cash-flow risk (Lettau and Wachter 2007), which is consistent with theempirical evidence in Campbell and Vuolteenaho (2004).
In addition, structural production-based asset pricing models have had success in relating thesensitivity of a firm’s traded equity to the firm’s physical assets and growth options (Berk, Green,
and Naik 1999, Gomes, Kogan, and Zhang 2003). Cutting physical capital in bad times entails
more adjustment costs that expanding physical capital in good times. Assets in place are therefore
riskier than growth options, especially in bad times when the price of risk is high. As a result,value stocks are more sensitive than growth stocks to the business cycle (Zhang 2005).6 Human
capital is a key complement of physical capital in the production process and is known to explain
6 Related channels include operational leverage (Carlson, Fisher, and Giammarino 2004), investment-specific tech-

nology (Kogan and Papanikolaou 2012), and the cyclicality of the demand for durable goods (Gomes, Kogan, andYogo 2009).

7

the value premium in a conditional CAPM context (Jagannathan and Wang 1996). For this reason, researchers have recently developed structural asset-pricing models that explicitly incorporate
human capital (Garleanu, Kogan, and Panageas 2012, Parlour and Walden 2011). Sylvain (2013)
develops a general equilibrium model with both human and physical capital investment and showsthat value stocks endogenously exhibit a high sensitivity to human capital risk.
2.1.3 Cognitive Biases
The success of value investing can also originate from the exuberant overpricing of growth stocks
and underpricing of value stocks by irrational investors (DeBondt and Thaler 1985, Lakonishok,
Shleifer, and Vishny 1994, La Porta, Lakonishok, Shleifer, and Vishny 1997, Shleifer 2000). Thesemistakes can be explained by the representativeness heuristic uncovered in the psychological literature, that is the tendency to pay more attention to recent events than Bayesian updating would
imply (Kahneman and Tversky 1973). In the context of equity markets, companies that have recently performed well tend to be overpriced growth or “glamour” stocks, while companies thathave recently performed poorly tend to be underpriced value stocks. Overconfidence, that is thetendency to overestimate the accuracy of available information, is a complementary explanationof the cross-section of returns. Overconfident investors overprice stocks following positive news
and underprice stocks following negative news, so that valuation ratios can predict future returns(Daniel, Hirshleifer, and Subrahmanyam 2001). These behavioral interpretations are consistent
with biases in stock analyst expectations (La Porta 1996, La Porta, Lakonishok, Shleifer, and
Vishny 1997, Greenwood and Sheifer 2013, Skinner and Sloan 2002) and with the pricing impact
of measures of investor sentiment (Baker and Wurgler 2006).

2.2 Portfolio Implications
Risk-based and behavioral explanations of the value premium have important implications for
portfolio choice. The topic, however, has remained relatively unexplored until now, presumably
because of the technical challenges involved and the complex nature of the value premium. We
now summarize existing results and conjecture possible relationships when formal results are not
yet available.

8Consistent with the ICAPM, the value factor forecasts future aggregate returns, so investors
can use growth stocks to hedge against adverse variations in future investment opportunities while
earning lower expected returns. Since the hedging motive is stronger for investors with longer
horizons, young households should pick growth stocks, while mature households should pick value
stocks. Lynch (2001), Jurek and Viceira (2011), and Larsen and Munk (2012) demonstrate thevalidity of this logic for a finite-horizon investor with constant relative risk aversion.
The optimal portfolio of risky assets may be impacted by household characteristics other thanage. Since value stocks carry systematic risk other than market portfolio return risk, intuition
suggests that high book-to-market ratio stocks should be picked by investors who have a strong
capacity to bear risk, such as investors with high liquid financial wealth, high real estate wealth,and low leverage. Conversely, growth stocks should be picked by investors with a limited capacity
to bear systematic risk, for instance because they own low financial and real estate wealth or havehigh leverage. Such tilts naturally arise in a habit-formation model with time-varying opportunities(Munk 2008). The hedging demand then depends on an effective risk aversion coefficient driven
by wealth and consumption habit, and, as a result, the optimal loading on the value factor is anincreasing function of wealth.
Human capital represents a large fraction of the overall wealth of most individuals, but its theoretical impact on the value tilt has not been widely studied. Intuition suggests that conflicting
forces drive the relationship between human capital and the value tilt. On the one hand, to theextent that it is a safe form of wealth, human capital increases the financial security of households
and should therefore tilt the risky asset portfolio toward value stocks. On the other hand, if income
is sensitive to recession risk, human capital should reduce the value tilt of the risky asset portfolio.
In his Presidential Address to the American Finance Association, Cochrane (2011) gives the following explanation of the value premium: “If a mass of investors has jobs or businesses that will
be hurt especially hard by a recession, they avoid stocks that fall more than average in a recession.”
Value stocks should therefore be held by investors with relatively safe jobs, while growth stocks
should be held by individuals with recession-sensitive incomes, such as entrepreneurs and small
business owners.
Two additional mechanisms may link human capital to the value tilt. First, the wage income ofrich households is highly exposed to aggregate fluctuations (Parker and Vissing-Jørgensen 2009,92010). Households with high human capital should therefore tilt their portfolios toward growthstocks. Second, aggregate human capital is positively correlated with aggregate physical capitalat the macro level (Baxter and Jermann 1997). If human capital and physical capital are alsorisk substitutes at the micro level, households with substantial human capital should allocate theirfinancial wealth away from the physical capital risk embedded in value stocks, and should instead
aggressively invest in growth stocks.
Cognitive biases have a number of important implications for portfolio choice. Consider for
instance the assumption that the superior performance of value stocks is due to expectational errors, such as representativeness heuristic and overconfidence.7 The psychology literature documents that cognitive biases tend to attenuate with experience in sufficiently regular environments(Hogarth 1987, Kahneman 2011, Oskamp 1965).8 To the extent that the relative performanceof value and growth stocks is sufficiently regular to be learned, households with longer financial market experience should be less prone to cognitive biases and exhibit a stronger tilt toward
value stocks. Moreover, overconfidence is generally more pronounced among men (Barber andOdean 2001) and entrepreneurs (Busenitz and Barney 1997, Cooper, Woo, and Dunkelberg 1988),
which suggest that men and entrepreneurs should favor growth stocks. In the next sections, we test
the portfolio implications of these theories on Swedish portfolio data.

3 Data and Construction of Variables
This section presents the security and household data, and defines the main variables used throughout the paper.

3.1 Local Fama and French Factors
We use stock market data for the 1985 to 2009 period provided by FINBAS, a financial databasemaintained by the Swedish House of Finance. The data include monthly stock returns, market
capitalizations at the semiannual frequency, and book values at the end of each year. We also use
Datastream to compute free-float adjusted market shares.
7 See Barberis and Thaler (2003) for a review.
8

Malmendier and Nagel (2011) provide some evidence that younger or less experienced investors are especiallylikely to extrapolate from recent financial data.

10

We focus on stocks with at least 2 years of available data. We exclude stocks worth less than
1 krona, which filters out very small firms. For comparison, the Swedish krona traded at 0.1371
U.S. dollar on 30 December 2003. We end up with a universe of approximately 1,000 stocks, outof which 743 are listed on one of the four major Scandinavian exchanges in 2003.9 The returnon the market portfolio is proxied by the SIX return index (SIXRX), which tracks the value of all
the shares listed on the Stockholm Stock Exchange. The risk-free rate is proxied by the monthly
average yield on the one-month Swedish Treasury bill. The excess return between the market
portfolio and the risk-free rate defines the market factor MKTt .
The local value, size, and momentum factors are constructed by following the methodology ofFama and French (1993) and Carhart (1997). We sort the stocks traded on the major exchanges
according to their book-to-market values, market size and past returns. We then compute the value
factor HMLt , the size factor SMBt , and the momentum factor MOMt in months t = 1 · · · , T , as isfully explained in the Internet Appendix.
We index stocks and funds by i ∈ {1, . . . , I}, and for every asset i, we estimate the four-factor
model:
ri,t = ai + bi MKTt + vi HMLt + si SMBt + mi MOMt + ei,t ,where ri,t denotes the excess return of asset i in month t, and ei,t are uncorrelated to the factors.
Estimated loadings are winsorized at -5 and +5. The value premium is substantial in Sweden: theaverage annual return on the HML portfolio is about 10% over the 1985 to 2009 period, which isin the range of country estimates reported in Liew and Vassalou (2000).

3.2 Household Panel Data
The Swedish Wealth Registry is an administrative data set compiled by Statistics Sweden, which
has been used in earlier work (Calvet, Campbell, and Sodini 2007, 2009a, 2009b, Calvet andSodini 2014). Statistics Sweden and the tax authority had until 2007 a parliamentary mandate tocollect highly detailed information on every resident. Income and demographic variables, such asage, gender, marital status, nationality, birthplace, education, and municipality of residence, areavailable on December 31 of each year from 1983 to 2007. The disaggregated wealth data include
9 The major Scandinavian exchanges are the Stockholm Stock Exchange, the Copenhagen Stock Exchange, theHelsinki Stock Exchange, and the Oslo Stock Exchange.

11the worldwide assets owned by the resident at year-end from 1999 to 2007. Real estate, debt,
bank accounts, and holdings of mutual funds and stocks are provided for each property, accountor security. Statistics Sweden provides a household identification number for each resident, whichallows us to group residents by living units.10 The age and gender variables used in the rest of thepaper refer to the household head.
We consider households that participate in risky asset markets and satisfy the following requirements. Disposable income is strictly positive, financial wealth is at least 1,000 kronor (approximately $140), and total wealth is at least 3,000 kronor (approximately $420). The household
head, defined as the individual with the highest income, is between 25 and 85 years old. Finally,
five years of household income data are available. Unless stated otherwise, the results are based onan unbalanced, random sample of approximately 70,000 households satisfying these requirements
observed at the yearly frequency between 1999 and 2007.
We also use a panel of twins satisfying the same requirements. The Swedish Twin Registry,
which is administered by the Karolinska Institute in Stockholm, is the largest twin database in
the world. It provides the genetic relationship (fraternal or identical) of each twin pair, and theintensity of communication between the twins. The twin database also allows us to identify twin
siblings in the Swedish Wealth Registry, so that all financial and demographic characteristics areavailable in the twin panel.

3.3 Definition of Main Variables
3.3.1 Financial Portfolio
We use the following definitions throughout the paper. Cash consists of bank account balances andSwedish money market funds.11 Risky mutual funds refer to all funds other than Swedish money
market funds. Risky financial assets consist of directly held stocks and risky mutual funds. We
exclude assets for which less than 3 months of returns are available.
10

In order to protect privacy, Statistics Sweden provided us with a scrambled version of the household identification
number.
11 Financial institutions are required to report the bank account balance at year-end if the account yields less than
100 Swedish kronor during the year (1999 to 2005 period), or if the year-end bank account balance exceeds 10,000
Swedish kronor (2006 and 2007 period). We impute unreported cash balances by following the method used in Calvet,
Campbell, and Sodini (2007, 2009a, 2009b) and Calvet and Sodini (2014), as we explain in the Internet Appendix.

12For every household h, the risky portfolio contains the household’s risky financial assets. The
risky share is the fraction of risky financial assets in the household’s portfolio of cash and risky
financial assets. A market participant is a household with a strictly positive risky share.
The value loading of the risky portfolio at time t is the weighted average of the asset loadings:
I

vh,t = ∑ wh,i,t vi ,

(1)

i=1

where wh,i,t denotes the weight of asset i in household h’s risky portfolio at time t. We will also
refer to vh,t as the HML loading or the value tilt. We similarly compute the value loading of thefund and stock portfolios. The methodology captures time variation in vh,t driven by time variationin portfolio weights, while taking advantage of the long time series available for individual asset
returns. The use of an unconditional pricing model guarantees that the value tilts of individual
firms, vi , are constant over the sample period and therefore do not generate time variation in theportfolio loading, vh,t . Thus, our estimates of active management of the value tilt by households
will not be contaminated by exogenous changes in firm value tilts during the 1999 to 2007 sample
period.
3.3.2 Financial Wealth and Real Estate
We measure the household’s financial wealth at date t as the total value of its cash holdings, risky
financial assets, directly held bonds, capital insurance, and derivatives, excluding from consideration illiquid assets such as real estate or consumer durables, and defined contribution retirement
accounts. Also, our measure of wealth is gross financial wealth and does not subtract mortgage orother household debt. Residential real estate consists of primary and secondary residences, while
commercial real estate consists of rental, industrial and agricultural property. The leverage ratio isdefined as the household’s total debt divided by the household’s financial and real estate wealth.
3.3.3 Human Capital
We consider a labor income specification based on Carroll and Samwick (1997) and accounting
for the persistence of income shocks:log(Lh,t ) = ah + b′ xh,t + θh,t + εh,t ,13(2)

where Lh,t denotes real income of household h in year t, ah is a household fixed effect, xh,t is avector of age and retirement dummies, θh,t is a persistent component, and εh,t is a transitory shock
distributed as N (0, σ2ε,h ). The persistent component θh,t follows the autoregressive process:θh,t = ρh θh,t−1 + ξh,t ,where ξh,t ∼ N (0, σ2ξ,h) is the persistent shock to income in period t. The Gaussian innovations
εh,t and ξh,t are white noise and are uncorrelated with each other at all leads and lags. We conduct
the estimation separately on bins defined by (i) the immigration dummy, (ii) the gender dummy,and (iii) educational attainment. We estimate the fixed-effects estimators of ah and b in each bin,and then compute the maximum likelihood estimators of ρh , σ2ξ,h and σ2ε,h using the Kalman filteron each household income series.
In the portfolio-choice literature (e.g., Cocco, Gomes, and Maenhout 2005), it is customary
to assume that the household observes the transitory and persistent components of income. Since
the characteristics xh,t are deterministic, labor income log(Lh,t ) then has conditional stochastic
component
ηh,t = ξh,t + εh,t ,(3)and conditional varianceσ2h = Vart−1 (ηh,t ) = σ2ξ,h + σ2ε,h .
We call σh the conditional volatility of income and use it as a measure of income risk throughout
the paper.
We define expected human capital asTh

HCh,t = ∑ Πh,t,t+n
n=1

Et (Lh,t+n ),(1 + r)n(4)where Th denotes the difference between 100 and the age of household h at date t, and Πh,t,t+ndenotes the probability that the household head h is alive at t + n conditional on being alive at
t. We make the simplifying assumption that no individual lives longer than 100. The survival
probability is computed from the life table provided by Statistics Sweden. The discount rate is
set equal to r = 5% per year. We have verified that our results are robust to alternative choicesof r. Detailed descriptions of the labor income and human capital imputations are provided in theInternet Appendix.
14

3.4 Summary Statistics
Table I, Panel A, reports summary statistics on the financial and demographic characteristics ofrisky asset market participants (first set of columns), mutual fund owners (second set of columns),
direct stockholders (third set of columns), and direct stockholders sorted by the number of stocks
that they own (last set of columns). All summary statistics are computed at the end of 2003. To
facilitate comparison, we convert all financial variables into U.S. dollars using the exchange rate
at the end of 2003 (1 Swedish krona = $0.1371). The average household owning risky assets has
a 46-year old head and a yearly income of $45, 000. It owns $50, 000 in liquid financial wealth,$155, 000 in gross residential and commercial real estate wealth, and $955, 000 in human capital.
The vast majority of risky asset participants (88%) hold mutual funds, while 59% of them directly
own stocks.
Direct stockholders have on average substantially higher financial ($65, 000) and real estate
wealth ($190, 000) than general risky asset market participants. There is also considerable heterogeneity among direct stockholders. Households owning 1 or 2 stocks own modest levels of financial wealth ($35, 000). By contrast, households owning at least 5 different stocks have substantiallyhigher financial wealth ($125, 000) and education attainment than the average participant.
In Table I, Panel B, we report summary statistics on household financial portfolios. The average
participant has a risky share of 40%, owns 4 different mutual funds, and directly invests in 2 or
3 firms. These estimates are similar to the average number of stocks in U.S. household portfolios(Barber and Odean 2000, Blume and Friend 1975). The panel also shows substantial heterogeneity
across investors. Households owning directly 1 or 2 stocks have substantially lower risky sharesthan owners of more diversified stock portfolios. Concentrated stock portfolios represent a small
fraction of household financial wealth and the corresponding diversification losses are modest, as
documented in Calvet, Campbell, and Sodini (2007).
The direct investments of the household sector are concentrated in a small number of popular
stocks. Specifically, we compute the aggregate value of household direct holdings in each stock,and classify a stock as popular if it is one of the top 10 holdings in at least one year during
the 1999 to 2007 sample period. Popular stocks, which account for 59% of the Swedish stock
market, represent 71% of the average household stock portfolio in 2003. Thus, household direct
stockholdings concentrate in a small number of popular companies. Furthermore, the popular share15

is more pronounced for portfolios with one or two stocks (79%) than for portfolios with at least
five stocks (57%).
Households may favor professionally close stocks for familiarity or informational reasons. We
classify a stock as professionally close to household h if it has the same 1-digit Standard Industrial
Classification code as the employer of one of the adults in h. The average direct stockholder
allocates 16% of the stock portfolio to professionally close companies, which is rather modest andindicates that households are not heavily tilted toward stocks in their employment sector. Thisestimate is consistent with the evidence from Norway (Døskeland and Hvide 2011).
The aggregate household portfolio is constructed by adding up the stock and fund holdingsof risky asset market participants. In the bottom rows of Table I, Panel B, we report the fractionof the aggregate portfolio held by specific subsets of investors. The share of risky asset market
participants is by definition equal to unity. Households owning 5 stocks or more represent 17% of
the population of risky asset market participants but own 36% of aggregate mutual fund holdings,
54% of the aggregate risky portfolio, and 80% of aggregate direct stockholdings. Thus, householdswith at least 5 stocks play an important role in determining the aggregate household demand forrisky assets. For this reason, we will pay special attention to this wealthy subgroup in the rest of
the paper.
In Figure 1, we sort firms by market capitalization, and for each size bucket we report thefraction of the firm’s stocks owned directly by Swedish households (solid bars) and the fraction offirms in the size bucket (solid line). Households directly own 30% to 50% of firms with a market
capitalization up to 100 million U.S. dollars, and a smaller fraction of larger firms. Since small
companies represent a large fraction of the overall population of companies, the aggregate demand
from the household sector is substantial and can therefore have a sizable impact on stock prices.

4 Empirical Evidence on Value and Growth Investors
In this section, we investigate the value tilts of household portfolios in the administrative panel.
We first analyze the cross-sectional distribution of the value loading. We then document the relationships between a household’s value tilt and the household’s financial and demographic characteristics. The present section documents new empirical regularities and Section 5 relates them to16theoretical explanations of the value premium.

4.1 Cross-Sectional Distribution of the Value Loading
In Table II, we report the cross-sectional distribution of the value loading for individual stocks andhousehold portfolios at the end of 2003. Individual stocks have widely heterogeneous loadings,
ranging from -3.22 (10th percentile) to 0.94 (90th percentile). The median loading is -0.37 and theequal-weighted average loading is -0.87. The distribution of the value loading is thus negatively
skewed across individual stocks. We next consider value-weighted portfolios. The value-weighted
portfolio coincides by construction with the SIXRX index and has a value loading of -0.15 in
2003, which is substantially higher than the equal-weighted average loading of a stock.12 The
different value loadings of the equal- and value-weighted portfolios are of course explained by thelarge number of small growth stocks. The value-weighted portfolio of all Swedish mutual funds
has a loading of -0.10 in 2003, which is close to the estimate for the market index. A portfoliowith a loading between -0.15 and -0.10 in 2003 is therefore neutral relative to the Swedish market
portfolio.
Household portfolios also exhibit substantially heterogeneity in value loadings. Among risky
asset market participants, the value loading of the risky portfolio ranges from -0.94 (10th percentile) to 0.10 (90th percentile), which corresponds to a difference in expected returns of about
10% per year. The median loading is approximately neutral at -0.18, so the cross-sectional loading distribution is negatively skewed. Subgroups of investors produce relatively similar estimates.
Stock portfolios have more dispersed value loadings than risky portfolios, with estimates ranging
from -1.20 (10th percentile) to 0.39 (90th percentile). Fund portfolios are centered around theneutral benchmark and are less dispersed than risky or stock portfolios, as intuition suggests.
The aggregate risky portfolio containing all the stocks and funds owned by Swedish households has a loading of -0.26, which confirms that the household sector as a whole exhibits only amild growth tilt. Table II indicates that the aggregate mutual fund portfolio has a neutral loadingof -0.18. The slight tilt of the aggregate risky portfolio therefore originates from the aggregate
stock portfolio, which has a loading of -0.36. Moreover, whether we consider stocks or funds, the
12 As equation (1) implies, the value loading of the SIXRX index can vary from year to year because the universe oflisted stocks changes over time and the value loadings of individual stocks are time-invariant over the period.

17equal-weighted average household has a stronger growth tilt than that its wealth-weighted counterpart. A natural explanation is that low-wealth households invest in growth stocks, while wealthierhouseholds invest in value stocks. We test this explanation in the next section.

4.2 What Drives the Value Tilt?
Table III maps the relationships between portfolio tilts and socioeconomic variables. We estimate
pooled regressions of a household’s value loading on the household’s characteristics and year,
industry, and county fixed effects. The industry fixed effect is the 2-digit Standard Identification
Code of the household head. We compute the value loading at the level of the risky portfolio incolumn (1), the stock portfolio in column (2), and the fund portfolio in column (3). We regress therisky share on characteristics in column (4). Standard errors are clustered at the household level.
The regressions reveal that financial characteristics are strongly related to the value loading.
The financial wealth coefficient is positive and strongly significant for the risky, stock and fund
portfolios. Households with more liquid financial wealth tend to select financial portfolios with avalue tilt. The financial wealth coefficient reaches its highest value for the stock portfolio, whichsuggests that wealthy households primarily achieve this tilt by investing directly in value stocks.
This finding is consistent with the fact that the value loadings of mutual funds themselves tend
to concentrate around the neutral benchmark (see Table II). In the Internet Appendix, we verify
that the link between financial wealth and the value loading is not due to reverse causality byregressing the value loading on lagged values of financial wealth. Thus, the empirical evidence
indicates that financial wealth has a positive impact on the value loading. In Section 4.3.5, weverify the robustness of this result to latent heterogeneity by using twin data.
Real estate is also associated with value investing. Residential and commercial real estate havepositive regression coefficients, which are significant for the risky and stock portfolios. Since homeownership is usually financed by a mortgage, it is also important to consider the impact of debt.
We report that households with a high leverage ratio tend to invest directly in growth stocks, whileno tilt is apparent in the risky and fund portfolios. Financial and real estate wealth are therefore
associated with a value tilt, while debt is associated with a growth tilt in the stock portfolio. We
investigate later in the section if the interaction between real estate and leverage also drives thefinancial portfolio.
18

Human capital and labor income are strongly related to the value loading. Households withhigh current income Lh,t and high expected human capital HCh,t (as defined in equation (4)) tilt
their financial portfolios toward growth stocks; these relationships are significant for all three typesof portfolios. Income risk measures also have strongly negative coefficients: households with highincome volatility or with a head who is either self-employed or unemployed are prone to selecting
growth stocks. In the Internet Appendix, we verify that these results are robust to regressing thevalue tilt on the persistent and transitory components of income risk, σξ,h and σε,h , instead of
the total volatility σh . Current income, expected human capital, and the volatility of the income
process therefore all tilt household financial portfolios toward growth stocks.
Demographic characteristics are also significant. The age of the household head tends to increase the value loading. Younger households tend to go growth and older households tend to govalue, primarily through direct stockholdings. Section 6 provides further evidence on the connection between the value tilt and age. The gender variable is strongly significant; men tend to have a
growth tilt and women a value tilt. Immigrants and educated households also have a tendency togo growth, which suggests that the value loading is not just driven by sophistication.
Table III raises some immediate questions about real estate and family size, which are important for the interpretation of the results and their connections with risk-based theories. Realestate is both (i) a form of wealth that can prompt households to aggressively invest in equities
with systematic risk exposures, such as value stocks, and (ii) a source of risk that can discourage
households from purchasing systematically risky stocks. The strength of these two channels is
likely influenced by leverage. In Table IV, Panel A, we regress the value loading of the financial
portfolio on the leverage ratio, log residential real estate, log commercial real estate, the leverage
ratio interacted with log residential real estate, the leverage ratio interacted with log commercial
real estate, and all the other characteristics considered in Table III. The full regression is reported
in the Internet Appendix. Leverage as a standalone variable has a strongly negative impact on thevalue loading, which is significant for all portfolios. For households with low leverage, residential
and commercial real estate tilt the risky and stock portfolios toward value stocks. By contrast, forhouseholds with high leverage, both forms of real estate tilt the financial portfolio toward growthstocks.
Family size also plays an ambiguous role in the baseline regressions of Table III. On the one

19hand, households with secure jobs and financial prospects are more likely to decide to have children; thus family size can be viewed as a predictor of sound future financial conditions and cantherefore co-vary positively with value investing in the cross-section. On the other hand, children
are a source of random needs and other forms of background risk that can discourage value investing. We now use a panel of twins to disentangle the two effects. Our identification strategy isthat while the decision to have a child is endogenous, the arrival of twins is an exogenous financial
shock that could not be fully anticipated and should tilt the portfolio toward growth stocks. In
Table IV, Panel B, we accordingly modify the baseline regression by including a dummy variable
for having children and a dummy variable for having twins. While the child dummy has positivecoefficients, the twin dummy has a negative impact on the loadings of all three portfolios. Thus,
the unexpected birth of an additional child tilts the portfolio toward growth stocks.
Overall, the regressions in Tables III and IV provide substantial evidence that the portfolio
value loading co-varies with financial and demographic characteristics. Value investors tend tohave high financial and real estate wealth, low leverage, low income risk, and low human capital;
they are also and more likely to be older and female. Conversely, young males with risky income
and high human capital are more likely to go growth. We now verify the robustness of thesebaseline results to alternative hypotheses.

4.3 Identification and Robustness Checks
4.3.1 Portfolio Concentration
In Table V, we investigate whether the baseline results are mechanical implications of portfolio
concentration. We reestimate the baseline regression on five separate groups of investors: mutualfund owners in column (1), direct stockholders in column (2), and direct stockholders sorted by the
number of firms that they own in columns (3) to (5). The baseline results remain valid in all groups.
Furthermore, the explanatory power of the regression is substantially higher for households owning
more stocks. Thus, wealthier, more educated direct stockholders holding at least three different
stocks are prone to selecting value tilts that are well explained by their financial and demographic
characteristics.

20

4.3.2 Popular Stocks
As Table I shows, household portfolios are dominated by a handful of popular firms. We now
assess the potential implications of popular stocks for the baseline results of Section 4.2. Table VI
reports the 10 stocks that are most widely held by Swedish households at the end of 2003. For
each of these 10 firms, we compute the percentage of direct stockholders owning it, the stock’s
percentage of aggregate household financial wealth, the stock’s percentage of the Swedish stock
market, the stock’s percentage of the Swedish free float, the stock’s value loading, and the percentile of the stock’s book-to-market ratio. Popular stocks are a mix of growth stocks and value
stocks, regardless of whether one classifies stocks by value loading or book-to-market percentile.
In the first two sets of columns of Table VII, we reestimate the baseline regression for the portfolio of popular stocks directly held by households in column (1), and the portfolio of non-popular
stocks in column (2). For both portfolios, characteristics have the same impact as in the baseline
regression. In the Internet Appendix, we verify that the baseline results also hold among households that invest either 100% or 0% of their stock portfolios in popular firms. We conclude thatthe relationship between the value loading and characteristics is unlikely to be driven by popular
stocks. Furthermore, the explanatory power of the regression in Table VII is substantially higher
for non-popular stocks, suggesting that investors with broad portfolios select their value tilts moredeliberately than other investors.
4.3.3 Professionally Close Stocks
We next ask if professionally close stocks, which represent 16% of household stock portfolios, canaccount for the relationship between the value loading and financial characteristics. In columns (3)and (4) of Table VII, we reestimate the baseline regression separately on the portfolio of professionally close stocks and on the portfolio of other stocks. Our baseline results are apparent in both
portfolios.
It is interesting to assess if our findings are driven by investors working in specific sectors
or are instead broad phenomena that can be observed in all industries. In the Internet Appendix,
we consider subsamples of households working in the public sector or in pools of companies
sorted according to the value loading of workers with only 1 stock, the value loading of employee

21

incomes, or the employees’ shares of professionally close stocks. Quite strikingly, the results
obtained from every subsample are consistent with the baseline results of Table III. In the Internet
Appendix, we verify that the baseline results also remain valid for households with extreme sharesof professionally close stocks. Thus, the baseline results are unlikely to be driven by holdings ofprofessionally close stocks.
4.3.4 Financial Market Experience
Age has a positive coefficient in the baseline regression, which indicates that ceteris paribus older
households tend to invest in value stocks. Risk-based theories provide a possible explanation forage effects through investment horizons. Another interpretation is that age simply proxies forfinancial market experience. Naive new investors might purchase overpriced growth stocks, learn
that these stocks are bad deals, and then progressively migrate toward value stocks as time goesby. Learning can thus create a positive cross-sectional correlation between age and value investing,which is unrelated to the investment horizon channel.
Table VIII reports regressions that include both age and the number of years of risky asset
market participation in the set of explanatory variables. Specifically, we consider households thatparticipate in risky asset markets in 2007, and regress the 2007 value loading on age, the number ofyears since entry, the value loading in the year of entry, and the other usual characteristics in 2007.
The coefficient on the number of years since entry is significantly negative for all portfolios, whichis inconsistent with the simple learning story.13 Thus, financial market experience, measured by the
number of years in risky asset markets, induces a growth tilt and, more importantly, cannot explain
away the positive link between age and the value tilt. In a recent study, Campbell, Ramadorai,
and Ranish (2014) consider an Indian brokerage data set containing highly detailed information on
individual trades, but no socioeconomic characteristics. They show that the returns experienced bya household drive its future portfolio style. Our results indicate that the number of years spent on
financial markets cannot explain away the relationship between age and the value tilt.
Table VIII also sheds light on the dynamics of the portfolio tilt during the participation period.
13 The panel does not allow us to observe entry to financial markets prior to 1999.

In the Internet Appendix, we
verify that the results are unchanged when we regress the value loading on a dummy for 1999 participation, themeasure number of participation years, and all the other characteristics in Table VIII, which shows that the limitationsof the experience variable are not a cause for concern.

22

The value loading in the entry year has a positive and strongly significant impact on the valueloading in 2007, as one might expect. Furthermore, the impact of other characteristics remain
significant and are consistent with our earlier results when we control for the initial loading. Thissuggests that the value loading is not simply driven by the initial portfolio in the year of entry, butalso depends on financial and demographic characteristics in the subsequent participation period.
One potential concern with Table VIII is that our definition of financial experience might becollinear to age. In the Internet Appendix, we remove age from the list of control variables and verify that the relationship between participation years and the value tilt remains negative. Section 6
provides further evidence on the relationship between age and value investing.
4.3.5 Latent Heterogeneity
The panel regressions presented until now include yearly, industry, and county fixed effects. One
might worry, however, that household characteristics merely proxy for latent traits or cohort effects.
For this reason, we estimate on the twin panel regressions of the form:
vk,1,t = αk,t + b′ xk,1,t + ek,1,t ,(5)

vk,2,t = αk,t + b′ xk,2,t + ek,2,t ,(6)where vk,s,t denote the value loading of sibling s ∈ {1, 2} in pair k at date t, αk,t is a yearly pair
fixed effect of twin pair k, xk,s,t denotes the vector of yearly characteristics of sibling s, and ek,s,t
is an orthogonal error. The yearly twin pair fixed effect captures the common effects of time, suchas age or stock market performance, as well as similarities between the twins, such as common
genetic makeup, family background, upbringing, and expected inheritance. Since twin siblings
have the same age, the twin regression naturally controls for cohort effects. Calvet and Sodini(2014) apply this methodology to the determinants of the risky share,14 and we now use it to checkthe robustness of our baseline value loading results.
In Table IX, we regress the value loading on yearly twin pair fixed effects and household
characteristics. Consistent with the baseline results in Table III, twins with high financial and realestate wealth and low income, low human capital, and low income risk tends to go value. In the
14 Cesarini, Dawes, Johannesson, Lichtenstein, and Wallace (2009), Cesarini, Johannesson, Lichtenstein, Örjan

Sandewall, and Wallace (2010), and Barnea, Cronqvist, and Siegel (2010) also use twins to investigate risk-taking.

23

Internet Appendix, we show that these results also hold on the subsample of identical twins. Thus,
the main empirical regularities reported in the paper are robust to the inclusion of yearly twin pair
fixed effects.
The twin regression has a substantially higher adjusted R2 coefficient than the baseline regression. For the stock portfolio, socioeconomic characteristics and year, industry, and county fixed
effects explain 4% of the cross-sectional variation in the value loading among the general population (Table III). By contrast, characteristics and yearly twin pair fixed effects account for 23%of the cross-sectional variation of the stock portfolio value tilt among twins (Table IX). Large
increases in adjusted R2 are also obtained for the risky and fund portfolios.
Thus, yearly twin pair fixed effects have a major impact on the portfolio tilt, but do not modify
the baseline relationships between the value loading and socioeconomic characteristics. In thenext section, we discuss the possible origins of the high explanatory power of yearly twin pair
fixed effects.
4.3.6 Communication and Genes
The twin panel obtained from the Karolinska Institute contains detailed information on the frequency of communication between twins. We classify a twin pair as “high communication” if thefrequency of mediated communication and the frequency of unmediated communication are bothabove the median, and as “low communication” otherwise.
In Table X, we sort twin pairs into high and low communication bins, and reestimate in each bin
the baseline regression of the value loading on characteristics and year, industry and county fixed
effects. The relationships between the value loading and characteristics are generally consistent
with the baseline results in each bin. In the Internet Appendix, we obtain similar results when weuse yearly twin pair fixed effects. Thus, communication does not impact the relationship between
the value tilt and socioeconomic variables. Moreover, the adjusted R2 is substantially higher inthe presence of yearly twin pair fixed effects, reaching 30% for the stock portfolio of frequently
communicating twins.
The high adjusted R2 of the twin regressions could suggest that value investing has genetic
origins, as has been recently proposed by Cronqvist, Siegel, and Yu (2013) on the basis of a genetic24

decomposition. Specifically, in the ACE model considered by Cronqvist, Siegel, and Yu (2013),
the value loading vk,s of sibling s in pair k is assumed to be the sum of a genetic component ak,s , acommon component ck , and an idiosyncratic component εk,s :vk,s = ak,s + ck + εk,s ,which satisfy the following identification conditions. The twin correlation of the genetic component, Corr(ak,1 ; ak,2 ), equals 1 for identical twins and 1/2 for fraternal twins. The cross-sectionalvariance of the genetic component, σ2a , is the same in the group of identical twins as in the groupof fraternal twins. Similarly, the variance of the common component, σ2c , and the variance of
the idiosyncratic component, σ2ε , are the same for fraternal and identical twins. Furthermore, thecomponents ak,s , ck , and εk,s are mutually uncorrelated. Under this model, the twin correlationof the value loading, Corr(vk,1 ; vk,2 ), is ρI = (σ2c + σ2a )/(σ2c + σ2a + σ2ε ) for identical twins, andρF = (σ2c + σ2a /2)/(σ2c + σ2a + σ2ε ) for fraternal twins. The rescaled correlation difference,2(ρI − ρF ) =σ2a,σ2c + σ2a + σ2ε(7)quantifies the contribution of the genetic component to the cross-sectional variance of the valueloading according to ACE.
Table XI reports the ACE decomposition of the value loading for all twins as well as for twins
sorted twins by communication frequency. We consider both the value loading itself (“No controls”) and the residual of a regression of the value loading on characteristics (“With controls”).
For all twins, the contribution of the genetic component ranges between 10 and 17% for the stockand the risky portfolios, and is slightly lower for the fund portfolio, regardless of whether or not we
consider the value loading itself or its residual in the baseline regression. These estimates confirm
the findings of Cronqvist, Siegel, and Yu (2013).
The table also reveals that the estimated contribution of the genetic component, given by (7),is highly sensitive to communication. For all three portfolios, the genetic share reaches 35% forfrequent communicators but disappears almost entirely among infrequent communicators, with estimates that do not exceed 1% across specifications.15 These low estimates are especially surprising
if ACE is correctly specified, because purely genetic effects should not depend on communication.
15 The estimator of the genetic share (7) is the rescaled difference between two sample correlations. It can therefore

take negative values if the estimate of ρI is lower than the estimate of ρF in a particular sample. In fact, under the null25

The table indicates that the so-called genetic component of the ACE model is unlikely to be purely
driven by genes. Instead, the genetic share estimate (7) incorporates other effects, such as thesubstantial impact of communication on portfolio decisions.
One could argue that the communication frequency itself has genetic origins, so that the resultsof Table XI could be construed as evidence that value investing is driven by genes. However, by
equation (7), the genetic share is zero if and only if the twin correlation of the value loading is thesame for identical and fraternal pairs: ρI = ρF . Thus, a genetic theory of value investing needs to
explain why infrequently communicating twins have the same loading correlations regardless ofgenetic makeup, which seems to be challenging task.
The sensitivity of the ACE decomposition is related to one of the well-known shortcomingsof ACE, namely that it neglects interactions between genetic and environmental variables. Interactions between nature and nurture are known to be empirically important in medicine andexperimental psychology (Ridley 2003). The modern view in these fields is that genes cause a predisposition to certain behaviors or diseases, which develop only in particular environments. Table
XI shows that the clean dichotomy between nature and nurture is equally elusive in the context ofvalue investing.
Overall, the section uncovers strong relationships between a household’s value loading and thehousehold’s financial and demographic characteristics. We show that these empirical regularities
are unlikely to be explained away by genes, communication, latent traits, experience or certain
types of stocks. We interpret these findings in the next section, and empirically investigate thedynamics of the value tilt in Section 6.

5 Interpretation of the Empirical Determinants of the Value
Tilt
We now relate our empirical results to the asset-pricing explanations of the value premium reviewed in Section 2.
hypothesis
H0 : ρ I = ρ F ,the estimator of the genetic share converges asymptotically to a centered normal as the number of pairs goes to infinity.
Negative estimates of the so-called genetic share are then asymptotically as likely as positive estimates.

26

5.1 Risk Aversion, Wealth, and Background Risk
Risk-based theories imply that household portfolio tilts are partly determined by financial wealth,leverage, background risk, and other variables affecting their willingness to take financial risk.
Quite remarkably, the empirical impact of financial variables on the portfolio tilt is generally inaccordance with the predictions of risk-based theories, as we now explain. Liquid financial wealthis positively related to the value loading across participants (Table III) and in the twin panel (Table
IX). In the Internet Appendix, we regress the value loading on lagged financial wealth and verify
that our results are unlikely to be driven by a reverse causality between the value loading andfinancial wealth. The empirical evidence therefore indicates that financial wealth has a positive
impact on the value loading. As early studies (e.g., Calvet and Sodini 2014) document and as
Table III confirms, financial wealth is also associated with high risky shares. These results suggestthat wealthier households adopt value strategies because they are more risk tolerant and therefore
more prone to bearing the systematic risk (other than market portfolio risk) embedded in value
stocks. In particular, our findings are consistent with Munk (2008)’s model of portfolio choicewith habit formation.
The positive relationship between financial wealth and the value tilt holds in all subgroups ofinvestors, including the wealthy group of stockholders owning 5 stocks or more (Table V). Furthermore, educated households favor growth stocks, even more so if they have studied economics.
We have also provided evidence that financial wealth does not proxy for financial market experience. Thus, growth investing is not the restricted turf of unsophisticated investors, and the positive
relationship between financial wealth and value investing is unlikely to be driven only by sophistication.
Our results on real estate, leverage, and family size provide additional support for risk-based
interpretations of value investing. Unlevered households with real estate tend to invest in value
stocks, while leveraged households tend to purchase growth stocks. As a form of wealth, realestate encourages households to tilt their portfolios toward value stocks in order to earn the value
premium. By contrast, households with substantial leverage choose a lower risky share and tilt theirrisky portfolios toward growth stocks in order to reduce their systematic exposure, thus giving up
the value premium. The unexpected birth of a child also induces a growth tilt, which is consistent
with the lower resources per-capita and higher idiosyncratic needs that the arrival of a newborn
27entails.

5.2 Income and Human Capital
Growth stocks are picked by households with risky incomes, as measured by the income volatility
σh , self-employment, or unemployment. This empirical regularity can be viewed as a consequenceof background risk if labor income is uncorrelated to the value factor. The growth tilt may also
be a hedge against future income shocks if household income and the value factor are positively
correlated.
To assess these mechanisms, we regress the stochastic component of income, defined by (3),on the returns of the pricing portfolios:
ηh,t = λ′h ft + η̃h,t ,(8)where ft = (1, MKTt , HMLt , SMBt , MOMt )′ . In Table XII, Panel A, we decompose income variance into systematic and idiosyncratic components: Var(ηh,t ) = Var(λ′h ft ) + Var(η̃h,t ). The idiosyncratic share, Var(η̃h,t )/Var(ηh,t ), is close to 80%. Most of labor income risk is idiosyncratic,so that the pricing portfolios can only provide a limited hedge against fluctuations in income. As
a consequence, one expects that the value tilt is driven more strongly by idiosyncratic income risk
than by systematic income risk.
In Table XII, Panel B, we confirm this intuition by regressing the portfolio tilt, vh,t , on theloading of income on the value factor, λh , and idiosyncratic labor income variance, Var(η̃h,t ),where λh , and η̃h,t are defined in (8). Idiosyncratic variance is by far the most significant variable
and negatively impacts the portfolio loadings. Thus, households with substantial idiosyncraticlabor income risk select low risky shares and tilt away from value stocks. In future work, it would
be interesting to refine the analysis by taking into account the cointegration of the stock and labormarkets, as in Benzoni, Collin-Dufresne, and Goldstein (2007).
The effect of human capital on the value tilt provides further evidence of an income risk effect.
In Section 2.2, we have explained that human capital plays an ambiguous role because it is both aform of wealth and a form of risk. The empirical evidence strongly suggests that human capital tilts
the portfolio toward growth stocks, so that the risk channel dominates. Furthermore, we report thatlabor income is weakly correlated to the value factor at the micro level (Table XII), while human28capital and physical capital are strongly correlated at the macro level (Baxter and Jermann 1997).
These contrasting results suggest that the idiosyncratic risks that dominate household labor income
risk aggregate out at the macro level. These results provide guidance for building general equilibrium models that can account for the empirical evidence on the value premium and household
micro data.

5.3 Intertemporal Hedging and Horizon Effects
The portfolio choice literature on the value factor focuses on intertemporal hedging and time horizon effects (Jurek and Viceira 2011, Larsen and Munk 2012, Lynch 2001). Indeed, if value stocks
outperform growth stocks when aggregate expected returns improve, market participants can use
growth stocks as a hedge against adverse variation in investment opportunities. Since the hedging
motive is stronger for investors with longer horizons, portfolio theory predicts that young investors
should hold growth stocks and old investors should hold value stocks.
As the results reported in Section 4 show, age is positively and significantly related to the valueloading. This relationship is observed even when we control for real estate, debt, financial market
experience, human capital, income risk, and other socioeconomic characteristics that vary with age.
Our baseline results thus provide strong empirical support for one the main predictions of portfolio
choice models incorporating the value factor, the positive link between age and value investing. In
Section 6, we will investigate the life-cycle variation in the value tilt and its relationship to age,financial wealth, human capital, and other socioeconomic characteristics.

5.4 Overconfidence
The impact of gender sheds light on behavioral and risk-based explanations of value investing.
Women tend to select low risky shares and invest in value stocks, while men tend to select aggressive risky shares and go growth. These patterns cannot easily be explained by differences inrisk aversion alone, since a risk-averse investor should choose both a lower risky share and tilt
the risky portfolio toward growth stocks in a model such as Munk (2008). A likely explanation isthat men are more overconfident than women and therefore tend to favor glittering growth stocks.
The positive link between self-employment and growth investing can also be viewed as evidence29

of overconfidence, since entrepreneurs are generally known to be overconfident in their financial
decision-making abilities (Busenitz and Barney 1997, Cooper, Woo, and Dunkelberg 1988). In
the Internet Appendix, we reestimate the baseline regression on the subsample of households witha male head and on the subsample of households with a self-employed head. The two groups
are especially prone to overconfidence according to earlier studies. The baseline results, however,hold in both subsample and are therefore unlikely to be driven by cross-sectional differences inoverconfidence alone.

6 Dynamics of the Value Tilt
This section investigates the dynamics of the portfolio tilt. We show that at the yearly frequency,
households actively rebalance their exposure to the value factor in response to passive variationin their portfolio tilt. At longer horizons, households progressively switch from growth stocks tovalue stocks as they get older, a migration which we coin the “value ladder.” We also quantify therespective impact of age and other characteristics on the value loading over the life-cycle.

6.1 Active Rebalancing at the Yearly Frequency
We now consider passive and active variation in the value tilt of household portfolios. Calvet,
Campbell, and Sodini (2009a) define active and passive changes of the risky share, and provide
strong evidence that households actively rebalance the passive variation in the risky share due torealized asset returns. We now apply a similar methodology to the portfolio tilt of the risky, stock,and fund portfolios.
We begin with definitions of passive and active rebalancing of the value tilt. Consider household h with portfolio weights wh,i,t−1 (i = 1, . . ., I) at the end of year t − 1. If the household did nottrade during the following year, the share of asset i at the end of year t would be
wPh,i,t =wh,i,t−1 (1 + ri,t ),I∑ j=1 wh, j,t−1 (1 + r j,t )(9)where, for every j ∈ {1, . . ., I}, r j,t denotes the rate of return on asset j between t − 1 and t. By

30equation (1), the value loading of the passive household at the end of year t would then be:IvPh,t = ∑ wPh,i,t vi .

(10)

i=1

The data set reports the actual loading vh,t . We can therefore decompose the actual change of theportfolio value loading, vh,t − vh,t−1 , as the sum of active and passive changes:
vh,t − vh,t−1 = ah,t + ph,t .
where ah,t = vh,t −vPh,t denotes the active change and ph,t = vPh,t −vh,t−1 denotes the passive change.
Table XIII regresses the active change, ah,t , on (i) the passive change, ph,t , (ii) the lagged valueloading, vh,t−1 , and (iii) either no characteristics or all other lagged characteristics. The passivechange has a negative and highly significant coefficient for all portfolios, regardless of whether or
not one controls for household characteristics. Specifically, the passive change coefficient is -0.36
for the risky portfolio, is slightly stronger for the stock portfolio, and is slightly weaker for the fund
portfolio. These estimates imply that the average household actively undoes passive variation in
the value loading, presumably because it has a sense of the target value loading that it would liketo achieve. Overall, Table XIII confirms that households actively rebalance the passive variation in
their value tilt, as portfolio theory (Lynch 2001, Munk 2008) implies.

6.2 Value Ladder over the Life-Cycle
Figure 2 illustrates life-cycle variation in the value loading. We sort households into 9 cohorts
based on the year of birth, and plot for each cohort the yearly average wealth-weighted valueloading over the 1997 to 2007 period. The figure is based on all Swedish households that directly
hold stocks during the period and satisfy the basic requirements stated in Section 3.2. Households
are weighted by financial wealth because this aggregation method has the strongest implications
for asset pricing. All value loadings in a given year are demeaned in order to control for changes
in the average loading of individual stocks, which are caused by the exit of some stocks from thestockmarket and the entry of new stocks.
We observe that young households select growth portfolios and older households choose value
portfolios. The dependence between the value loading and age is therefore positive, which confirms
the positive coefficient on age in the baseline regression of Table III and the other results reported
31

in Section 4. The relationship between the loading and age is also strikingly linear, which is also
consistent with our baseline specification. Furthermore, for all cohorts, there is a tendency forhouseholds to migrate toward higher loadings as time goes by. Figure 2 illustrates the value ladder
for the stock portfolio. In the Internet Appendix, we show that a similar ladder exists for the risky
portfolio. We also plot the equal-weighted value loading of household portfolios and obtain thatthe results are similar to the wealth-weighted estimates in Figure 2.
One may ask if the value ladder is due to exogenous drifts to which stockmarket participants are
passively exposed. In order to control for such effects, a natural solution is to consider the value
tilt of new participants in the year they enter risky asset markets. In Table XIV, we regress thestock portfolio value loading on household characteristics. Consistent with the baseline results, theexposure to the value factor increases with financial wealth, commercial real estate, and age, anddecreases with human capital, income risk, self-employment, and unemployment. In particular, age
has a significantly positive coefficient: older entrants select a higher value loading than younger
entrants, which confirms that pure horizon effects are empirically important.
We next assess if the relationship between the value loading and age is driven by specific age
groups. In Table XV, we regress the value loading on cumulative age dummies, cumulative age
dummies for new entrants, and all characteristics other than age. The cumulative age dummies
corresponding to all participants are strictly positive and almost all significant. Moreover, therelationship between a participant’s age and its value loading is approximately linear, consistent
with the baseline results in Section IV.
The age dummies of new entrants are primarily insignificant. One interesting exception is thedummy variable for new entrants aged 30 or more, which is significantly negative. Since the age
dummy coefficients are cumulative, this result simply implies that all new entrants have a significant bias toward growth stocks. Since the other coefficients are insignificant, age does not seemto impact differences in the value tilt between preexisting participants and new entrants. Thus,
the value ladder of new entrants is parallel to and located below the value ladder of preexisting
participants.
These results have a natural interpretation in a general equilibrium context. In an economy inwhich participants gradually sell their growth stocks and migrate toward value stocks, the growthstocks must be absorbed by another group of investors. The empirical evidence in this section
32

shows that new entrants have a growth tilt compared to other households. Thus, new entrants
absorb the growth stocks of preexisting participants. At the other end of the ladder, the portfoliosof the deceased contain value stocks that surviving investors can purchase. New entrants andinheritances therefore permit the migration from growth stocks to value stocks over the life-cycle.
In future work, it would be interesting to construct a formal overlapping generations model withthese features. Our results also suggest that demographic changes can affect the demand for value
and growth stocks, which may have implications for the value premium.

6.3 Economic Significance of the Value Ladder
We have documented that a household’s value tilt is related to its contemporaneous financial circumstances, and that a household migrates from growth stocks to value stocks over the life-cycle.
These results suggest that the value ladder is driven both by (i) changes in financial conditions and(ii) pure investment horizon effects. We now quantify the respective roles of these two channels.
In Table XVI, Panel A, we consider a 30-year old investor, to which we assign the average
financial wealth, real estate wealth, leverage, income and human capital in his age cohort in 2003.
We also consider a 70-year old investor with the average characteristics of its age group. The
estimates in Table III allow us to decompose the life-cycle variation in the value loading. Between
30 and 70, the value loading of the risky portfolio increases by 0.20, out of which 0.12 is due
to age. For the stock portfolio, the value loading increases by 0.58 between 30 and 70, out of
which 0.36 is attributed to age. For both portfolios, age therefore explains slightly more than 50%of the life-cycle variation in the value loading. Among financial characteristics, human capitaland financial wealth are the most important variables. The reduction in human capital over thelife-cycle accounts for 36% of the life-cycle variation of the risky portfolio loading, while theaccumulation of financial wealth accounts for another 12% of the migration. Other characteristics,
such as real estate, have a marginal impact.
In Table XVI, Panel B, we reestimate the decomposition when the interaction between realestate and leverage is taken into account. Age alone continues to explain half of the life-cycle
variation in the value loading. The measured impact of real estate and leverage is now substantiallystronger, which shows once again that is important to account for the interaction between debt andreal estate wealth.
33

Overall, this section documents that households both actively rebalance the value loading at the
yearly frequency and progressively shift to higher loadings over the life cycle. As in the portfoliochoice models of Jurek and Viceira (2011) and Lynch (2001), households seem to have a slowmoving target loading, and actively undo the higher frequency passive changes that move themaway from the target. Furthermore, changes in age account for half of the value ladder, whilechanges in human capital and financial wealth account for the most of the remainder. Life-cycle
variation in human capital and financial wealth are important determinants of value and growthinvesting, which deserve to be incorporated in future portfolio-choice models.

7 Conclusion
This paper documents strong empirical patterns in the holdings of value and growth stocks byhouseholds. The average value investor is substantially older, and has higher financial wealth,higher real estate wealth, lower leverage, lower income risk, and lower human capital than the
average growth investor. Moreover, males, entrepreneurs, and immigrants tend to have a growthtilt. These baseline results hold regardless of whether or not one excludes popular or professionallyclose stocks, and are unlikely to be explained away by latent preferences, genes, communication,or financial market experience.
Our study provides empirical support for a number of key theoretical explanations of the value
premium. Consistent with risk-based theories, value stocks are held by investors who are in thebest position to take financial risk, for instance because they hold substantial liquid wealth, earnsafe incomes, and have low debt. Our paper is the first to document portfolio evidence in favorof rational theories of the value premium. Furthermore, the relationships between growth investing and variables such as gender or entrepreneurship seem consistent with the representativenessheuristic and overconfidence biases documented in the psychology literature. Thus the panel is
explained by a mix of psychological and risk-based explanations of the value premium.
We provide evidence that households actively manage their holdings of growth and value
stocks. At yearly frequencies, households dynamically rebalance their exposure to the value factor
in response to passive variation in the portfolio tilt. Quite strikingly, the relationships between
the value tilt and household characteristics hold just as strongly for new entrants as they do for34preexisting participants. At longer life-cycle horizons, households climb the “value ladder” andgradually shift from growth to value investing as they become older, wealthier, less levered, andless dependent on their human capital. We estimate that pure horizon effects, captured by age, account for at least 50% of the life-cycle variation of the value tilt, which provides strong empiricalsupport for intertemporal hedging (Lynch 2001).
Our results provides new directions for portfolio-choice and asset-pricing theories of the value
factor. The household panel reveals that growth investing is tightly linked to human capital, income
risk, and psychological biases, which would deserve formal investigation in calibrated portfoliochoice models. Furthermore, our empirical findings suggest that powerful general equilibrium
effects are at play in the cross-sectional distribution and the dynamics of portfolio tilts. The development of overlapping generations models matching these features would be natural extensionsof the present paper. Last but not least, the empirical patterns in the demand for value and growthstocks uncovered in this paper may have major implications for equity valuation, which will be
investigated in further research.

35

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Financial Studies, 19(1), 1–44.
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43

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44

Financial Characteristics
Financial wealth ($)
R id ti l reall estate
Residential
t t wealthlth ($)
Commercial real estate wealth ($)
Leverage ratio
Human Capital and Income Risk
Human capital ($)
Income ($)
Self-employment dummy
Unemployment dummy
Conditional income volatility
Demographic Characteristics
Age
Male household head dummyHigh school dummy
Post-high school dummy
Economics education dummy
Immigration dummy
Family size
Number of observations
121,578
184 525
184,525
112,626
1.13
515,879
31,316
0.20
0.27
0.12
10.73
0.48
0.36
0.48
0.32
0.27
1.40
71,639,48,849137,108137
108
19,581
0.66
955,680
46,184
0.04
0.08
0.16
46.27
0.64
0.85
0.37
0.12
0.08
2.53
,
71,639Mean Standard
deviation

All Participants

46.06
0.63
0.85
0.37
0.12
0.08
2.61
62,972,

972,40246,785
0.04
0.07
0.16

50,614
138,327138
327
19,520
0.65

10.69
0.48
0.35
0.48
0.32
0.26
1.40
62,972,

513,389
30,687
0.19
0.26
0.11

121,099
179 024
179,024
111,890
1.09

Mean Standard
deviation

Fundholders

47.60
0.69
0.86
0.42
0.13
0.08
2.52
42,153,

993,11450,066
0.05
0.07
0.17

66,478165,020165
020
27,255
0.53

10.58
0.46
0.35
0.49
0.34
0.27
1.37
42,153,545,932
37,029
0.22
0.25
0.12

152,690
215 680
215,680
135,585
0.91

Mean Standard
deviation

Stockholders

Panel A: Financial and Demographic Characteristics

46.82
0.66
0.84
0.35
0.12
0.08
2.42
22,522,929,51744,902
0.05
0.08
0.17

37,123
129,854129854
21,598
0.65

47.55
0.70
0.86
0.42
0.14
0.09
2.56
7,786
,

1,030,770
51,133
0.05
0.06
0.17

60,091
169,241169
241
30,115
0.46

49.12
0.73
0.90
0.53
0.16
0.07
2.69
11,845,

1,089,285
59,183
0.05
0.05
0.18

126,493229 107229,10736,131
0.34

Stockholders Sorted
By Number of Stocks Owned
1-2
3-4
5+
Mean
Mean
Mean

The table reports summary statistics on the financial and demographic characteristics (Panel A) and portfolio characteristics (Panel B) of participating
Swedish households at the end of 2003. We consider risky asset market participants (first set of columns), mutual fund holders (second set of columns),
direct stockholders (third set of columns),
columns) and direct stockholders sorted by the number of stocks that they own (last set of three columns).
columns) For eachcharacteristic, we report the cross-sectional mean and standard deviation in each sample. The bottom rows of Panel B tabulate the fraction of theaggregate wealth of risky asset market participants held by specific groups of investors. The calculations are based on the representative panel ofhouseholds over the 1999 to 2007 period defined in Section 3.2. All variables are described in Table A.

Table I
Summary Statistics

Portfolio Characteristics
Risky share
Share of direct stockholdings in risky portfolio
Share of popular stocks
Share of professionally close stocks
Number of stocks
Number of funds
Share of Aggregate
gg g
WealthRisky portfolio
Stock portfolio
Fund portfolio
Number of observations
0.27
0.37
0.37
0.32
5.15
4.51

71,639

0.40
0.29
0.71
0.16
2.59
4.11
1.00
1.00
1.00
71,639Mean Standard
deviation

All P
Participants
ti i
t

0.94
0.85
1.00
62,972

0.42
0.19
0.71
0.16
2.53
4.68

62,972

0.26
0.28
0.36
0.31
5.30
4.53

Mean Standard
deviation

F dh ld
Fundholders

Panel B: Portfolio Characteristics

Table I
Summary Statistics - Continued

0.86
1.00
0.75
42,153

0.46
0.49
0.71
0.16
4.40
4.55

42,153

0.27
0.37
0.37
0.32
6.10
5.19

Mean Standard
deviation

St kh ld
Stockholders

0.18
0.09
0.25
22,522

0.37
0.44
0.79
0.15
1.35
3.49

0.13
0.11
0.14
7,786

0.49
0.48
0.71
0.17
3.42
4.90

0.54
0.80
0.36
11,845

0.61
0.58
0.57
0.18
10.85
6.34

St kh ld
Stockholders
S
Sortedt dBy Number of Stocks Owned
1-2
3-4
5+
Mean
Mean
Mean

Assets
Stocks listed on Stockholm Stock Exch.
Funds
Households
All participants- Risky portfolio- Stock portfolio- Fund portfolio
Fundholders- Risky portfolio
portfolio- Stock p- Fund portfolio
Direct stockholders- Risky portfolio- Stock portfolio- Fund portfolio
Direct stockholders with 1 or 2 stocks- Risky portfolio- Stock portfolio- Fund portfolio
Direct stockholders with 3 or 4 stocks- Risky portfolio- Stock portfolio- Fund portfolio
Direct stockholders with 5+ stocks- Risky portfolio- Stock portfolio- Fund portfolio
-0.87
-0.15

-0.30
-0.58
-0.20
-0.25
-0.57
-0.20
-0.38
-0.58
-0.22
-0.38
-0.62
-0.21
-0.41
-0 59
-0.59
-0.22
-0.38
-0.48
-0.24

-0.26
-0.36
-0.18
-0.25
-0.35
-0.18
-0.28
-0.36
-0.19
-0.27
-0.64
-0.17
-0.29
-0 49
-0.49
-0.18
-0.28
-0.32
-0.21

Mean

-0.15
-0.10

Aggregate
Portfolio
f

-0.98
-1.30
-0.60

-1.05
-1 37
-1.37
-0.57

-1.11
-1.14
-0.58

-1.07
-1.20
-0.58

-0.71
-1.17
-0.57

-0.94
-1.20
-0.57

-3.22
-0.41

-0.56
-0.83
-0.35

-0.62
-1 07
-1.07
-0.33

-0.65
-1.14
-0.31

-0.61
-1.09
-0.33

-0.40
-1.06
-0.30

-0.46
-1.09
-0.30

-1.57
-0.26

-0.25
-0.33
-0.18

-0.28
-0 54
-0.54
-0.16

-0.23
-1.00
-0.14

-0.24
-0.53
-0.16

-0.17
-0.52
-0.14

-0.18
-0.53
-0.14

-0.37
-0.10

-0.05
0.02
-0.06

-0.05
0 03
0.03
-0.04

0.01
0.11
-0.01

-0.02
0.11
-0.03

-0.01
0.10
0.00

0.00
0.11
0.00

0.09
0.01

Cross-Sectional Distribution
10
10th
2
25th
50th
0
75th

Value Loading

0.10
0.25
0.03

0.10
0 36
0.36
0.06

0.11
0.44
0.07

0.11
0.39
0.07

0.09
0.38
0.08

0.10
0.39
0.08

0.94
0.20

90
90th

0.11
0.16
0.06

0.12
0 18
0.18
0.06

0.12
0.16
0.07

0.12
0.16
0.07

0.08
0.16
0.07

0.11
0.16
0.07

0.42
0.06

Expected Return
Spread Between Top
and Bottom Deciles

The table reports summary statistics on the cross-sectional distribution of the value loading at the end of 2003 for some of the mainfamilies of assets and household portfolios used in the paper. The columns report (i) the value loading of the aggregate portfolio,(ii) the cross-sectionalcross sectional distribution of the value loading,loading and (iii) the expected return spread between the top and bottom deciles.
deciles
The first row considers stocks listed on the Stockholm Stock Exchange and the second row considers all Swedish risky mutual
funds. Household aggregate portfolios are constructed by adding up the market values of individual household holdings.

Table II
Cross-Sectional Distribution of the Value LoadingFinancial Characteristics
Log financial wealthLog residential real estate
Log commercial real estate
Leverage ratio
Human Capital and Income Risk
Log human capital
Log income
Self-employment dummy
Unemployment dummy
Conditional income volatility
Demographic Characteristics
Age
Male household head dummyHigh school dummyg school dummyyPost-highEconomics education dummy
Immigration dummy
Family size
2
Adjusted R
Number of observations
12.44
1.75
3.97
0.30
-9.50
-11.35-4.41-3.99-21.8416.02-18.48
-3.38
-4.64
-5.94
-11.13
24.60

0.017
0.001
0.001
0.000
-0.052-0.046-0.034-0.017-0.3530.003-0.062-0.014-0.016-0.027-0.066
0.036
2.37%
589,561

Risky Portfolio(1)
Estimatet-stat0.009-0.106-0.0350.016-0.011-0.1350.024
3.95%
331,693-0.103-0.044-0.037-0.021-0.338

0.050
0.003
0.007
-0.00823.50-13.57
-3.43
2.00
-1.09
-10.33
7.42

-9.50
-5.75
-2.66
-2.03
-10.98

16.15
4.55
12.36
-1.73

Stock Portfolio(2)
Estimatet-stat0.001-0.013-0.006-0.015-0.014-0.0030.017
0.94%
523,798-0.021-0.029-0.011-0.005-0.116

0.012
0.000
0.000
-0.001

5.53
-5.85
-2.16
-6.89
-4.76
-0.95
19.23

-6.63
-12.87-2.62-1.97-13.28

14.57
-0.27
0.43
-0.98

Fund Portfolio(3)
Estimatet-statDependent Variable: Value Loading-0.002
0.014
0.023
0.034
0.011
-0.007-0.007
16.57%
589,561

0.016-0.062-0.047-0.012-0.0620.095
0.000-0.002-0.008-26.14
8.62
11.20
19.95
4.69
-2.61
-10.445.92-29.50-13.49-5.92-9.24

135.953.32-11.89-14.46

Risky Share(4)
Estimatet-statDependent Variable:

This table reports pooled regressions of the value loading on household characteristics and year, industry, and county fixed effects. The
value loading is computed at the level of the risky portfolio in column (1), the stock portfolio in column (2), and the fund portfolio in column(3) We regress the risky share on the same characteristics and fixed effects in column (4).
(3).
(4) The computations are based on the
representative panel of households over the 1999 to 2007 period defined in Section 3.2. All variables are described in Table A. Standard
errors are clustered at the household level.

Table III
Panel Regression of the Value Loading on Characteristics

Dummy for having children
Dummy for having twins

Log residential real estate
Log commercial real estate
Log residential real estate × Leverage ratio
Log commercial real estate × Leverage ratio
Leverage ratio

Dependent Variable: Value Loading
Risky Portfolio
Stock Portfolio
Fund Portfolio
(1)
(2)
(3)
Estimatet-statEstimatet-statEstimatet-stat
0.087
17.21
0.028
2.17
0.03
8.20
-0.020-2.63-0.039
-1.83
-0.01
-1.15

Panel B: Family Size Variables

Dependent Variable: Value Loading
Risky Portfolio
Stock Portfolio
Fund Portfolio
(1)
(2)
(3)
Estimatet-statEstimatet-statEstimatet-stat
0.000
0.96
0.002
3.25
0.000
-0.55
0.000
1.13
0.006
8.42
0.000
-1.11
-0.001-3.81-0.004
-4.75
0.000
-1.25
-0.002-5.23-0.003-3.10-0.001-3.76-0.011-3.91-0.040-5.25-0.004-2.11

Panel A: Real Estate Interacted with LeveragegThis table reports the effects of additional real estate, leverage, and family size variables on the value loading in the presence ofyear, industry, and county fixed effects. Panel A includes measures of real estate wealth interacted with leverage. We conduct
the estimation on the representative panel of households over the 1999 to 2007 period defined in Section 3.2.
3 2 Panel B includes adummy variable for having a child during the year and a dummy variable for having twins during the year. The estimation isconducted on a sample of households that includes all newborn twins. The regressions are otherwise similar to the baseline
regression in Table III, and the full estimation details and results are available in the Internet Appendix. All variables aredescribed in Table A. Standard errors are clustered at the household level.

Table IV
Alternative Risk Measures

Financial Characteristics
Log financial wealthLog residential real estate
Log commercial real estate
Leverage ratio
Human Capital and Income Risk
Log human capital
Log income
Self-employment
p y
dummy
y
Unemployment dummy
Conditional income volatility
Demographic Characteristics
Age
Male household head dummyHigh school dummy
Post-high school dummy
Economics education dummy
Immigration dummy
Family size
2
Adjusted R
N b off observations
Number
b
ti
9.27
-0.45
2.34
-0.96
-9.29
-15.12-4.51-2.83-20.9816.78-14.08
-2
2.76
76
-6.75
-5.47
-6.93
22.24

0.010
0.000
0.001
-0.001-0.039-0.047-0.025-0.009-0.247
0.002-0.037-00.009
009-0.019-0.020-0.0310.025
2.02%
523 798
523,798

Fundholders(1)
Estimatet-stat0.005-0.085
-0
0.024
024
0.005
-0.018-0.1200.040
4.45%
331 693
331,693-0.073-0.043-0.024-0.031-0.403

0.047
0.002
0.003
-0.01017.40-16.28
-3
3.46
46
0.90
-2.75
-12.39
17.74

-9.15
-7.38
-2.29
-3.93
-17.01

19.97
4.48
7.50
-3.03

Stockholders(2)
Estimatet-stat0.005-0.077-00.029
029-0.003-0.035-0.115
0.046
3.50%
175 707
175,707-0.068-0.047-0.024-0.042-0.379

0.040
0.002
0.004
-0.005

11.92-10.47
-3
3.20
20
-0.38
-3.54
-8.65
14.54

-5.84
-5.63
-1.48
-3.91
-10.78

11.82
2.65
8.40
-1.28

0.005
-0.113
-0
0.008
008
0.016
-0.014-0.1080.038
7.54%
59 697
59,697-0.060-0.036-0.014-0.012-0.444

0.091
0.002
0.002
-0.0288.88-11.20
-0
0.57
57
1.62
-1.06
-5.76
8.36

-3.54
-2.73
-0.65
-0.75
-9.81

16.70
2.01
3.38
-3.42

0.005
-0.076
-0
0.013
013
0.021
0.011
-0.1380.030
7.22%
96 28996,289-0.067-0.044-0.027-0.017-0.413

0.067
0.004
0.001
-0.041

11.65
-9.96
-1
1.15
15
2.71
1.19
-9.46
9.00

-5.87
-5.29
-1.90
-1.47
-12.88

18.18
4.92
0.99
-5.15

Stockholders Sorted by Number of Stocks Owned
One or Two Stocks Three or Four Stocks Five or More Stocks
(3)
(4)
(5)
Estimatet-stat Estimate
t-stat Estimate
t-statDependent Variable: Value Loading of Risky Portfolio

This table reports pooled regressions of the value loading of the risky portfolio on household characteristics and year, industry, and county fixed
effects estimated over different subsets of investors. The regressions are similar to the baseline regressions in Table III, but we consider subgroups ofrisky asset market participants: fund holders in column (1),(1) direct stockholders in column (2),(2) and direct stockholders sorted by the number of owned
stocks in columns (3) to (5). The subgroups are obtained from the representative panel of households over the 1999 to 2007 period defined in Section
3.2. All variables are described in Table A. Standard errors are clustered at the household level.

Table V
Value Loadings of Investor Subgroups

Ericsson
Telia
Swedbank
SEB
Volvo
H&M
Billerud
AstraZeneca
Nokia
Investor
Aggregate portfolio of popular stocks

60.46%
46.50%
24.54%
23.57%
14.58%
11.39%
10.78%
9.66%
8.71%
8 61%
8.61%

21.69%
4.02%
3.76%
5.52%
5.00%
4.75%
1.11%
5.38%
3.78%
2 48%
2.48%
57.49%% of Stockholders % of Household
Owning Company
Stock Wealth
7.49%
6.48%
2.74%
2.69%
3.18%
5.21%
0.22%
4.81%
23.77%
1 95%
1.95%
58.53%% of Swedish
Stockmarket
8.70%
4.16%
2.75%
3.14%
3.36%
3.76%
0.25%
3.79%
31.14%
1 59%
1.59%
62.64%% of Swedish
Free Float

-1.22
-1.00
0.11
0.74
0.41
-0.07
-0.06
0.09
-0.08
0 27
0.27
-0.41

Value Loading

25.41%
44.19%
46.85%
56.21%
68.94%
4.29%
46.26%
68.23%
14.69%
80 77%
80.77%
39.21%

B/M Quantile

The table reports the ten stocks that are most widely held by Swedish households at the end of 2003. Stocks are sorted by the proportion ofhouseholds that hold them directly (first column). We also report the stock’s percentage in aggregate household financial wealth (second column),
the stockstock’ss percentage of the total market capitalization of all firms listed on Swedish exchanges (third column),
column) the stock
stock’ss percentage of the free
float-adjusted market capitalization of all firms listed on Swedish exchanges (fourth column), the stock’s value loading (fifth column), and thepercentile of the stock’s book-to-market ratio (sixth column). The analysis is conducted on the representative panel defined in Section 3.2. The
aggregate household financial wealth used in the second column is the total amount of wealth owned by risky asset market participants. In the lastrow we consider the aggregate portfolio of the top ten popular stocks. The value loadings and book-to-market ratio percentiles are based on stock
averages, where stocks are weighted by their shares of aggregate household portfolio reported in the second column.

Table VI
Stocks Most Widely Held by Swedish Households

Financial Characteristics
Log financial wealthLog residential real estate
L commercial
Log
i l reall estatet t
Leverage ratio
Human capital and Income Risk
Log human capital
Log income
Self-employment dummy
Unemployment dummy
Conditional income volatility
Demographic Characteristics
Age
Male household head dummyg school dummyyHigh
Post-high school dummy
Economics education dummy
Immigration dummy
Family size
2
Adjusted R
Numberu be oof obseobservations
at o s
0.016 19.21-0.169 -9.74-0.032 -1.33
0.061
3.55
-0.004 -0.18-0.321 -10.59
0.021
3.03
5.35%
188,449
88, 9

22.01
2.45
1 81
1.81
-3.01

0.005 17.20-0.061 -10.21-0.029 -3.79
0.017
2.81
-0.010 -1.28-0.086 -9.20
0.017
7.14
2.95%
287,574
8 ,5

0.144
0.004
0 002
0.002
-0.029-0.101 -4.38-0.077 -4.89-0.139 -4.29-0.069 -2.84-0.704 -11.01

8.39
4.08
15 30
15.30
1.79

0.008
-0.080-0.0350.032
0.028-0.1480.015
3.08%
98,91698,9
6-0.109-0.040-0.098-0.020-0.335

0.073
0.003
0 003
0.003
-0.033

10.33
-5.49
-1.85
2.17
1.70
-5.86
2.54

-5.20
-2.61
-3.52
-0.97
-6.35

12.87
1.90
2 29
2.29
-3.70

Not Popular
Profess Close
Profess.
(2)(3)
Estimatet-stat Estimate
t-stat-0.085 -10.53-0.021 -3.86-0.020 -1.95-0.009 -1.25-0.047 -2.21

0.019
0.002
0 007
0.007
0.005

Popular(1)
Estimatet-stat0.008-0.113-0.0250.022-0.021-0.126
0.022
3.33%
288,40988, 09-0.115-0.034-0.023-0.017-0.312

0.053
0.004
0 007
0.007
-0.00217.80-13.77
-2.37
2.65
-1.95
-9.09
6.58

-9.47
-3.85
-1.52
-1.45
-9.54

16.48
5.01
11 70
11.70
-0.49

Not Profess
Profess. Close(4)
Estimatet-statDependent Variable: Value Loading of Stock Subportfolio

This table reports pooled regressions of the value loading of household subportfolios on household characteristics in thepresence of year, industry, and county fixed effects. Every subportfolio in the table is a subset of the stock portfolio. We
consider the portfolio of popular stocks in column (1),(1) the portfolio of stocks other than popular stocks in column (2),(2) theportfolio of professionally close stocks in column (3), and the portfolio of stocks other than professionally close stocks incolumn (4). The computations are based on the representative panel of households over the 1999 to 2007 period defined in
Section 3.2. All variables are described in Table A. Standard errors are clustered at the household level.

Table VII
Value Loadings of Portfolios of Popular and Professionally Close Stocks

Initial value loading
Experience
Number of participation years
Financial Characteristics
Log financial wealthLog residential real estate wealthLog commercial real estate wealth
Leverage ratio
Human Capital and Income Risk
Log human capital
Log income
Self-employment dummy
Unemployment dummy
Conditional income volatility
Demographic Characteristics
AgegMale household head dummyHigh school dummy
Post-high school dummy
Economics education dummy
Immigration dummy
Family size
2
Adjusted R
Number of observations
-4.07
7.24
-0.24
1.73
1.23
-6.96
-0.46
-3.93
-2.27
-6.09
1.49
9
-7.36
-3.50
-2.02
-5.04
-7.63
11 72
11.72

-0.006
0.018
0.000
0.001
0.004
-0.079-0.004-0.052-0.026-0.1720.001
000-0.037-0.022-0.010-0.036-0.0730 029
0.029
15.15%
50,818Risky Portfolio(1)
Estimatet-stat0.351 35.77

0.005
0
005
-0.055-0.0770.037-0.009-0.1330 005
0.005
13.25%
27,701-0.1400.018-0.064-0.032-0.046

0.075
0.003
0.007
0.010

-0.015

5.90
5
90
-4.45
-4.63
3.00
-0.55
-6.17
0 89
0.89

-6.18
1.00
-2.45
-1.13
-0.83

12.57
2.07
7.11
0.93

-2.46

Stock Portfolio(2)
Estimatet-stat0.470 34.01

0.001
0
00
-0.003-0.003-0.015-0.019
0.007
0 012
0.012
6.12%
45,257-0.021-0.014-0.007-0.001-0.0230.002-0.0010.000
0.000-0.011

4.13
3
-0.91
-0.78
-5.27
-4.80
1.55
9 17
9.17

-3.98
-3.40
-1.18
-0.16
-1.75

1.29
-4.79
0.10
0.23

-11.42Fund Portfolio(3)
Estimatet-stat0.126 29.09

Dependent Variable: Value LoadingThis table reports pooled regressions of the value loading in 2007 on (i) the number of years in thepanel when the household participates in risky asset markets, (ii) the earliest value loading in the panel,and (iii) all the other household characteristics,
characteristics and year,
year industry,
industry and county fixed effects.
effects The
computations are based on the representative panel of households over the 1999 to 2007 period
defined in Section 3.2. All variables are described in Table A.

Table VIII
Financial Market ExperienceFinancial Characteristics
Log financial wealthLog residential real estate wealthLog commercial real estate wealth
Leverage ratio
Human Capital and Income risk
Log human capital
Log income
p y
dummy
y
Self-employment
Unemployment dummy
Conditional income volatility
Demographic Characteristics
Male household head dummyHigh school dummy
Post-highPosthigh school dummy
Economics education dummy
Family size
2
Adjusted R
Number of observations
2.00
1.59
0.72
-0.18
-4.00
-4.53
-0.05
-2.71
-7.75
-2.69
-2.27
0 30
0.30
-0.73
8.26

0.009
0.002
0.001
-0.001-0.070-0.060-0.001-0.038-0.365-0.027-0.0350 004
0.004-0.0100.040
12.56%
104,522-0.025-0.071
0 040
0.040
0.019
0.035
22.79%
43,906-0.083-0.0870.022-0.017-0.459

0.030
0.005
0.006
0.033

-0.97
-1.83
1 33
1.33
0.55
2.76

-1.93
-3.02
0.55
-0.47
-3.81

2.79
1.58
2.69
1.55

-0.019-0.005-00.012
012-0.0080.015
10.71%
87,972-0.025-0.023-0.019-0.021-0.126

0.010
0.000
0.000
0.000

-2.88
-0.50
-1
1.47
47
-0.75
4.75

-1.87
-2.41
-1.44
-2.25
-3.69

3.32
0.14
0.31
0.08

Dependent Variable: Value Loading
Risky Portfolio
Stock Portfolio
Fund Portfolio
(1)
(2)
(3)
Estimatet-statEstimatet-statEstimatet-statYearly Twin Pair Fixed Effects

This table reports pooled regressions of the value loading on household characteristics and yearly twin-pair
fixed effects estimated on the 1999 to 2007 panel of participating households with an adult twin. The valueloading is computed at the level of the risky portfolio in column (1),(1) the stock portfolio in column (2),(2) and thefund portfolio in column (3). All variables are described in Table A. Standard errors are clustered at thehousehold level.

Table IX
Twins

Financial Characteristics
C
Log financial wealthLog residential real estate wealthLog commercial real estate wealth
Leverage ratio
Human Capital and Income Risk
Log human capital
Log income
Self-employment dummy
Unemployment dummy
Conditional income volatility
Demographic
g pCharacteristics
Age
Male household head dummyHigh school dummy
Post-high school dummy
Economics education dummy
Family size
2
Adjusted R
Number of observations
2.32
-0.01
3.11
0.25
-4.56
-3.20
-0.58
-1.97
-7.05
2.58
-2.21
-1.69
-0.38
-2.83
8.59

0.012
0.000
0.003
0.002
-0.097-0.051-0.016-0.033-0.4230.002-0.026-0.025-0.004-0.0470.046
3.15%
36,230High
Communication(1)
Estimatet-stat0.002-0.020-0.001
0.005-0.0190.038
2.13%
42,588-0.094-0.047-0.011-0.035-0.375

0.010
0.002
0.002
-0.003

1.64
-1.96
-0.07
0.45
-1.11
7.27

-4.79
-3.12
-0.49
-2.11
-6.46

2.15
1.46
1.83
-0.37

Low
Communication(2)
Estimatet-statRisky Portfolio

0.009-0.0180.005
0.041-0.0370.052
5.64%
15,462-0.186-0.064-0.006-0.026-0.385

0.039
0.000
0.013
-0.002

3.37
-0.56
0.12
1.11
-0.83
3.36

-3.72
-1.88
-0.09
-0.56
-2.86

2.81
-0.01
5.55
-0.11

High
Communication(3)
Estimatet-stat0.006-0.054
0.026
0.099
0.029
0.055
4.08%
17,448-0.167-0.0530.009-0.053-0.435

0.038
0.010
0.009
0.018

2.63
-2.07
0.63
3.42
0.77
3.91

-3.39
-1.51
0.18
-1.00
-3.41

2.97
2.71
3.88
0.89

Low
Communication(4)
Estimatet-statStock Portfolio

Dependentp
Variable: Value Loading
g

0.000
0.007
-0.007-0.022-0.0340.021
2.28%
30,572-0.037-0.033-0.033-0.023-0.1610.014-0.001
0.001
0.005

0.36
1.01
-0.70
-2.59
-2.62
6.14

-2.49
-3.15
-1.86
-2.12
-4.03

4.39
-0.65
0.84
1.11

High
Communication(5)
Estimatet-stat0.000-0.0110.004-0.018-0.0110.017
1.64%
36,008-0.035-0.0330.000-0.013-0.1630.010-0.0010.001-0.005

0.49
-1.62
0.34
-2.42
-1.03
4.89

-2.38
-2.91
0.02
-1.25
-4.52

2.94
-1.80
0.71
-1.08

Low
Communication(6)
Estimatet-statFund Portfolio

This table reports pooled regressions of the value loading on yearly twin-pair fixed effects and characteristics, estimated on (a) households with twins
communicating frequently with each other (“High Communication”), and (b) households with twins communicating infrequently with each other (“LowCommunication”)). The value loading is computed at the level of the risky portfolio in columns (1) and (2),Communication(2) the stock portfolio in columns (3) and (4),(4) and thefund portfolio in columns (5) and (6). A twin pair is classified as “High Communication” if the frequency of mediated communication and the frequency ofunmediated communication are both above the median, and as “Low Communication” otherwise. The communication subsamples are obtained from the 1999
to 2007 panel of participating households with an adult twin. All variables are described in Table A of the main text. Standard errors are clustered at thehousehold level.

Table X
Communication

All twin pairs
High-communication pairs
Low-communication pairs

All twin pairs
High-communication pairs
Low-communication pairs

All twin pairs
High-communication pairs
Low-communication pairs

No Controls
Genetic
CommonComponent Component
8.7%
4.4%
33.80%-10.21%-8.25%
12.74%

Panel C: Value Loading of Fund Portfolio

No Controls
Genetic
CommonComponent Component
12.3%
13.7%
37.58%
7.55%-0.24%
11.65%

Panel B: Value Loading of Stock Portfolio

No Controls
Genetic
CommonComponent Component
17.0%
0.1%
35.39%-6.13%
0.87%
4.74%

Panel A: Value Loading of Risky Portfolio

With Controls
Genetic
CommonComponent Component
7.3%
3.8%
32.24%-10.75%-9.50%
12.69%

With Controls
Genetic
CommonComponent Component
10.8%
11.4%
37.64%
3.74%-3.91%
11.15%

With Controls
Genetic
CommonComponent Component
16.1%
-0.6%
33.05%-6.27%-0.23%
4.87%

This table reports an ACE decomposition of the value loading of households with twins over the1999 to 2007 period. We report the results for (i) all twins, (ii) twins who communicate frequently witheach other ((“HighHigh Communication
Communication”)), and (iii) twins who communicate infrequently with each other(“Low Communication”). The set of columns labeled “No Controls” presents the ACE decomposition
for the value loading itself. The set of columns labeled “With Controls” presents the ACE
decomposition for the value loading residual from the regression in Table IX. Panel A conducts theanalysis at the level of the risky portfolio, Panel B at the level of the stock portfolio, and Panel C at
the level of the fund portfolio. A twin pair is classified as “High Communication” if the frequency ofmediated communication and the frequencyqy of unmediated communication are both above themedian, and as “Low Communication” otherwise.

Table XIACE Decomposition

Components of Income Risk
- Market beta
- Value loading- Size loading- Momentum loading- Idiosyncratic volatility

78 67%
78.67%
16.59%

80.99%
13.80%

0.042
0.021
0.027
-0.0050 005-0.329

4.17
1.84
2.34
-0.35
0 35
-21.09

0.062
0.038
0.070
-0.0340 034-0.314

3.09
1.70
3.04
-1.22
1 22
-10.49

0.016
0.015
0.007
0 003
0.003
-0.116

2.81
2.21
0.99
0 42
0.42
-13.57

Dependent Variable: Value Loading
Risky Portfolio
Stock Portfolio
Fund Portfolio
(1)
(2)
(3)
Estimatet-stat Estimate
t-stat Estimate
t-statActive Households
Cross-sectional average
16.26%
46.75%
Cross-sectional standard deviation
9.89%
40.60%
Retired Households
C
Cross-sectionalti
l average
11 82%
11.82%
65 76%
65.76%
Cross-sectional standard deviation
9.37%
34.74%
Panel B. Impact of Income Risk on Portfolio Tilt

Dependent Variable: Income ShockTotal Persistence
Idiosyncratic
Volatility
Parameter
Share

Panel A: Decomposition of Income Risk

This table analyzes the systematic and idiosyncratic components of income risk, and theirrelationships to the value tilt. For every household, we regress the stochastic component oflabor income defined in Section 3.3
3 3 on the four Fama-French-Cahart
Fama French Cahart factors,
factors and refer to the
explained component as the systematic component and to the residual as idiosyncratic risk.
Panel A reports the standard deviation of labor income risk, the persistence parameter of thelabor income process, and the share of idiosyncratic risk in the standard deviation of labor
income risk. Panel B reports a pooled regression of the value loading on (i) the systematic andidiosyncratic components of income, and (ii) other characteristics, and year, industry, andcountyy fixed effects. The full results are reportedpin the Internet Appendix.
pp
The computationsp
are based on the representative panel of households over the 1999 to 2007 period defined in
Section 3.2. Standard errors are clustered at the household level.

Table XII
Decomposition of Income Risk

Value Loading Variables
Passive change in the value loading-0.356
Lagged value loading-0.116
Lagged Financial Characteristics
Log financial wealthLog residential real estate
Log commercial real estate
Leverage ratio
Lagged Income
Log human capital
Log income
Self-employment dummy
Unemployment dummy
Conditional income volatility
Lagged Demographic Characteristic
Familyy size2
6.85%
Adjusted R
Number of observations
406,561(1)
Estimate-27.63-41.95-27.61-42.55
4.81
1.97
1.64
2.30
-14.49
-1.58
-2.79
-2.28
-11.21
14.40

0.002
0.000
0.000
0.001
-0.020-0.002-0.006-0.004-0.051
0.006
0.070
406,561t-stat-0.356-0.119(2)
t-stat Estimate

Risky Portfolio

5.27%
221,143-0.372-0.078(3)
Estimate-27.30-38.24t-stat0.001
0.054
221,143-0.0310.008-0.006-0.004-0.028

0.005
0.001
0.001
0.000

-0.375-0.082(4)
Estimate

Stock Portfolio

1.83-12.31
3.21
-1.51
-1.27
-3.61

6.08
3.71
5.09
-0.04

-27.40-39.15t-stat7.06%
355,443-0.283-0.110(5)
Estimate

Dependent Variable: Active Change of Value Loading-27.95-54.30t-stat0.002
0.071
355,443-0.0090.000-0.0010.000-0.011

0.000
0.000
0.000
0.000

-0.284-0.111(6)
Estimate

Fund Portfolio

9.10-11.79
0.36
-0.69
0.00
-4.92

0.90
-1.96
1.65
0.22

-27.98-54.41t-statThis table reports pooled regressions of the active change in the value loading on (i) the passive change in the value loading and (ii) the lagged value loading.
We conduct the analysis at the level of the risky portfolio in columns (1) and (2), the stock portfolio in columns (3) and (4), and the fund portfolio in columns (5)and (6).
(6) For each portfolio,
portfolio we report the regression with and without lagged households characteristics.
characteristics All variables are demeaned each year.
year The
computations are based on the representative panel of households over the 1999 to 2007 period defined in Section 3.2. Standard errors are clustered at thehousehold level.

Table XIII
Active Rebalancing of the Value LoadingFinancial Characteristics
Log financial wealthLog residential real estate
Log commercial real estate
Leverage ratio
Human Capital and Income Risk
Log human capital
Log income
Self-employment dummy
Unemployment dummyyConditional income volatility
Demographic Characteristics
Age
Male household head dummyHigh school dummy
Post-high school dummy
Economics education dummy
Immigration dummy
Family size
2
Adjusted R
Number of observations
-1.49
-2.12
-4.51
-1.42
-5.99
2.08
-7.88
0.30
-3.72
-1.87
1.87
-2.31
5.07

-0.036-0.048-0.189-0.029-0.3280.001-0.1090.005-0.058-0.0360.036-0.0430.030
2.06%
13,927

2.25
-1.46
0.13
4.98

t-stat0.015-0.002
0.000
0.015

(1)
Estimate

Risky Portfolio

3.73%
4,779

0.007
-0.1260.021-0.0890.000-0.0610.010-0.059-0.041-0.243-0.126-0.2530.106-0.001
0.009
0.037

(2)
Estimate

4.38
-3.57
0.49
-2.46
0.00
-1.37
0.69

-1.26
-0.87
-3.43
-2.20
-2.21

6.82
-0.43
2.27
3.79

t-statStock Portfolio

0.44%
10,4720.000-0.0200.007-0.012-0.016
0.016
0.017
0.006

-0.005-0.0180.001
0.012-0.0650.014-0.001
0.000
0.000

(3)
Estimate

1.08
-2.37
0.74
-1.33
-1.33
1.33
1.64
1.88

-0.38
-1.95
0.08
1.10
-2.11

3.78
-0.99
0.39
-0.22

t-statFund Portfolio

Dependent Variable: Value LoadingThis table reports pooled regressions of a new participant’s value loading on socioeconomic
characteristics and year, industry, and county fixed effects. We conduct the estimation on households thatenter the stock market between 1999 to 2007 in the representative sample defined in Section 3.2.
3 2 Theanalysis is based on data in the year of entry. All variables are described in Table A.

Table XIV
Value Loadings of New ParticipantsAge Dummies
30 and above
35 and above
40 andd aboveb
45 and above
50 and above
55 and above
60 and above
65 and above
70 and above
New Entrant Dummies
New entrant aged 30+
New entrant aged 35+
New entrant aged 40+
New entrant aged 45+
New entrant aged 50+
New entrant aged 55+
New entrant aged 60+
New entrant aged 65+
New entrant aged 70+
0.45
1.23
2 87
2.87
3.55
4.49
5.98
3.49
3.52
5.62
-5.99
-0.35
-1.42
-0.28
0.07
-0.93
-0.04
-2.96
-0.45

0.003
0.005
0 010
0.010
0.012
0.015
0.019
0.011
0.019
0.058
-0.091-0.007-0.032-0.0070.001-0.020-0.001-0.119-0.010Risky Portfolio(1)
Estimatet-stat-0.252-0.007-0.090-0.0250.037-0.029-0.068-0.028-0.013

0.052
0.047
0 039
0.039
0.058
0.040
0.037
0.022
0.021
0.139
-6.14
-0.12
-1.48
-0.41
0.69
-0.61
-1.23
-0.39
-0.26

3.29
4.28
4 14
4.14
6.62
5.17
5.60
3.36
1.92
6.47

Stock Portfolio(2)
Estimatet-stat-0.007-0.008
0.004
0.000
0.005
-0.009-0.002-0.010
0.032

0.000
0.000
0 005
0.005
0.002
0.000
0.002
0.003
0.006
0.023

-0.83
-0.62
0.29
-0.02
0.40
-0.65
-0.12
-0.43
2.58

-0.07
-0.05
2 11
2.11
0.91
0.15
1.07
1.59
1.66
3.46

Fund Portfolio(3)
Estimatet-statDependent Variable: Value LoadingThis table reports a pooled regression of the value loading on (i) age dummies for participating households,(ii) age dummies for households that enter risky asset markets during the year, and (iii) all the othercharacteristics of the baseline regression,
regression and year,
year industry,
industry and county fixed effects.
effects Age dummies are
cumulative. The computations are based on the representative sample of households over the 1999 to 2007
period defined in Section 3.2. All variables are discussed in Table A of the main text. Standard errors are
clustered at the household level. The full results are available in the Internet Appendix.

Table XVExisting vs. New Participants

AgeFinancial wealth ($)
Residential real estate ($)
Commercial real estate ($)
Leverage
Income ($)
Human capital ($)
Total effectFraction due to age

Demographic Group
30-Year Old
70-Year Old
30
7030,247
126,547
126,329
175,897
12,957
48,668
0 91
0.91
0 06
0.06
45,25560,367
1,237,705
305,171

Variable

Marginal Effect on Value Loading
Risky Portfolio Stock Portfolio Fund Portfolio
0.105
0.341
0.023
0.021
0.067
0.015
0.012
0.035
0.004
0.016
0.029
0.006
0 010
0.010
0 033
0.033
0 004
0.004
-0.013-0.012-0.008
0.069
0.142
0.027
0.220
0.636
0.071
47.76%
53.59%
32.89%

Marginal Effect on Value Loading
Risky Portfolio Stock Portfolio Fund Portfolio
Age
0.109
0.353
0.023
Financial wealth ($)
0.024
0.071
0.017
Residential real estate ($)
0.000
0.001
0.000
Commercial real estate ($)
0.001
0.010
0.000
Leverage
0.000
0.006
0.001
I
Income($)-0.0130 013-0.0130 013-0.0080 008
Human capital ($)
0.073
0.145
0.030
Total effect
0.195
0.574
0.062
Fraction due to age
56.0%
61.6%
36.9%
Panel B: Economic Impact with Real Estate-Leverage Interaction

Demographic Group
30-Year30
Year Old
70 Year Old
70-Year30
70
30,247
126,547
126,329
175,897
12,957
48,6680.91
0.06
45 25545,25560 36760,367
1,237,705
305,171

Variable

Panel A: Economic Impact Computed from Baseline Regression

This table reports the impact on the value loading of life-cycle variation in age and financial characteristics. We use
as benchmarks a 30-year old household and a 70-year old household, to which we assign the averagecharacteristics of households in their respective cohorts in 2003.
2003 In Panel A,
A the impact of changes in characteristics
is assessed using the baseline regression coefficients in Table III. Panel B is based on the specification estimated inTable IV, Panel A, in which real estate is interacted with leverage. All variables are described in Table A.

Table XVI
Economic SignificanceDummy variable equal to one if the household head has a high school degree.
Dummy variable equal to one if the household head has had some post-high school education.
Dummy variable equal to one if the household head received education in an field related toeconomics and management.
Number of people living in the household.

High school dummy

Post-high school dummy

Economics education dummy

Family sizeAge of the household head.

Standard deviation of the total income shock, defined as the sum of the persistent andtransitory income shocks in a given year.

Conditional income volatility

Dummy variable equal to one if the household head is male.

Dummy variable equal to one if the household head is unemployed.

Unemployment dummyMale household head dummy

Dummy variable equal to one if the household head is self-employed.

Self-employment dummy

AgeExpected present value of future nonnon-financial
financial disposable real income
income.
Total household disposable income.

Total debt divided by the sum of financial and real estate wealth.

Leverage ratio

IncomeValue of rental, industrial, and agricultural property.

Commercial real estate wealth

Human capital

Number of assets in the fund portfolio.

Number of assets in the stock portfolio.

Number of stocks
Value of primary and secondary residences.

Fraction of the stock portfolio invested in firms with the same 1-digit industry code as an adultyhousehold member's current employer.

Share of professionally close stocks

Residential real estate wealth

Fraction of the household stock portfolio invested in public firms which were one of the ten
most widely held in at least one year between 1999 and 2007.

Share of popular stocks

Number of funds

Proportionpof riskyy assets in the pportfolio of cash and risky
y financial assets.
Value of holdings in cash, risky financial assets, capital insurance products, derivatives, anddirectly held bonds, excluding illiquid assets and defined contribution retirement accounts.

Financial wealth

Risky portfolio

y shareRisky

Portfolio of directly held stocks.
Portfolio of risky mutual funds and directly held stocks.

Stock portfolio

Bank account balances and money market funds.
Portfolio of mutual funds other than money market funds.

D
Description
i ti

Cash
Fund portfolio

V i blVariableThis table summarizes the main household variables used in the paper.

Table A
Definition of Household Variables

% of Eq
quity Held by H
Households

500 - 1K

1K - 5K

5K - 10K

10K+

Firm Market Capitalization ($ Million)

0%
100 - 500

0%50 - 100

5%10%

10 - 50

10%20%

5 - 10

15%

30%

1-5

20%

40%

0-1

25%

30%

50%

60%

This figure illustrates (i) the percentage of firm market capitalizations owned directly by Swedish households at the end of 2003 as function of firm size
(solid bars and left axis), and (ii) the distribution of firm size (solid line and right axis). The calculations are based on all the 352 firms listed on Swedishexchanges (SSE,(SSE ATO,ATO NGM,NGM INO) and all Swedish households that own stocks at the end of 2003.
2003

Figure 1
Percentage of Public Equity Directly Held by Households

Distribution of firms ((%)

Figure 2
The Value Ladder

-0.4
04

-0.3

-0.2
02

-0.1

0

0.1

02
0.2

0.3

04
0.4

30

35

40

45

50

Age

55

60

65

70

75

This figure illustrates the value loading of the stock portfolio for different cohorts of households. Each solid line corresponds to a givencohort, defined as a 5-year age bin. The first cohort contains households with a head aged between 30 and 34 in 1999, while the oldest
cohorth has
h a headh d agedd betweenb
70 andd 74 ini 1999.
1999 TheTh loadingsl di
off allll householdshh ld ini year t are demeaneddd to controll forf changes
h
i the
in
h
composition of the Swedish stock market. A cohort’s loading in year t is the wealth-weighted average year-t loading of households in thecohort. The figure is based on the panel of all Swedish direct stockholders over the 1999 to 2007 period.

Value Looading