Who are the Value and Growth Investors?∗

Sebastien Betermier, Laurent E. Calvet, and Paolo Sodini

First version: October 2013This version: May 2015

Abstract

This paper investigates value and growth investing in a large administrative panel of Swedishresidents over the 1999-2007 period. We show that over the life-cycle, households progres-sively shift from growth to value as they become older and their balance sheets improve. Weverify that households climb the value ladder by actively rebalancing their stock and fundholdings. Furthermore, investors with low human capital and low exposure to aggregate la-bor income shocks tilt their portfolios toward value. While several behavioral biases seemevident in the data, the patterns we uncover are overall strikingly consistent with risk-basedexplanations of the value premium.

∗Betermier: Desautels Faculty of Management, McGill University, 1001 Sherbrooke St West, Montreal, QC H3A1G5, 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 are grateful to Kenneth Single-ton (the Editor), the Associate Editor, and an anonymous referee for many insightful comments. We thank StephanJank, Claus Munk, Per Östberg, Jonathan Parker, Sébastien Pouget, and Shaojun Zhang for helpful discussions, andacknowledge constructive comments from Laurent Barras, John Campbell, Chris Carroll, Luigi Guiso, Marcin Kacper-czyk, Bige Kahraman, Hugues Langlois, Alex Michaelides, Ben Ranish, David Robinson, Johan Walden, and seminarparticipants at the City University of Hong Kong, Copenhagen Business School, HEC Montréal, HEC Paris, Impe-rial College Business School, Lund University, McGill University, Peking University, the Securities and ExchangeCommission, the Swedish School of Economics, the Toulouse School of Economics, Université de Sherbrooke, theUniversity of Helsinki, the University of Southern Denmark, the Norges Bank Household Finance Workshop, the 2014IFM2 Math Finance Days, the 2014 China International Conference in Finance, the 2014 NBER Summer Institute As-set Pricing Workshop, the 2014 European Conference on Household Finance, and the 2015 Cologne Colloquium onFinancial Markets. We thank Statistics Sweden and the Swedish Twin Registry for providing the data. The projectbenefited from excellent research assistance by Milen Stoyanov, Tomas Thörnqvist, Pavels Berezovkis, and espe-cially Andrejs Delmans. This material is based upon work supported by Agence Nationale de la Recherche, BFI, theHEC Foundation, Riksbank, the Social Sciences and Humanities Research Council of Canada, and the Wallander andHedelius Foundation.

Who are the Value and Growth Investors?

ABSTRACT

This paper investigates value and growth investing in a large administrative panel of

Swedish residents over the 1999-2007 period. We show that over the life-cycle, house-

holds progressively shift from growth to value as they become older and their balance

sheets improve. We verify that households climb the value ladder by actively rebal-

ancing their stock and fund holdings. Furthermore, investors with low human capital

and low exposure to aggregate labor income shocks tilt their portfolios toward value.

While several behavioral biases seem evident in the data, the patterns we uncover are

overall strikingly consistent with risk-based explanations of the value premium.

JEL Classification: G11, G12.

Keywords: Asset pricing, value premium, household finance, portfolio allocation, hu-

man capital, factor-based investing.

1 Introduction

A large academic and practitioner literature documents that value stocks outperform growth stocks

on average in the United States and around the world (Basu 1977, Fama and French 1992, 1998,

Graham and Dodd 1934).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 of system-

atic risk other than market portfolio return risk (Fama and French 1992, 1995), such as aggre-

gate labor income and consumption shocks (Cochrane 1999, Jagannathan and Wang 1996, Let-

tau and Ludvigson 2001, Petkova and Zhang 2005, Yogo 2006),2 cash-flow risk (Campbell and

Vuolteenaho 2004), long-run consumption risk (Bansal, Dittmar, and Lundblad 2005, Hansen,

Heaton, and Li 2008),3 the costly reversibility of physical capital (Zhang 2005), or displacement

risk (Garleanu, Kogan, and Panageas 2012).4 The underperformance of growth stocks relative to

value stocks may also be evidence that investors are irrationally exuberant about the prospects of in-

novative glamour companies (DeBondt and Thaler 1985, Lakonishok, Shleifer, and Vishny 1994).5

The extensive empirical literature on the value premium focuses primarily on stock returns and

their relationships to macroeconomic and corporate data. Disentangling theories of the value pre-

mium, however, has proven to be challenging on traditional data sets that do not provide individual

positions and therefore do not permit researchers to assess the determinants of investor decisions.6

The present paper proposes to use the rich information in investor portfolios to shed light on ex-

planations of the value premium. We investigate value and growth investing in a highly detailed

1See also Asness, Moskowitz, and Pedersen (2013), Ball (1978), Basu (1983), Capaul, Rowley, and Sharpe (1993),Chan, Hamao, and Lakonishok (1991), Fama and French (1993, 1996, 2012), Griffin (2003), Liew and Vassalou(2000), and Rosenberg, Reid, and Lanstein (1985). Some recent work also shows that the strength of the valuepremium can be improved by refining the sorting methodology (Asness and Frazzini 2013, Barras 2013, Hou, Karolyi,and Kho 2011).

2Eiling (2013), Jagannathan, Kubota, and Takehara (1998), Addoum, Korniotis, and Kumar (2013), and Santosand Veronesi (2006) provide further evidence on the relationship between labor income and the value premium.

3See also Bansal, Kiku, Shaliastovich, and Yaron (2014), Bansal, Dittmar, and Kiku (2009), and Gulen, Xing, andZhang (2011).

4Other 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 tendto provide low dividends when the aggregate housing collateral is low (Lustig and van Nieuwerburgh 2005).

5See also Barberis and Thaler (2003), Daniel, Hirshleifer, and Subrahmanyam (2001), La Porta, Lakonishok,Shleifer, and Vishny (1997), and Shleifer (2000).

6See Liu, Lu, Sun, and Yan (2015) for a recent discussion.

1

administrative panel, which contains the disaggregated holdings and socioeconomic characteris-

tics of all Swedish residents between 1999 and 2007. The data set reports portfolio holdings at the

level of each stock or fund, along with other forms of wealth, debt, labor income, and employment

sector.

The paper makes four main contributions to the literature. First, we show that the value tilt

exhibits substantial heterogeneity across households. When we sort investors by the value tilt of

their risky asset portfolios, the difference in expected returns between the top and bottom deciles

is approximately equal to the value premium. Over the life-cycle, households climb the “value

ladder”, i.e. they gradually shift from growth to value investing as their investment horizons and

financial circumstances evolve. The value ladder is made possible by active rebalancing, which

allows households to mitigate the impact of realized returns and revert to their slow-moving target.

The positive relationship between age and the value loading is also evident among new participants,

whose portfolios are not passively affected by past returns.

Second, we relate the value tilt to household characteristics. We show that value investors are

substantially older, have higher financial wealth, higher real estate wealth, lower leverage, lower

income risk, lower human capital, and are also more likely to be female, than the average growth

investor. By contrast, men, entrepreneurs, and educated investors are more likely to invest in

growth stocks. These baseline patterns are evident both in stock and mutual fund holdings, and

are robust to controlling for the length of risky asset market participation and other measures of

financial sophistication. The explanatory power of socioeconomic characteristics is especially high

among households that invest directly in at least five companies, a wealthy subgroup that owns the

bulk of aggregate equity.

Third, we show that households adjust their portfolio value loadings to systematic risk in their

employment sectors. We uncover a strong factor structure in the panel of industry per-capita in-

come growth and show that a single macroeconomic factor, per-capita aggregate income growth,

explains on average 88% of the time-series variation of per-capita income in any given 2-digit SIC

industry. Households employed in sectors with high exposures to the macroeconomic factor tend

to select portfolios of stocks and funds with low value loadings. We obtain similar results when

2

we measure systematic risk by using industry exposures to the value factor.

Fourth, we verify that our results are robust to a large number of alternative hypotheses which

include financial market experience or stock characteristics other than the value loading, such as

professional proximity, the dividend yield, taxes, firm age, skewness, and size. We document that

the equities most widely held by households are a mix of growth stocks and value stocks, and

that the relationships between portfolio tilts and investor characteristics are unlikely to be driven

by these stocks. As in Calvet and Sodini (2014), we consider the subsample of Swedish twins

to control for latent investor fixed effects, such as family background, upbringing, inheritance, or

attitudes toward risk. The sensitivities of the value loading to socioeconomic characteristics are

similar in the twin subsample as in the general household population, regardless of whether or not

the twins communicate frequently or infrequently with each other.

The patterns that we uncover in the Swedish portfolio data appear remarkably consistent with

the implications of risk-based theories for the cross-section of portfolio tilts. Value stocks are held

by households that are in the best position to take systematic financial risk, for instance because

they have sound balance sheets or low background risk. We document that young households

with long investment horizons go growth and progressively migrate toward value as they get older,

which provides strong empirical support for intertemporal hedging explanations (Jurek and Viceira

2011, Larsen and Munk 2012, Lynch 2001). In addition, investors with high human capital and

high exposure to aggregate labor income shocks tilt away from value, which is in line with labor-

based theories of the value premium. These empirical regularities are stronger among households

that invest directly in at least five stocks, have high risky shares, and own the bulk of aggregate

equity.

The Swedish data set provides highly detailed information on household finances and demo-

graphics but is somewhat less informative about psychological traits. With this caveat, we find that

behavioral explanations of the value premium also help to explain the portfolio evidence. Overcon-

fidence, which is more prevalent among men than women (Barber and Odean 2001), is consistent

with the preference of male investors for growth stocks. As attention theory predicts (Barber and

Odean 2008), a majority of direct stockholders hold a small number of popular stocks. Further-

3

more, some of the empirical regularities documented in the paper can receive complementary risk-

based and psychological explanations. For instance, the tilt of entrepreneurs toward growth stocks

can be attributed both to a marked overconfidence in own decision-making skills (Busenitz and

Barney 1997) and to exposure to private business risk (Moskowitz and Vissing-Jørgensen 2002).

The evidence reported in this paper contributes to the growing body of work showing the rel-

evance of portfolio theory for explaining household financial behavior. Retail investors allocate

a high share of liquid financial wealth to risky assets if they have high financial wealth and hu-

man capital (Calvet and Sodini 2014), earn safe labor incomes (Betermier, Jansson, Parlour, and

Walden 2012, Calvet and Sodini 2014, Guiso, Jappelli, and Terlizzese 1996), and are not en-

trepreneurs (Heaton and Lucas 2000).7 Households actively rebalance their financial portfolios

in response to realized returns (Calvet, Campbell, and Sodini 2009a). Furthermore, a major-

ity of households incur small welfare losses from underdiversification (Calvet, Campbell, and

Sodini 2007). We document here that financial theory also accounts for the cross-sectional and

time-series properties of household value tilts.

Our results complement the literature showing that retail investors favor assets with certain

characteristics, such as familiar stocks,8 and adjust their investment styles to news and past ex-

perience (Kumar 2009a, Campbell, Ramadorai, and Ranish 2014). The Swedish panel contains

high-quality data on holdings and socioeconomic characteristics, which allows us to uncover new

patterns in the household demand for value and growth stocks. The paper also sheds light on the

potential influence of genes. Cronqvist, Siegel, and Yu (2015) estimate a variance decomposition

of the portfolios held by twins and conclude that value investing has a strong genetic component.

The present paper demonstrates that the so-called “genetic” component is negligible among twins

that communicate infrequently with each other, which suggests that simple variance decomposi-

tions severely overestimate the impact of genes. The value tilt is not simply encoded in the DNA

of retail investors, but is also strongly driven by financial circumstances and communication.

7See also Angerer and Lam (2009), Bonaparte, Korniotis, and Kumar (2014), and Knüpfer, Rantapuska, andSarvimäki (2013).

8Households are known to favor stocks that are familiar (Døskeland and Hvide 2011, Huberman 2001, Massa andSimonov 2006), geographically and culturally close (Grinblatt and Keloharju 2001), attention-grabbing (Barber andOdean 2008), or connected to products they consume (Keloharju, Knüpfer, and Linnainmaa 2012).

4

The rest of the paper is organized as follows. Section 2 presents the data and defines the main

variables. Section 3 reports the cross-sectional distribution of the value loading. Section 4 docu-

ments the value ladder and empirically investigates how the value tilt relates to the demographic

and financial characteristics of households. Section 5 relates the evidence to risk- and sentiment-

based explanations of the value premium. Section 6 presents robustness checks and Section 7

concludes. An Internet Appendix (Betermier, Calvet, and Sodini 2015) develops an equilibrium

model of the cross-section of portfolio tilts, discusses estimation details, and reports additional

empirical results.

2 Data and Construction of Variables

2.1 Local Fama and French Factors

Data on Nordic stock markets for the 1985 to 2009 period are available from FINBAS, a financial

database maintained 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. Free-

float adjusted market shares are available from Datastream. We focus on stocks with at least two

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, out of which 743 are listed on one of the

four major Nordic exchanges in 2003.9

The return on 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 market factor MKTt is

the market return minus the risk-free rate in month t. The local value, size, and momentum factors

are constructed as in Fama and French (1993) and Carhart (1997). We sort the stocks traded on

the major Nordic exchanges by book-to-market value, market size, and past performance. We use

9The major Nordic exchanges are the Stockholm Stock Exchange, the Copenhagen Stock Exchange, the HelsinkiStock Exchange, and the Oslo Stock Exchange.

5

these bins to compute the value factor HMLt , the size factor SMBt , and the momentum factor

MOMt , as is fully explained in the Internet Appendix.

We index stocks and funds by i ∈ {1, . . . , I}. For every asset i, we estimate the four-factor

model:

rei,t = ai +bi MKTt + vi HMLt + si SMBt +mi MOMt +ui,t , (1)

where rei,t denotes the excess return of asset i in month t and ui,t is a residual uncorrelated to the

factors. Estimated loadings are winsorized at -5 and +5. The value premium is substantial in

Sweden: HMLt averages to about 10% per year over the 1985 to 2009 period, which is consistent

with the Sweden estimate in Fama and French (1998) and also in the range of country estimates

reported in Liew and Vassalou (2000). In the Internet Appendix, we also show the Swedish HML

factor shares many similar properties with its U.S. equivalent.

2.2 Household Panel Data

The Swedish Wealth Registry is an administrative data set compiled by Statistics Sweden, which

has previously been used in household finance (Calvet, Campbell, and Sodini 2007, 2009a, 2009b,

Calvet and Sodini 2014). Statistics Sweden and the tax authority had until 2007 a parliamen-

tary mandate to collect highly detailed information on every resident. Income and demographic

variables, such as age, gender, marital status, nationality, birthplace, education, and municipality

of residence, are available on December 31 of each year from 1983 to 2007. The disaggregated

wealth data include the worldwide assets owned by the resident at year-end from 1999 to 2007.

Real estate, debt, bank accounts, stockholdings, and mutual fund investments are observed at the

level of each property, account, or security. Statistics Sweden provides a household identification

number for each resident, which allows us to group residents by living units. The age, gender,

education and immigration variables used in the paper refer to the household head.

We focus on households that participate in risky asset markets. Unless stated otherwise, the

results are based on representative random sample of approximately 70,000 households observed

at the yearly frequency between 1999 and 2007. The data requirements imposed on households

6

and the method used to construct the random panel are fully explained in the Internet Appendix.

We also use a twin panel from the Swedish Twin Registry, the largest twin database in the

world. The registry provides the genetic relationship (fraternal or identical) of each pair and the in-

tensity of communication between the twins. We have merged the twin data base with the Swedish

Wealth Registry, so that all financial and demographic characteristics are available for the twin

panel.

2.3 Definition of Main Variables

2.3.1 Financial Assets and Real Estate

We use the following definitions throughout the paper. Cash consists of bank account balances and

Swedish money market funds.10 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 with less than 3 months of return data.

For every household h, the risky portfolio contains risky financial assets. The risky share is

the fraction of risky financial assets in the portfolio of cash and risky financial assets. A market

participant has a strictly positive risky share.

The value loading of the risky portfolio at time t is the weighted average of individual asset

loadings:

vh,t =I

∑i=1

wh,i,tvi, (2)

where wh,i,t denotes the weight of asset i in household h’s risky portfolio at time t. We will oc-

casionally call vh,t the HML loading or the value tilt. The value loadings of the fund and stock

portfolios are similarly defined. The estimation methodology takes advantage of (i) the detailed

yearly data available for household portfolios, which permit the calculation of wh,i,t , and (ii) the

10Financial institutions are required to report the bank account balance at year-end if the account yields less than100 Swedish kronor during the year (1999 to 2005 period), or if the year-end bank account balance exceeds 10,000Swedish kronor (2006 and 2007). We impute unreported cash balances by following the method used in Calvet,Campbell, and Sodini (2007, 2009a, 2009b) and Calvet and Sodini (2014), as is explained in the Internet Appendix.

7

long monthly series available for individual assets, which permit the precise estimation of vi. Un-

der the unconditional pricing model (1), individual firms have constant value loadings, vi, so that

time variation in household portfolio loading, vh,t , in (2) are driven exclusively by time variation

in portfolio weights. Thus, in Section 4, our estimates of active management of the value tilt by

households will not be contaminated by exogenous changes in firm tilts over the 1999-2007 sample

period.

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 con-

sideration illiquid assets such as real estate or consumer durables, and defined contribution re-

tirement accounts. Also, our measure of wealth is gross financial wealth and does not subtract

mortgage or other household debt. Residential real estate consists of primary and secondary res-

idences, while commercial real estate consists of rental, industrial and agricultural property. The

leverage ratio is defined as the household’s total debt divided by the household’s financial and real

estate wealth.

2.3.2 Human Capital

We consider a labor income specification based on Carroll and Samwick (1997) that accounts for

the persistence of income shocks. Specifically, we assume that the real income of household h in

year t, denoted by Lh,t , satisfies

log(Lh,t) = ah +b′xh,t +θh,t + εh,t , (3)

where ah is a household fixed effect, xh,t is a vector of age and retirement dummies, θh,t is a persis-

tent 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 ,

8

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 household bins sorted by (i) immigration status, (ii) gender, 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 σ2

ε,h using the Kalman filter on each

household income series.

As is customary in the portfolio-choice literature (e.g., Cocco, Gomes, and Maenhout 2005),

we assume that the household observes both the persistent and transitory components of income.

At a given date t−1, the household knows the contemporaneous component θh,t−1 and next-period

characteristics xh,t . The period-t log labor income, log(Lh,t), therefore has conditional stochastic

component

ηh,t = ξh,t + εh,t , (4)

and conditional variance

σ2h = Vart−1(ηh,t) = σ2

ξ,h +σ2ε,h.

We call σh the conditional volatility of income and use it as a measure of income risk.

We define expected human capital as

HCh,t =Th

∑n=1

Πh,t,t+n

Et(Lh,t+n)

(1+ r)n, (5)

where Th denotes the difference between 100 and the age of household h at date t, and Πh,t,t+n

denotes the probability that the household head h alive at t is still alive at date t + n. We make

the simplifying assumption that no individual lives longer than 100. The survival probability is

imputed from the life table provided by Statistics Sweden. The discount rate r is set equal to 5% per

year. We have verified that our results are robust to alternative choices of r. Detailed descriptions

of the labor income and human capital imputations are provided in the Internet Appendix.

9

2.4 Summary Statistics on Participating Households

Table I reports summary statistics on risky asset market participants (first set of columns), mutual

fund owners (second set of columns), direct stockholders (third set of columns), and direct stock-

holders sorted by the number of stocks that they own (last set of columns) 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 participating household has a 46-year old head and a yearly income of $45,000.

It owns about 1 year of income in liquid financial wealth, 3 years of income in real estate wealth,

and 21 years of income in human capital. Within the financial portfolio, 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 vast majority of risky asset participants (88%) hold

mutual funds, while 59% of them own stocks directly.

About half of direct stockholders invest in 1 or 2 companies; they have modest levels of finan-

cial wealth ($35,000), and low risky shares. We classify a stock as popular if it is one of the 10

most widely held by the household sector in at least one year between 1999 and 2007. Popular

stocks, which account for 59% of the Swedish equity market, represent 79% of the direct hold-

ings of households with 1 or 2 stocks. The diversification losses of these households are modest,

however, because concentrated stock portfolios represent only a small fraction of their financial

wealth.11

By contrast, almost 30% of direct stockholders own at least 5 different stocks. This subgroup

is important for the following reasons. Households in the subgroup have high education levels

and exhibit no bias toward popular stocks. They also have substantially higher financial wealth

($125,000) and select a higher risky share (61%) than the average investor, and correspondingly

own the bulk of aggregate equity. In the bottom rows of Table I, Panel B, we report the fraction of

the aggregate portfolio held by specific subsets of investors. The aggregate portfolio is constructed

11See Calvet, Campbell, and Sodini (2007) for a detailed analysis.

10

by adding up the stock and fund holdings of all participants. Households owning 5 stocks or more,

which represent only 17% of all participants, own 36% of aggregate mutual fund holdings and

80% of aggregate direct stockholdings. They therefore account for a substantial fraction of the

household demand for risky assets. Polkovnichenko (2005) obtains very similar statistics for this

wealthy subgroup in the U.S. Survey of Consumer Finances.

Households are not heavily tilted toward stocks in their employment sector. 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 and consistent with the

evidence from Norway (Døskeland and Hvide 2011).

In Figure 1, we report the fraction of corporate equity held by Swedish households. Specif-

ically, we sort firms by market capitalization, and for each size bucket we report the fraction of

firms in the size bucket (solid line) and the fraction of equity owned directly by Swedish house-

holds (solid bars). 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.12 For the majority of Swedish com-

panies, the aggregate demand from the household sector is therefore substantial and can potentially

have a sizable impact on stock prices.

3 The Cross-Section of Household Tilts

Table II reports the distribution of the value loading of individual stocks at the end of 2003. The

loadings of individual stocks range from -3.22 (10th percentile) to 0.94 (90th percentile), with a

median of -0.37. The distribution of the value loading across individual stocks is therefore highly

heterogenous and negatively skewed. The value-weighted (VW) portfolio of Swedish stocks,

which by construction coincides with the SIXRX market index, has a value loading of -0.15 in

12In the Internet Appendix, we verify that the share of equity held by the household sector is the same for value andgrowth firms.

11

2003.13 We will therefore view a value loading of -0.15 in 2003 as being neutral. The equal-

weighted (EW) average stock loading is more negative than its VW counterpart, which stems from

the large number of small growth stocks.

Like individual stocks, household portfolios exhibit substantial heterogeneity in their value

loadings. Among participants, the loading of the risky portfolio ranges from -0.94 (10th percentile)

to 0.10 (90th percentile); the implied expected return differential is therefore approximately equal

to the value premium.14 The median loading is neutral at -0.18, so the loading distribution is neg-

atively skewed. Cross-sectional heterogeneity is slightly stronger for stock portfolios, as intuition

suggests.

The aggregate risky portfolio of the household sector has a loading of -0.26, which confirms

that the household sector as a whole exhibits only a mild growth tilt. This slight tilt originates

from the aggregate stock portfolio, which has a loading of -0.36, while the aggregate fund portfo-

lio is neutral. Moreover, whether we consider stocks or funds, the EW average household has a

more negative tilt than that its VW counterpart. A natural explanation is that low-wealth house-

holds invest in growth stocks, while high-wealth households invest in value stocks. We test this

explanation in the next section.

4 What Drives the Value Tilt?

4.1 The Value Ladder

In Figure 2, we illustrate that households progressively switch from growth to value stocks over

the life-cycle, a phenomenon which we call the “value ladder.” We sort households by birthyear

into 9 cohorts, and for each cohort we plot the average VW value loading between 1997 and 2007.

The figure is based on the stock portfolios of all Swedish households that directly hold equities

13As equation (2) 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.

14In the Internet Appendix, we report standard errors for the loading percentiles and infer that their difference arehighly significant. We also show that the return differential is slightly higher for households owning 5 stock or more,which suggests that heterogeneous loadings are not just the by-product of portfolio underdiversification.

12

during the period. We weigh households by their 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 the stockmarket and the entry of new stocks. A similar ladder also exists

with the EW average loading or for the risky portfolio, as the Internet Appendix shows.

The value ladder in Figure 2 indicates that between the ages of 30 and 70, the value loading

varies by 0.58 and the implied return differential is therefore about one half of the value premium

(5.8% per year), which is economically substantial. The relationship between the loading and age

is strikingly linear, and in every cohort there is a tendency for households to migrate toward higher

loadings as time goes by. We note that the value ladder cannot be explained by cohort effects alone,

since such effects can explain the average loading of a cohort but not the migration of each cohort

toward value stocks. A tight combination of time and cohort effects would therefore be required

to generate the value ladder in the absence of age effects. Such a structure might originate from

inertia and other mechanical effects driving the portfolio tilt, but would otherwise be economically

challenging to explain.

From a portfolio-choice perspective, the value ladder can be attributed to (i) changes in financial

conditions over the life-cycle and (ii) pure investment horizon effects. In Sections 4.2 to 4.4, we

run pooled panel regressions to quantify the respective roles of age, financial characteristics, and

income risk on the value ladder. In Section 4.5, we consider the impact of purely mechanical

effects, such as portfolio inertia, by investigating if households actively rebalance their value tilts

and by analyzing how new entrants select their initial portfolios. These sections indirectly answer

concerns about cohort and time effects. The value ladder might also originate from learning, time-

variation in overconfidence, or peer effects. In Section 6 and in the Internet Appendix, we use

measures of sophistication, financial market experience, interpersonal communication, and firm

age to control for such mechanisms.

13

4.2 Demographic and Financial Determinants

Table III maps the relationships between portfolio tilts and socioeconomic variables. We report

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. In the first three columns, we consider the value loading at the level

of (1) the risky portfolio, (2) the stock portfolio, and (3) the fund portfolio. In column (4), we

regress the risky share on characteristics. Standard errors are clustered at the household level.

The financial wealth coefficient is positive and strongly significant for all three portfolios.

Households with more liquid financial wealth tend have a higher value tilt than other households.

The financial wealth coefficient is the highest for the stock portfolio, which suggests that wealthy

households achieve a value tilt primarily via direct stockholdings. This finding is consistent with

the fact that mutual funds tend to have fairly neutral HML loadings (see Table II).

Households with high current income Lh,t and high expected human capital HCh,t (as defined in

equation (5)) tilt their financial portfolios toward growth stocks. These relationships are significant

for all three portfolios. Measures of income risk also have strongly negative coefficients: house-

holds with high income volatility and a self-employed or unemployed head are prone to selecting

growth stocks.15 Expected human capital and the volatility of the income process therefore both

tilt household financial portfolios toward growth stocks.

Demographic characteristics are significantly related to the value tilt. The age of the household

head tends to increase the value loading in the regression, which suggests that age is a likely

contributor to the value ladder. Younger households tend to go growth and older households tend

to go value, primarily through direct stockholdings. The gender variable is strongly significant:

men have a growth tilt and women a value tilt. Immigrants and educated households both tend to

go growth, which suggests that the value loading is not just driven by sophistication.

In Table IV, we reestimate the baseline regression on five separate groups of investors: (1) mu-

15In the Internet Appendix, we verify that our findings are robust to regressing the value tilt on the persistent andtransitory components of income risk, σξ,h and σε,h, instead of the total volatility σh. We also show that the results arerobust to alternative definitions of the income process.

14

tual fund owners, (2) direct stockholders, and (3) to (5) direct stockholders sorted by the number of

firms that they own. The baseline results remain valid in all groups. Furthermore, the explanatory

power of the regression is twice as high for households with at least 3 stocks as for households

with 1 or 2 stocks. Thus, wealthier, more educated direct stockholders holding at least three stocks

are prone to selecting value tilts that are well explained by their financial and demographic char-

acteristics.

Tables III and IV raise some immediate questions about real estate wealth, which are important

for the interpretation of the results and their connections with risk-based theories. The effects

of real estate and leverage on the value loading are relatively weak in the panel regressions. In

Table III, real estate has a positive but small effect for the risky and stock portfolios, and no effect

for the fund portfolio. Likewise, leverage has a negative effect on the value loading of the stock

portfolio, but no effect for the risky and the fund portfolios. These weak results are potentially due

to the fact that real estate is both (i) a form of wealth and (ii) a source of background risk. The

strength of the two channels is likely influenced by the level of leverage.

In Table V, Panel A, we obtain stronger results by interacting demeaned real estate with de-

meaned leverage. The leverage ratio as a standalone variable has a strongly negative impact on the

value loading, which is significant for all portfolios. For households with low leverage, residen-

tial and commercial real estate tilt the risky and stock portfolios toward value stocks, whereas for

households with high leverage, both forms of real estate tilt the financial portfolio toward growth

stocks.

Like leverage, family size plays an ambiguous role in the baseline regressions of Table III.

On the one hand, households with secure jobs and sound financial prospects are more likely to

decide to have children; thus family size can be viewed as a predictor of sound financial conditions

and 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 use twins to disentangle the two effects. Our identification strategy is that 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 V, Panel B, we

15

accordingly modify the baseline regression by including a dummy variable for having children and

a dummy variable for having twins. While the child variable has a positive coefficient, the twin

variable has a negative impact on the value loading for all three portfolios. Thus, the unexpected

birth of an additional child tilts the portfolio toward growth stocks.

The regressions in Tables III to V provide substantial evidence that the portfolio value loading

co-varies with financial and demographic characteristics. Value investors have high financial and

real estate wealth, low leverage, low income risk, and low human capital; they are also more likely

to be older and female. Conversely, young males with risky income and high human capital are

more likely to go growth. In Section 6 and in the Internet Appendix, we verify that these results

hold in a large set of investor subgroups and sub-portfolios, and are robust to a large number

of alternative hypotheses. In particular, we show that the links between the value loading and

socioeconomic characteristics are unlikely to be due to financial market experience, the latent

heterogeneity of investors, or stock characteristics other than the value loading, such as popularity,

professional proximity, dividend yield, taxes, skewness, firm age, or exposure to the size factor.

4.3 Economic Significance

We now assess the respective contributions of age and financial characteristics to the value ladder.

In Table VI, we consider a 30-year old investor, to which we assign the average wealth-weighted

characteristics of his age group in 2003. We also consider a 50-year-old and a 70-year old with

the average characteristics of their age groups. The estimates in Table III allow us to quantify how

characteristics drive the life-cycle variation in the value loading. Between 30 and 70, the value

loading of the risky portfolio increases by 0.23, out of which 0.11 is due to age. For the stock

portfolio, the value loading increases by 0.58 between 30 and 70, out of which 0.35 is due to age.

For both portfolios, age therefore explains about 50% of the life-cycle variation in the value load-

ing. Financial characteristics also have a substantial impact. The decumulation of human capital

between 30 and 70 accounts for 27% of the life-cycle variation of the risky portfolio loading, while

the accumulation of financial wealth accounts for another 10% of the migration. Other character-

16

istics, such as real estate, have more marginal impacts.16 Overall, age and financial characteristics

explain almost entirely the slope of the value ladder.

The table reveals that life-cycle changes in age and financial characteristics all tend to increase

the value loading. Households migrate to higher loadings as their investment horizons shorten,

their balance sheets improve, and their human capital decumulates. The estimates for the 50-year

old investor confirm that consistent with the value ladder, the predicted migration is approximately

linear, which we attribute to linear changes in average characteristics across age groups.

In the Internet Appendix, we obtain similar results when we consider equal-weighted averages

instead of wealth-weighted averages. We also reestimate the decomposition when the interaction

between real estate and leverage is taken into account, and verify that age continues to explain half

of the life-cycle variation in the value loading. The measured impact of real estate and leverage

is then substantially stronger, which illustrates once again that is important to account for the

interaction between debt and real estate.

4.4 Systematic Labor Income Risk

We have seen that income volatility tends to tilt the financial portfolio toward growth stocks. We

now investigate if the value loading can also be affected by forms of systematic risk to which

households in different industries are heterogeneously exposed.

For every two-digit SIC sector s, we compute per-capita income, Ls,t , in year t using all workers

in the sector, and impute the sector’s per-capita income growth,

ℓs,t = log(Ls,t)− log(Ls,t−1).

16Demographic characteristics other than age, such as immigration status or educational attainment, vary acrosscohorts but are not expected to vary over the life-cycle of a typical household. Moreover, as Table V shows, the impactof family size is not accurately measured by the regression coefficient in Table III. We include all characteristics inTable VI for completeness, but we observe that demographic characteristics other than age only have a marginal impacton the value loading and therefore have no impact the conclusions of this section.

17

We similarly compute the growth rate of per-capita income in the economy:

ℓt = log(Lt)− log(Lt−1),

where Lt is average per-capita income in year t.

Table VII, Panel A, documents that there is considerable comovement in sectoral income

growth.17 We estimate the regression:

ℓs,t = αs +ϕs ℓt + εs,t (6)

for each of the 70 sectors, and report the distribution of the sensitivity ϕs and the coefficient of

determination R2 across regressions. The R2 coefficients are generally high and equal to 0.88

on average. Thus, aggregate income growth, ℓt , is an important factor explaining the panel of

sectoral growth rates. The sensitivity, ϕs, is heterogeneous across sectors, ranging from 0.81 (10th

percentile) to 1.22 (90th percentile).

Table VII, Panel B, shows that households working in sectors with high aggregate income

exposures tend to reduce the value tilts of their risky portfolios. Specifically, we regress a house-

hold portfolio’s value tilt, vh,t , on the household sensitivity to the macro factor, ϕh,t , the conditional

volatility of household income, σh,t , and all the other characteristics in the baseline regression. The

household sensitivity, ϕh,t , is measured by the average income-weighted sensitivity of its members,

as is explained in the Internet Appendix. The value tilt of the financial portfolio is negatively related

to industry sensitivity and income volatility. These results are especially strong for the risky port-

folio, which further confirms that household tilts are not simply the by-product of a preference for

certain types of stocks. Economic significance is substantial. For instance, as Table VII shows, the

income exposures of sectors in the 10th and 90th percentiles differ by about 0.4, which corresponds

to an absolute difference in household portfolio loading of 0.2×0.4 = 0.08. As a comparison, this

estimate slightly exceeds the change in loading induced by the life-cycle decumulation of human

capital.

17We thank the referee for encouraging us to investigate income risk at the sector level.

18

We make several observations about these results. First, we impute household sensitivities

from industry data because household income growth has a large idiosyncratic component and the

direct measurement of household sensitivity entails large estimation error, as is further explained

in the Internet Appendix. Second, our approach is motivated by earlier research showing that

the value factor correlates positively with future economic growth (Liew and Vassalou 2000) and

future labor income in U.S. and international data. In the Internet Appendix, we replicate these

earlier results on Swedish data, even though the available time series are relatively short. We

also consider a direct measure of risk, the sensitivity of labor income to the lagged value factor

itself, and similarly obtain that the portfolio value loading is negatively related to the labor income

sensitivity to HML. Overall, Table VII uncovers a powerful factor structure in industry income

growth and shows that households adjust their portfolio loadings to systematic labor income risk.

4.5 Active Rebalancing and New Entrants

4.5.1 Active Rebalancing at the Yearly Frequency

In order to climb the value ladder over the life-cycle, households presumably need to rebalance

their portfolios at shorter horizons to mitigate the impact of realized returns and revert to their

slow-moving target. For this reason, we now investigate passive and active variation in the value

tilt of household portfolios.18 Consider household h with portfolio weights wh,i,t−1 (i = 1, . . . , I) at

the end of year t −1. If the household did not trade 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)

∑Ij=1 wh, j,t−1 (1+ r j,t)

. (7)

18Calvet, Campbell, and Sodini (2009a) define active and passive changes of the risky share, and show that house-holds actively rebalance the passive variation in the risky share due to realized asset returns. We apply a similarmethodology to the value tilt of the risky, stock, and fund portfolios.

19

By equation (2), the value loading of the passive household at the end of year t would then be:

vPh,t =

I

∑i=1

wPh,i,tvi. (8)

The data set reports the actual loading vh,t . We can therefore decompose the actual change of the

portfolio value loading 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 = vP

h,t − vh,t−1 the passive change.

Table VIII regresses the active change, ah,t , on (i) the passive change, ph,t , (ii) the lagged value

loading, vh,t−1, and (iii) either no characteristics or all other lagged characteristics. The passive

change 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. Thus, households actively rebalance the passive variations generated by realized

returns, which confirms that their portfolios tilts are not purely driven by inertia.

4.5.2 New Entrants

We now verify that the value ladder is not the mechanical consequence of exogenous drifts to which

stockmarket participants are passively exposed. A natural identification strategy is to consider new

participants in the year they enter risky asset markets. In Table IX, we regress the stock portfolio

value loading of new participants on their characteristics. All the results are consistent with the

baseline regression.

In Table X, we verify that the positive age coefficient is not driven by specific age groups.

Specifically, 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 common

to all participants are strictly positive and almost all significant. Moreover, the relationship be-

20

tween a participant’s age and its value loading is approximately linear, consistent with our baseline

specification and the value ladder in earlier sections.

The dummy variable for new entrants aged 30 or more is significantly negative. The other

age dummies of new entrants are insignificant. Since the age dummy coefficients are cumulative,

these results imply that (i) all new entrants have a significant bias toward growth stocks and (ii)

age does not impact the difference between the tilt of preexisting participants and the tilt of new

entrants. Thus, the value ladder of new entrants is located below and is parallel to the value ladder

of preexisting participants.

Overall, this section documents that household portfolios progressively shift from growth to

value over the life cycle. The migration is explained by the complementary impact of age and

socioeconomic characteristics on the portfolio tilt. The value ladder is made possible by active

rebalancing and is also observed in the portfolio of new entrants. In the next section, we discuss

how these results relate to theoretical explanations of the value premium.

5 Interpretation of the Portfolio Evidence

In this Section, we show that our empirical results are consistent with some of the leading expla-

nations of the value premium.

5.1 Intertemporal Hedging

5.1.1 Investment Horizon and Age

Risk-based theories of the value premium emphasize the link between the HML factor and the

dynamics of the investment opportunity set. Empirically, good realizations of the value factor pre-

dict high aggregate returns (Campbell and Vuolteenaho 2004) and high economic growth (Koijen,

Lustig, and Van Nieuwerburgh 2014, Liew and Vassalou 2000) in U.S. and international data.

Thus, the HML factor explains both the cross-section of risk premia and the distribution of future

21

returns, which is consistent with the definition of a factor in the Intertemporal Capital Asset Pric-

ing Model (ICAPM, Merton 1973). Investors can use growth stocks to hedge against low future

aggregate returns.

The existing portfolio-choice literature on the value factor focuses on intertemporal hedging

and its implications for investors with different horizons (Jurek and Viceira 2011, Larsen and

Munk 2012, Lynch 2001). Because the hedging motive is stronger for long-term investors than

for short-term investors, portfolio theory implies that young investors should be more tilted toward

growth stocks than old investors. A closely related mechanism is that value stocks have shorter

durations and less discount-rate risk than growth stocks (Campbell and Vuolteenaho 2004, Cornell

1999, Dechow, Sloan, and Soliman 2004, Lettau and Wachter 2007); one therefore expects young

investors to hold long-duration growth stocks, while old investors should hold short-duration value

stocks.19

The empirical evidence in Section 4 shows that age is positively and significantly related to the

value loading. 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.

5.1.2 Household Tilts in Partial and General Equilibrium

In order to facilitate the theoretical interpretation of the value ladder and the panel regressions in

Section 4, we now consider an ICAPM model similar to Merton (1973) and Breeden (1979). The

economy consists of K state variables, I risky assets, and a set of investors with finite horizons and

heterogeneous lifespans, which we fully specify in the Internet Appendix. The model accommo-

dates a wide range of overlapping generations structures, which is important for the analysis of

the value ladder. When the state variables consist of the market price of risk and aggregate labor

income, the model can relate the HML portfolio to time-varying returns, as in Lynch (2001), and

19Campbell and Viceira (2001) apply a similar logic to bond investments.

22

to labor income risk, as in Jagannathan and Wang (1996).20

The following portfolios play an important role in the analysis. The tangency portfolio τtτtτt

maximizes the Sharpe ratio of a myopic (or short-lived) agent. The kth mimicking portfolio is

the portfolio with the highest absolute correlation with the kth state variable. We denote by fk,tfk,tfk,t

the zero-sum portfolio that is short the tangency portfolio and long the kth mimicking portfolio.

The long-short portfolios fk,tfk,tfk,t can be viewed as “factor portfolios” that have similar definitions and

pricing implications as HML.

The optimal portfolio of an individual investor h is determined by diversification and hedging.

The shares of risky wealth held in each risky asset, ωhtωhtωht ∈ R

I, satisfy

ωhtωhtωht = τtτtτt +

K

∑k=1

ηhk,t

wht

fk,tfk,tfk,t , (9)

where each coefficient ηhk,t quantifies the investor’s sensitivity to state variable k and wh,t denotes

the risky share. The investor’s deviation from the tangency portfolio is substantial if the ratios

ηhk,t/wh

t are large, that is if the hedging demand is strong and represents a substantial fraction of

the risky portfolio.

The portfolio choice literature implies that the following properties hold under a wide range

of state dynamics. Young investors generally have stronger sensitivities ηhk,t than old investors,

which stems from the fact that older investors are close to the terminal date and tend to behave

like myopic investors (Lynch 2001, Wachter 2002). When aggregate income is a state variable,

the sensitivity ηhk,t is strong if the household is exposed to high systematic risk in labor income

or has a large stock of human capital. The risky share wht , which has been widely studied in the

portfolio-choice literature,21 is low if the investor has high risk aversion, holds little liquid wealth,

earns a risky income, and has high debt. In the context of value and growth investing, this partial

equilibrium analysis suggests that young investors with risky incomes and weak balance sheets

should tilt their financial portfolios away from value.

20Breeden (1979) and Cochrane (2007) show that labor income risk can easily be included in the ICAPM framework.21See Campbell and Viceira (2000) and the references therein.

23

In general equilibrium, households hold the market portfolio, mtmtmt , and heterogeneous positions

in the factor portfolios:

ωhtωhtωht =mtmtmt +

K

∑k=1

(

ηhk,t

wht

−ηm

k,t

wmt

)

fk,tfk,tfk,t , (10)

where each coefficient ηmk,t/wm

t denotes the relative sensitivity of the aggregate investor to the

kth factor.22 While the aggregate investor holds the market portfolio, each investor h tilts toward

or away from the kth factor if its relative sensitivity to the state variable, ηhk,t/wh

t , differs from the

average sensitivity ηmk,t/wm

t . We refer the reader to the Internet Appendix for a full discussion of the

model. Equation (10) implies that the value ladder can arise in the equilibrium of the overlapping

generations ICAPM economy, because the young have stronger hedging needs and the old weaker

hedging needs than the average investor.

The value ladder of new entrants reported in Section 4.5 also has a natural interpretation in

a general equilibrium context. In an economy in which participants gradually sell their growth

stocks and migrate toward value stocks, the growth stocks must be absorbed by another group of

investors. The empirical evidence 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 portfolios of the deceased contain value stocks that surviving investors can

purchase. New entrants and inheritances therefore permit the migration from growth stocks to

value stocks over the life-cycle. Our results also suggest that demographic changes can affect the

demand for value and growth stocks, which may have implications for the value premium.

5.2 Risk Aversion, Wealth and Background Risk

Risk-based explanations of the value premium are based on the premise that HML carries sys-

tematic risk that is unspanned by the market index. Value stocks should therefore be picked by

investors who have a strong capacity to bear risk, for instance because they have high liquid finan-

cial wealth, high real estate wealth, and low leverage. These effects are apparent in the ICAPM

model discussed in Section 5.1.

22Our derivation builds on the work of Long (1974), Ingersoll (1987), and Cochrane (2007).

24

Quite remarkably, the empirical impact of financial variables on the portfolio tilt is generally in

accordance with the predictions of risk-based theories. Liquid financial wealth is positively related

to the value loading across participants (Table III) and in all subgroups of investors, including the

wealthy group of stockholders owning 5 stocks or more (Table IV). As in earlier studies, financial

wealth is also associated with high risky shares (Table III). These results suggest that wealthier

households adopt value strategies because they are effectively more risk tolerant and therefore more

prone to bearing the systematic risk (other than market portfolio risk) embedded in value stocks.

The results on real estate wealth and leverage provide further evidence that households with sound

balance sheets tilt their portfolios toward value stocks in order to earn the value premium.

Expected utility theory also implies a link between effective risk tolerance and the level of

background risk (see, e.g., Kihlstrom, Romer and Williams 1981). The baseline results on family

size, income risk, self-employment, and immigration status all give empirical support to this view.

The unexpected birth of a child induces a growth tilt, which is consistent with the lower resources

per-capita and higher idiosyncratic needs that the arrival of a newborn entails. High volatility of

income also creates a growth tilt. Indeed, the volatility of household real disposable income is

substantial in Sweden, with an average of 16% per year (Table I),23 and is primarily idiosyncratic,

as we show in the Internet Appendix.24 Similarly, entrepreneurs and immigrants exhibit a growth

tilt, presumably because of substantial idiosyncratic risk in business assets and income.25

23The high volatility of income in Sweden is also documented in Floden and Lindé (2001) and Betermier, Jansson,Parlour, and Walden (2012). Complementary evidence from Holmlund and Storrie (2002) shows a sharp rise in fixed-term contracts following the recession of the early 1990’s, accounting for up to 70% of new hires in Sweden by thelate 1990’s.

24We verify that consistent with the baseline regression, idiosyncratic income volatility induces both a low riskyshare and growth tilt, just as theory predicts.

25The income volatility of self-employed households is estimated at 29% per year, as compared to 16% for the aver-age household. Private businesses are also characterized by high failure rates and highly risk in capital returns, whichare primarily idiosyncratic (Moskowitz and Vissing-Jørgensen 2002). For immigrants, the rate of unemployment is11% in Sweden in 2003, as compared to 7% for non-immigrants. Lemaître (2007) provides a detailed description ofthe hurdles faced by immigrants in the Swedish labor market.

25

5.3 Income and Human Capital

Risk-based theories consider that the HML factor captures forms of cash flow risk to which value

and growth stocks are heterogeneously exposed.26 While the exact nature of this risk is the subject

of some debate, an extensive literature relates HML to labor income and human capital. For

instance, Jagannathan and Wang (1996), Lettau and Ludvigson (2001), Palacios-Huerta (2003),

Petkova and Zhang (2005) and Santos and Veronesi (2006) develop conditional versions of the

CAPM and C-CAPM that incorporate aggregate income growth and can price the Fama and French

portfolios.27 Garleanu, Kogan, and Panageas (2012) consider that human capital is sensitive to

innovation shocks, and derive the implications of the resulting “displacement” risk for the cross-

section of stock returns and household portfolio tilts.

The present paper uncovers several empirical facts in support of labor-based theories of the

value premium. First, we document a strong factor structure in the industry distribution of income

growth. As Section 4.4 shows, aggregate income growth explains on average 88% of the time

series variation in sectoral income growth, and the sensitivity to the factor is heterogeneous across

sectors. Industry data therefore confirm that aggregate income growth is an appealing macroeco-

nomic factor for asset pricing.

Second, we find that households working in sectors with high exposures to the macro factor

tend to select financial portfolios with low exposures to HML, as the hedging motive implies. The

household data thus reveal a close link between aggregate income risk and the portfolio choices

of retail investors, which confirms that aggregate income growth is a good candidate for asset

pricing applications. In his Presidential Address to the American Finance Association, Cochrane

(2011) develops the following implications of the value factor: “If a mass of investors has jobs

or businesses that will be hurt especially hard by a recession, they avoid stocks that fall more

26Statistical decompositions show that value stocks have higher exposures to the market’s cash-flow risk than growthstocks (Campbell and Vuolteenaho 2004), and that the value loadings of individual stocks are primarily driven by theirown cash flows (Campbell, Polk, and Vuolteenaho 2010). Furthermore, value stocks are strongly exposed to deeprecessions and the persistent reductions in aggregate cash flows that they entail (Campbell, Giglio, and Polk 2013).

27Complementing these empirical studies, Parlour and Walden (2011) and Sylvain (2013) derive general equilibriummodels in which risky labor income drives the cross-section of book-to-market ratios and risk premia. See alsoLewellen and Nagel (2006), Nagel and Singleton (2011), Ang and Kristensen (2012) and Roussanov (2013) for recentcritiques of the CAPM and C-CAPM.

26

than average in a recession.” The present paper confirms Cochrane’s prediction by showing that

workers in exposed industries select portfolios with low HML loadings. Furthermore we document

that households with self-employed heads exhibit additional tilts toward growth stocks, presumably

because small businesses are especially sensitive to recession risk.

In addition to these results, we uncover that high expected human capital is associated with a

growth tilt in the financial portfolio. This relationship is strong in all the specifications considered

in this paper and the Internet Appendix. Intuition suggests that human capital is both of form of

wealth, which in principle might induce a value tilt, and a form of risk, which in the data induces a

growth tilt. We can offer several possible explanations for the dominance of the risk channel, which

build on the extensive literature relating the value premium to the production process.28 Since

human capital is a key complement of physical capital in production, households with high levels

of human capital may tilt away from the physical capital in value firms and invest instead in growth

firms. Sylvain (2013) accordingly develops a general equilibrium model with both human and

physical capital investment, and shows that value stocks endogenously exhibit a high sensitivity to

human capital risk.29 A complementary explanation is that human capital is highly risky because

it is exposed to tail risks and innovation shocks that are difficult to anticipate and measure ex ante,

as in the theoretical models Garleanu, Kogan, and Panageas (2012) and Kogan, Papanikolaou, and

Stoffman (2013). The strong empirical link between human capital and growth investing is a novel

empirical fact that deserves to be explored in future research.

28Production-based asset pricing models, which have had success in relating the sensitivity of a firm’s traded equityto 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. Asa result, value stocks are more sensitive than growth stocks to the business cycle (Zhang 2005). Related channelsinclude operational leverage (Carlson, Fisher, and Giammarino 2004), investment-specific technology (Kogan andPapanikolaou 2014), and the cyclicality of the demand for durable goods (Gomes, Kogan, and Yogo 2009).

29Baxter and Jermann (1997) show that human capital is positively correlated with aggregate physical capital at themacro level.

27

5.4 Overconfidence

Sentiment-based explanations of the value premium consider that investors exuberantly overprice

growth (“glamour”) stocks and underprice value stocks (“fallen angels”), which explains the long-

run success of value investing. Several psychological biases may account for such mispricing. In-

vestors may be overconfident and thereby overestimate the accuracy of available information. They

may also pay more attention to recent events than Bayesian updating would imply (Kahneman and

Tversky 1973). Investor with such biases tend to overprice stocks following positive news and un-

derprice stocks following negative news, so that valuation ratios can predict future returns (Daniel,

Hirshleifer, and Subrahmanyam 2001). These mechanisms are consistent with the biases in stock

analyst expectations (La Porta 1996, La Porta, Lakonishok, Shleifer, and Vishny 1997, Greenwood

and Sheifer 2013, Skinner and Sloan 2002) and the pricing impact of sentiment measures (Baker

and Wurgler 2006).

Cognitive biases have a number of potential implications for portfolio choice. Men and en-

trepreneurs are known to be especially prone to overconfidence (Barber and Odean 2001, Busenitz

and Barney 1997, Cooper, Woo, and Dunkelberg 1988) and should therefore favor growth stocks.

The evidence in Section 4.2 confirms these predictions. Women tend to select low risky shares and

invest in value stocks, while men tend to select aggressive risky shares and go growth. These pat-

terns cannot easily be explained by differences in risk aversion alone, since a risk-tolerant investor

should choose both a high risky share and a growth tilt. The positive link between entrepreneurship

and growth investing might also be explained by overconfidence.

In the Internet Appendix, we reestimate the baseline regression on the subsample of households

with a male head and on the subsample of households with a self-employed head. The baseline

results of the paper hold in both subsamples and are therefore unlikely to be driven by cross-

sectional differences in overconfidence alone.

28

6 Identification and Robustness Checks

6.1 Popular and Professionally Close Stocks

A potential concern with Swedish data is that a handful of firms dominate the domestic stock mar-

ket and therefore the stock portfolios of a majority of households (Table I). In Table XI, we consider

the 10 stocks that are most widely held by households at the end of 2003, and report for each firm

the percentage of direct stockholders owning it, the stock’s percentage of aggregate household fi-

nancial 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 their value loadings or book-to-market ratios.

In the first two sets of columns of Table XII, we reestimate the baseline regression on (1)

household portfolios of popular stocks and (2) household portfolios of non-popular stocks. For

both portfolios, characteristics have the same impact as in the baseline regression.30 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 next ask if professionally close stocks, which represent 16% of household stock portfolios,

can account for the relationships between the value loading and characteristics. In columns (3)

and (4) of Table XII, we show that the baseline results are also valid in household portfolios of

professionally close stocks and in household portfolios of other stocks. In the Internet Appendix,

we reach a similar conclusion for households with extreme shares of professionally close stocks or

working in specific sectors. Thus, the relationships between the value loading and characteristics

are not driven by popular and professionally close stocks.

30One complementary result is that the baseline results holds among the wealthy subgroup of investors holding atleast 5 firms, who do not have a bias for popular stocks.

29

6.2 Other Stock Characteristics

We now verify that the baseline results are not driven by stock characteristics other than the value

loading.

Dividends and Taxes. One may ask if the value tilt picks up a retail demand for dividend-paying

or tax-advantaged stocks, which is unrelated to HML. For example, Graham and Kumar (2006) use

U.S. brokerage data to show that the demand for dividend yield increases with age and decreases

with income, which they interpret as evidence of age and tax clienteles. We note that in Sweden,

capital losses are deductible and the tax rate is 30% on both capital gains and dividends, so the tax

clientele story is not as clear in Sweden as it is in the United States. Furthermore, in Table XIII, we

reestimate the baseline regression on sub-portfolios of stocks sorted by dividend yields, and verify

that the baseline results hold in all portfolios (including the 40% of stocks that pay no dividends).

In Table XIII, we investigate the potential impact of taxes by considering two identification

methods. First, the wealth tax, which was levied on Swedish households until 2007, applied to

stocks in the A list of the Stockholm Stock Exchange but not to the smaller stocks in the O list.

The O list includes approximately 230 stocks and is updated each year. The baseline results are

observed both in the portfolio of A-listed stocks and in the portfolio of O-listed stocks. Second,

until 2004, Swedish households were levied inheritance and gift taxes at death, but these taxes

did not apply to O-listed stocks (Du Rietz, Henrekson, and Waldenström 2012). In the Internet

Appendix, we verify that the baseline results hold in the subperiod that follows the repeal of the

inheritance tax (2005-2007).

Firm Age A possible story is that young households invest in young firms and old households

invest in old firms, without consideration of HML loadings. This mechanism is unlikely to explain

our baseline results for two main reasons. First, since we use unconditional estimates of firm

loadings, our results cannot be contaminated by exogenous changes in firm value tilts between

1999 and 2007. Consequently, the value ladder in Figure 2 shows that the shares of value stocks

increase over time in household portfolios. Second, we consider all the firms traded on Nordic

exchanges in 2007. We construct the “young” portfolio as the set of stocks that have been listed

30

for less than 10 years, and the “old” portfolio as the set of firms that have listed for at least 20

years. The young and old portfolios respectively contain 50% and 30% of all stocks. The baseline

results hold in both portfolios.

Skewness. A recent literature suggests that the demand for positively-skewed “lottery” stocks

could explain the under-diversification of household portfolios (Goetzmann and Kumar 2008,

Kumar 2009b, Mitton and Vorkink 2007, Polkovnichenko 2005). While lottery stocks tend to

be small and young growth stocks, it is unlikely that the value tilt is explained by preference for

skewness. First, the demand for lottery-stocks is relatively small. Kumar (2009b) estimates that the

average share invested in lottery-stocks is less than 4% of household risky portfolios. We observe

a similar pattern in Table I. Among households that own 1 or 2 stocks directly, the amount invested

in the smaller non-popular stocks only represents $1,000 out of a financial wealth of $37,000. Sec-

ond, households choose similar value tilts in their stock and fund portfolios (Table III), which is

inconsistent with the implications of portfolio theory when investors have a preference for skew-

ness (Langlois 2013, Mitton and Vorkink 2007). Third, it is clear from Table IV that our results

are the strongest among households with more diversified portfolios. In Table XII and XIII, our re-

sults remain strong in the portfolios of popular and old stocks, which do not include typical lottery

stocks. Thus, preference for skewness alone cannot explain our main empirical results.

6.3 Financial Market Experience

A possible interpretation of the value ladder is that age proxies for financial 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 goes by.31 In Table XIV, we control

for learning effects by considering a measure of experience, the number of years since entry. We

focus on 2007 risky asset market participants and regress the 2007 value loading on the experience

measure, age, 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, which is

31The psychology literature documents that cognitive biases attenuate with experience in sufficiently regular envi-ronments (Hogarth 1987, Kahneman 2011, Oskamp 1965). Malmendier and Nagel (2011) provide some evidence thatyounger or less experienced investors are especially likely to extrapolate from recent financial data.

31

inconsistent with the simple learning story.32 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 by

a 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 value investing.

Table XIV also sheds light on the dynamics of the portfolio tilt during the participation period.

The value loading in the entry year has a positive and strongly significant impact on the value

loading 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. This

suggests that the value loading is not simply driven by the initial portfolio in the year of entry, but

also depends on financial and demographic characteristics in the subsequent participation period.

6.4 Latent Heterogeneity

The baseline regression includes household characteristics and yearly, industry, and county fixed

effects. We now use the twin panel to verify that the characteristics do not merely proxy for latent

traits or cohort effects. In Table XV, we estimate the specification:

vk,1,t = αk,t +b′xk,1,t + ek,1,t , (11)

vk,2,t = αk,t +b′xk,2,t + ek,2,t , (12)

where vk, j,t denote the value loading of sibling j ∈ {1,2} in pair k at date t, αk,t is a yearly pair

fixed effect, xk, j,t denotes the vector of yearly characteristics of sibling j, and ek, j,t is an orthogonal

error. The yearly twin pair fixed effect captures the impact of time, such as age or stock market

performance, as well as similarities between the twins, such as common genetic makeup, family

32In the Internet Appendix, we verify that the results of Table XIV are not driven by multicollinearity between ageand the experience variable, nor by limitations in the experience variable due to the length of the panel.

32

background, upbringing, and expected inheritance. Since twin siblings have the same age, the

twin regression naturally controls for cohort effects.33 The twin regressions are consistent with the

baseline results. The Internet Appendix reports similar results for the subsample of identical twins.

The twin regression has a substantially higher adjusted R2 coefficient than the baseline regres-

sion. For the stock portfolio, characteristics and yearly twin pair fixed effects account for 23% of

the cross-sectional variation of the stock portfolio value tilt. By contrast, in the baseline regression

(Table III), socioeconomic characteristics and year, industry, and county fixed effects explain 4%

of the cross-sectional variation in the value loading. Large increases in adjusted R2 are also ob-

tained for the risky and fund portfolios. In the next section, we discuss the possible origins of the

high explanatory power of the yearly twin pair fixed effects.

6.5 Communication and Genes

We now assess the impact of communication between twins on their portfolio tilts. The twin panel

contains detailed information on the frequency of communication between twins. In Table XVI, we

classify a twin pair as “high communication” if the frequency of mediated communication and the

frequency of unmediated communication are both above the median, and as “low communication”

otherwise. We sort twin pairs into communication bins, and reestimate in each bin the baseline

regression with year, industry and county fixed effects. The reported regressions are generally

consistent with the baseline results. In the Internet Appendix, we obtain similar coefficients when

we include yearly twin pair fixed effects. Thus, communication does not drive the relationship

between the value tilt and socioeconomic variables.

The large R2 of the twin regressions in Table XV might suggest that value investing has genetic

origins. Cronqvist, Siegel, and Yu (2015) consider a genetic model of the value loading, in which

the value loading vk,s of sibling s in pair k is assumed to be the sum of a genetic component, ak,s,

33Calvet and Sodini (2014) apply this methodology to the determinants of the risky share. Cesarini, Dawes, Johan-nesson, 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.

33

a common component, ck, and an idiosyncratic component, εk,s :

vk,s = ak,s + ck + εk,s.

The main identification condition is that the twin correlation of the genetic component, Corr(ak,1;

ak,2), is 1 for identical twins and 1/2 for fraternal twins. Under this model, the contribution of the

genetic component to the cross-sectional variance of the value loading satisfies

Var(ak,s)

Var(vk,s)= 2(ρI −ρF), (13)

where ρI denotes the twin correlation of the value loading among identical siblings, and ρF denotes

the twin correlation among fraternal siblings. This equation serves as the basis for estimation.

Cronqvist, Siegel, and Yu (2015) attribute 30% of the value loading to the genetic component.

Table XVII confirms these earlier results, but shows that they are highly sensitive to communi-

cation. The genetic share reaches 35% for frequent communicators but disappears almost entirely

among infrequent communicators, with estimates that do not exceed 1% across specifications.34

These low estimates are especially surprising if the genetic is correctly specified, because purely

genetic effects should not depend on communication. One could argue that the communication

frequency itself has genetic origins, so that the results of Table XVII could be construed as ev-

idence that value investing is driven by genes. However, by equation (13), the genetic share is

zero if and only if the twin correlation of the value loading is the same for identical and fraternal

pairs: ρI = ρF . Thus, a genetic theory of value investing would need to explain why infrequently

communicating twins have the same loading correlations regardless of genetic makeup.

The sensitivity of the genetic decomposition is related to one of its well-known shortcomings,

namely that it neglects interactions between genetic and environmental variables. Interactions

34The estimator of the genetic share (13) is the rescaled difference between two sample correlations. It can thereforetake negative values if the estimate of ρI is lower than the estimate of ρF in a particular sample. In fact, under the nullhypothesis

H0 : ρI = ρF ,

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.

34

between nature and nurture are known to be empirically important in medicine and experimental

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 XVII shows

that the clean dichotomy between nature and nurture is equally elusive in the context of value

investing.

7 Conclusion

An extensive asset-pricing literature relates the value premium to a wide range of macroeconomic

variables. The present paper uncovers that strong patterns exist at the microeconomic level in a

large panel of household portfolios. We document substantial heterogeneity in the value tilt across

households, which is of the same size as the value premium. Households migrate from growth

stocks to value stocks as they become older and their balance sheets improve. Households achieve

their migration up the value ladder by actively managing their stock and fund holdings, for instance

by rebalancing the passive variation in portfolio tilts induced by market returns.

The paper provides empirical support for several key explanations of the value premium. In

striking accordance with risk-based theories, value stocks are held by households that are in the

best position to take systematic financial risk, for instance because they have sound balance sheets

or low background risk. Pure horizon effects account for at least 50% of the life-cycle variation

of the value tilt, which provides strong empirical support for intertemporal hedging (Jurek and

Viceira 2011, Larsen and Munk 2012, Lynch 2001). We also find that investors with high human

capital and high exposure to aggregate labor income shocks tilt away from value, which is in

line with labor-based theories of the value premium. These empirical regularities are stronger

among households that own diversified stock portfolios, have high risky shares and own the bulk

of aggregate equity.

The evidence suggests that behavioral biases matter as well, especially among the large group

of small investors who have low risky shares and hold a small fraction of aggregate equity. A large

number of investors hold one to four stocks, which concentrate in a handful of popular companies.

35

The relationships between growth investing and variables such as gender seem consistent with

the overconfidence bias documented in the psychology literature. Thus the panel of household

portfolio tilts is explained by a mix of risk-based and behavioral explanations.

Our results provide new directions for portfolio-choice and asset-pricing theories of the value

factor. The household data reveal that growth investing is tightly linked to aggregate income risk

and human capital, which one might seek to match in a calibrated life-cycle model. Our empirical

findings also suggest that powerful general equilibrium effects are at play in the cross-sectional dis-

tribution and the dynamics of portfolio tilts. The development of overlapping generations models

matching these features are natural extensions of our work. Last but not least, the empirical pat-

terns in the demand for value and growth stocks may have major implications for equity valuation,

which will be investigated in further research.

36

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47

Tabl

e I

Sum

mar

y St

atis

tics

The

tabl

ere

ports

sum

mar

yst

atis

tics

onth

efin

anci

alan

dde

mog

raph

icch

arac

teris

tics

(Pan

elA)

and

portf

olio

char

acte

ristic

s(P

anel

B)of

parti

cipa

ting

Swed

ish

hous

ehol

dsat

the

end

of20

03.W

eco

nsid

erris

kyas

setm

arke

tpar

ticip

ants

(firs

tset

ofco

lum

ns),

mut

ualf

und

hold

ers

(sec

ond

seto

fcol

umns

),di

rect

stoc

khol

ders

(third

seto

fcol

umns

)an

ddi

rect

stoc

khol

ders

sorte

dby

the

num

ber

ofst

ocks

that

they

own

(last

seto

fthr

eeco

lum

ns)

For

each

dire

ctst

ockh

olde

rs(th

irdse

tofc

olum

ns),

and

dire

ctst

ockh

olde

rsso

rted

byth

enu

mbe

rof

stoc

ksth

atth

eyow

n(la

stse

toft

hree

colu

mns

).Fo

rea

chch

arac

teris

tic,

we

repo

rtth

ecr

oss-

sect

iona

lmea

nan

dst

anda

rdde

viat

ion

inea

chsa

mpl

e.Th

ebo

ttom

row

sof

Pane

lBta

bula

teth

efra

ctio

nof

the

aggr

egat

ew

ealth

ofris

kyas

set

mar

ket

parti

cipa

nts

held

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ecifi

cgr

oups

ofin

vest

ors.

The

calc

ulat

ions

are

base

don

the

repr

esen

tativ

epa

nelo

fho

useh

olds

over

the

1999

to20

07pe

riod

defin

edin

Sec

tion

2.2.

Allv

aria

bles

are

desc

ribed

inTa

ble

A.

Pane

l A: F

inan

cial

and

Dem

ogra

phic

Cha

ract

eris

tics

AllP

artic

ipan

tsFu

ndho

lder

sSt

ockh

olde

rsSt

ockh

olde

rsSo

rted

Mea

nSt

anda

rdM

ean

Stan

dard

Mea

nSt

anda

rdde

viatio

nde

viatio

nde

viatio

nM

ean

Mea

nM

ean

Fina

ncia

l Cha

ract

eris

tics

Fina

ncia

l wea

lth ($

)48

,849

121,

578

50,6

1412

1,09

966

,478

152,

690

37,1

2360

,091

126,

493

Rid

til

lt

tlth

($)

137

108

184

525

138

327

179

024

165

020

215

680

129

854

169

241

229

107

1-2

3-4

5+

All P

artic

ipan

tsFu

ndho

lder

sSt

ockh

olde

rsBy

Num

ber o

f Sto

cks

Own

edSt

ockh

olde

rs S

orte

d

Resi

dent

ial r

eal e

stat

e we

alth

($)

137,

108

184,

525

138,

327

179,

024

165,

020

215,

680

129,

854

169,

241

229,

107

Com

mer

cial

real

est

ate

weal

th ($

)19

,581

112,

626

19,5

2011

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027

,255

135,

585

21,5

9830

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36,1

31Le

vera

ge ra

tio0.

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130.

651.

090.

530.

910.

650.

460.

34H

uman

Cap

ital a

nd In

com

e R

isk

Hum

an c

apita

l ($)

955,

680

515,

879

972,

402

513,

389

993,

114

545,

932

929,

517

1,03

0,77

01,

089,

285

Inco

me

($)

46,1

8431

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46,7

8530

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50,0

6637

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44,9

0251

,133

59,1

83Se

lf-em

ploy

men

t dum

my

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0.20

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ploy

men

t dum

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Con

ditio

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e vo

latili

ty0.

160.

120.

160.

110.

170.

120.

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170.

18D

emog

raph

ic C

hara

cter

istic

sAg

e46

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10.7

346

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10.6

947

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10.5

846

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47.5

549

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Mal

e ho

useh

old

head

dum

my

0.64

0.48

0.63

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0.69

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0.70

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High

sch

ool d

umm

y0.

850.

360.

850.

350.

860.

350.

840.

860.

90Po

st-h

igh

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ol d

umm

y0.

370.

480.

370.

480.

420.

490.

350.

420.

53Ec

onom

ics

educ

atio

n du

mm

y0.

120.

320.

120.

320.

130.

340.

120.

140.

16Im

mig

ratio

n du

mm

y0.

080.

270.

080.

260.

080.

270.

080.

090.

07Fa

mily

siz

e2.

531.

402.

611.

402.

521.

372.

422.

562.

69Nu

mbe

r of o

bser

vatio

ns71

,639

71,6

3962

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62,9

7242

,153

42,1

5322

,522

7,78

611

,845

,,

,,

,,

,,

,

Tabl

e I

Sum

mar

y St

atis

tics

-Con

tinue

d

Pane

l B: P

ortfo

lio C

hara

cter

istic

sAl

lPti

it

Fdh

ldSt

khld

Stkh

ldS

td

Mea

nSt

anda

rdM

ean

Stan

dard

Mea

nSt

anda

rdde

viatio

nde

viatio

nde

viatio

nM

ean

Mea

nM

ean

Port

folio

Cha

ract

eris

tics

Risk

y sh

are

0.40

0.27

0.42

0.26

0.46

0.27

0.37

0.49

0.61

By N

umbe

r of S

tock

s O

wned

1-2

3-4

5+

All P

artic

ipan

tsFu

ndho

lder

sSt

ockh

olde

rsSt

ockh

olde

rs S

orte

d

Shar

e of

dire

ct s

tock

hold

ings

in ri

sky

portf

olio

0.29

0.37

0.19

0.28

0.49

0.37

0.44

0.48

0.58

Shar

e of

pop

ular

sto

cks

0.71

0.37

0.71

0.36

0.71

0.37

0.79

0.71

0.57

Shar

e of

pro

fess

iona

lly c

lose

sto

cks

0.16

0.32

0.16

0.31

0.16

0.32

0.15

0.17

0.18

Num

ber o

f sto

cks

2.59

5.15

2.53

5.30

4.40

6.10

1.35

3.42

10.8

5Nu

mbe

r of f

unds

4.11

4.51

4.68

4.53

4.55

5.19

3.49

4.90

6.34

Shar

e of

Agg

rega

te W

ealth

ggg

Risk

y po

rtfol

io1.

000.

940.

860.

180.

130.

54St

ock

portf

olio

1.00

0.85

1.00

0.09

0.11

0.80

Fund

por

tfolio

1.00

1.00

0.75

0.25

0.14

0.36

Num

ber o

f obs

erva

tions

71,6

3971

,639

62,9

7262

,972

42,1

5342

,153

22,5

227,

786

11,8

45

Tabl

e II

Cro

ss-S

ectio

nal D

istr

ibut

ion

of th

e Va

lue

Load

ing

The

tabl

ere

ports

sum

mar

yst

atis

tics

onth

ecr

oss-

sect

iona

ldis

tribu

tion

ofth

eva

lue

load

ing

atth

een

dof

2003

fors

ome

ofth

em

ain

fam

ilies

ofas

sets

and

hous

ehol

dpo

rtfol

ios

used

inth

epa

per.

The

colu

mns

repo

rt(i)

the

valu

elo

adin

gof

the

aggr

egat

epo

rtfol

io(ii

)th

ecr

oss

sect

iona

ldis

tribu

tion

ofth

eva

lue

load

ing

and

(iii)

the

valu

elo

adin

gsp

read

betw

een

the

aggr

egat

epo

rtfol

io,(

ii)th

ecr

oss-

sect

iona

ldis

tribu

tion

ofth

eva

lue

load

ing,

and

(iii)

the

valu

elo

adin

gsp

read

betw

een

the

top

and

botto

mde

cile

s.Th

efir

stro

wco

nsid

ers

stoc

kslis

ted

onth

eSt

ockh

olm

Stoc

kEx

chan

gean

dth

ese

cond

row

cons

ider

sal

lSw

edis

hris

kym

utua

lfun

ds.H

ouse

hold

aggr

egat

epo

rtfol

ios

are

cons

truct

edby

addi

ngup

the

mar

ketv

alue

sof

indi

vidu

alho

useh

old

hold

ings

.

Valu

e Lo

adin

g

Aggr

egat

eSp

read

Portf

olio

Mea

n10

th25

th50

th75

th90

th(9

0th

- 10t

h)A

sset

sSt

ocks

list

ed o

n St

ockh

olm

Sto

ck E

xcha

nge

-0.1

5-0

.87

-3.2

2-1

.57

-0.3

70.

090.

944.

16Fu

nds

-0.1

0-0

.15

-0.4

1-0

.26

-0.1

00.

010.

200.

61H

ouse

hold

s

Cro

ss-S

ectio

nal D

istri

butio

n

All p

artic

ipan

ts -

Risk

y po

rtfol

io-0

.26

-0.3

0-0

.94

-0.4

6-0

.18

0.00

0.10

1.04

- St

ock

portf

olio

-0.3

6-0

.58

-1.2

0-1

.09

-0.5

30.

110.

391.

58 -

Fund

por

tfolio

-0.1

8-0

.20

-0.5

7-0

.30

-0.1

40.

000.

080.

65Fu

ndho

lder

s- R

isky

portf

olio

-0.2

5-0

.25

-0.7

1-0

.40

-0.1

7-0

.01

0.09

0.80

Ri

sky

portf

olio

0.25

0.25

0.71

0.40

0.17

0.01

0.09

0.80

- St

ock

portf

olio

-0.3

5-0

.57

-1.1

7-1

.06

-0.5

20.

100.

381.

55 -

Fund

por

tfolio

-0.1

8-0

.20

-0.5

7-0

.30

-0.1

40.

000.

080.

65D

irect

sto

ckho

lder

s -

Risk

y po

rtfol

io-0

.28

-0.3

8-1

.07

-0.6

1-0

.24

-0.0

20.

111.

18 -

Stoc

k po

rtfol

io-0

.36

-0.5

8-1

.20

-1.0

9-0

.53

0.11

0.39

1.58

-Fun

dpo

rtfol

io-0

19-0

22-0

58-0

33-0

16-0

030

070

65 -

Fund

por

tfolio

-0.1

9-0

.22

-0.5

8-0

.33

-0.1

6-0

.03

0.07

0.65

Tabl

e III

Pane

l Reg

ress

ion

of th

e Va

lue

Load

ing

on C

hara

cter

istic

s

This

tabl

ere

ports

pool

edre

gres

sion

sof

the

valu

elo

adin

gon

hous

ehol

dch

arac

teris

tics

and

year

,in

dust

ry,

and

coun

tyfix

edef

fect

s.Th

eva

lue

load

ing

isco

mpu

ted

atth

ele

velo

fthe

risky

portf

olio

inco

lum

n(1

),th

est

ock

portf

olio

inco

lum

n(2

),an

dth

efu

ndpo

rtfol

ioin

colu

mn

(3)

We

regr

ess

the

risky

shar

eon

the

sam

ech

arac

teris

tics

and

fixed

effe

cts

inco

lum

n(4

)Th

eco

mpu

tatio

nsar

eba

sed

onth

e(3

).W

ere

gres

sth

eris

kysh

are

onth

esa

me

char

acte

ristic

san

dfix

edef

fect

sin

colu

mn

(4).

The

com

puta

tions

are

base

don

the

repr

esen

tativ

epa

nelo

fhou

seho

lds

over

the

1999

to20

07pe

riod

defin

edin

Sect

ion

2.2.

Allv

aria

bles

are

desc

ribed

inTa

ble

A.St

anda

rder

rors

are

clus

tere

dat

the

hous

ehol

dle

vel.

(1)

(2)

(3)

(4)

Risk

y Sh

are

Dep

ende

nt V

aria

ble:

Val

ue L

oadi

ngRi

sky

Portf

olio

Stoc

k Po

rtfol

ioFu

nd P

ortfo

lioD

epen

dent

Var

iabl

e:

Estim

ate

t-sta

tEs

timat

et-s

tat

Estim

ate

t-sta

tEs

timat

et-s

tat

Fina

ncia

l Cha

ract

eris

tics

Log

finan

cial

wea

lth

0.01

712

.44

0.05

016

.15

0.01

214

.57

0.09

513

5.95

Log

resi

dent

ial r

eal e

stat

e0.

001

1.75

0.00

34.

550.

000

-0.2

70.

000

3.32

Log

com

mer

cial

real

est

ate

0.00

13.

970.

007

12.3

60.

000

0.43

-0.0

02-1

1.89

(1)

(2)

(3)

(4)

Leve

rage

ratio

0.00

00.

30-0

.008

-1.7

3-0

.001

-0.9

8-0

.008

-14.

46H

uman

Cap

ital a

nd In

com

e R

isk

Log

hum

an c

apita

l -0

.052

-9.5

0-0

.103

-9.5

0-0

.021

-6.6

30.

016

5.92

Log

inco

me

-0.0

46-1

1.35

-0.0

44-5

.75

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62-2

9.50

Self-

empl

oym

ent d

umm

y-0

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49Un

empl

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ent d

umm

y-0

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tiona

l inc

ome

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tility

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0.98

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3.28

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62-9

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Dem

ogra

phic

Cha

ract

eris

tics

Age

0.00

316

.02

0.00

923

.50

0.00

15.

53-0

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14M

ale

hous

ehol

d he

ad d

umm

y-0

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48-0

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-13.

57-0

.013

-5.8

50.

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High

sch

ool d

umm

y-0

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8-0

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023

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0Po

st-h

igh

scho

ol d

umm

y-0

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40.

016

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-0.0

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419

.95

gy

Econ

omic

s ed

ucat

ion

dum

my

-0.0

27-5

.94

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.09

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0.01

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69Im

mig

ratio

n du

mm

y-0

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13-0

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mily

siz

e0.

036

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00.

024

7.42

0.01

719

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0.44

Adju

sted

R2

2.37

%3.

95%

0.94

%16

.57%

Num

ber o

f obs

erva

tions

589,

561

331,

693

523,

798

589,

561

Tabl

e IV

Valu

e Lo

adin

gs o

f Inv

esto

r Sub

grou

ps

This

tabl

ere

ports

pool

edre

gres

sion

sof

the

valu

elo

adin

gof

the

risky

portf

olio

onho

useh

old

char

acte

ristic

san

dye

ar,

indu

stry

,an

dco

unty

fixed

effe

cts

estim

ated

over

diffe

rent

subs

ets

ofin

vest

ors.

The

regr

essi

ons

are

sim

ilart

oth

eba

selin

ere

gres

sion

sin

Tabl

eIII

,but

we

cons

ider

subg

roup

sof

risky

asse

tmar

ketp

artic

ipan

ts:f

und

hold

ers

inco

lum

n(1

)di

rect

stoc

khol

ders

inco

lum

n(2

)an

ddi

rect

stoc

khol

ders

sorte

dby

the

num

bero

fow

ned

risky

asse

tmar

ketp

artic

ipan

ts:f

und

hold

ers

inco

lum

n(1

),di

rect

stoc

khol

ders

inco

lum

n(2

),an

ddi

rect

stoc

khol

ders

sorte

dby

the

num

bero

fow

ned

stoc

ksin

colu

mns

(3)t

o(5

).Th

esu

bgro

ups

are

obta

ined

from

the

repr

esen

tativ

epa

nelo

fhou

seho

lds

over

the

1999

to20

07pe

riod

defin

edin

Sect

ion

2.2.

All

varia

bles

are

desc

ribed

inTa

ble

A.St

anda

rder

rors

are

clus

tere

dat

the

hous

ehol

dle

vel.

Five

orM

ore

Stoc

ksSt

ockh

olde

rs S

orte

d by

Num

ber o

f Sto

cks

Own

edO

neor

Two

Stoc

ksTh

ree

orFo

urSt

ocks

Dep

ende

nt V

aria

ble:

Val

ue L

oadi

ng o

f Ris

ky P

ortfo

lio

Fund

hold

ers

Stoc

khol

ders

Estim

ate

t-sta

tEs

timat

et-s

tat

Estim

ate

t-sta

tEs

timat

et-s

tat

Estim

ate

t-sta

tFi

nanc

ial C

hara

cter

istic

sLo

g fin

anci

al w

ealth

0.

010

9.27

0.04

719

.97

0.04

011

.82

0.09

116

.70

0.06

718

.18

Log

resi

dent

ial r

eal e

stat

e0.

000

-0.4

50.

002

4.48

0.00

22.

650.

002

2.01

0.00

44.

92

(1)

(2)

(3)

(4)

(5)

Five

or M

ore

Stoc

ksO

ne o

r Two

Sto

cks

Thre

e or

Fou

r Sto

cks

Fund

hold

ers

Stoc

khol

ders

Log

com

mer

cial

real

est

ate

0.00

12.

340.

003

7.50

0.00

48.

400.

002

3.38

0.00

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99Le

vera

ge ra

tio-0

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uman

Cap

ital a

nd In

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e R

isk

Log

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l -0

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g in

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t dum

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ent d

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tiona

l inc

ome

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tility

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0.78

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44-9

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Dem

ogra

phic

Cha

ract

eris

tics

Age

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216

.78

0.00

517

.40

0.00

511

.92

0.00

58.

880.

005

11.6

5M

ale

hous

ehol

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ad d

umm

y-0

.037

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08-0

.085

-16.

28-0

.077

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47-0

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6Hi

ghsc

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dum

my

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76-0

024

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gh s

choo

l dum

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024

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008

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st-h

igh

scho

ol d

umm

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620.

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2.71

Econ

omic

s ed

ucat

ion

dum

my

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20-5

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-0.0

35-3

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14-1

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0.01

11.

19Im

mig

ratio

n du

mm

y-0

.031

-6.9

3-0

.120

-12.

39-0

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-8.6

5-0

.108

-5.7

6-0

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-9.4

6Fa

mily

siz

e0.

025

22.2

40.

040

17.7

40.

046

14.5

40.

038

8.36

0.03

09.

00Ad

just

ed R

22.

02%

4.45

%3.

50%

7.54

%7.

22%

Nb

fb

ti52

379

833

169

317

570

759

697

9628

9Nu

mbe

r of o

bser

vatio

ns52

3,79

833

1,69

317

5,70

759

,697

96,2

89

Tabl

e V

Alte

rnat

ive

Ris

k M

easu

res

This

tabl

ere

ports

the

effe

cts

ofad

ditio

nalr

eale

stat

e,le

vera

ge,a

ndfa

mily

size

varia

bles

onth

eva

lue

load

ing

inth

epr

esen

ceof

year

,ind

ustry

,and

coun

tyfix

edef

fect

s.Pa

nelA

incl

udes

mea

sure

sof

dem

eane

dre

ales

tate

wea

lthin

tera

cted

with

dem

eane

dle

vera

geW

eco

nduc

tthe

estim

atio

non

the

repr

esen

tativ

epa

nelo

fhou

seho

lds

over

the

1999

to20

07pe

riod

defin

edin

Sect

ion

leve

rage

.We

cond

uctt

hees

timat

ion

onth

ere

pres

enta

tive

pane

lofh

ouse

hold

sov

erth

e19

99to

2007

perio

dde

fined

inSe

ctio

n2.

2.Pa

nelB

incl

udes

adu

mm

yva

riabl

efo

rhav

ing

ach

ilddu

ring

the

year

and

adu

mm

yva

riabl

efo

rhav

ing

twin

sdu

ring

the

year

.Th

ees

timat

ion

isco

nduc

ted

ona

sam

ple

ofho

useh

olds

that

incl

udes

allh

ouse

hold

sw

ithne

wbo

rntw

ins.

The

regr

essi

ons

are

othe

rwis

esi

mila

rto

the

base

line

regr

essi

onin

Tabl

eIII

,and

the

full

estim

atio

nde

tails

and

resu

ltsar

eav

aila

ble

inth

eIn

tern

etA

ppen

dix.

All

varia

bles

are

desc

ribed

inTa

ble

A.St

anda

rder

rors

are

clus

tere

dat

the

hous

ehol

dle

vel.

Pane

lA:R

ealE

stat

eIn

tera

cted

with

Leve

rage

Estim

ate

t-sta

tEs

timat

et-s

tat

Estim

ate

t-sta

tLo

g re

side

ntia

l rea

l est

ate

0.00

01.

370.

003

3.79

0.00

0-0

.44

Risk

y Po

rtfol

ioSt

ock

Portf

olio

Fund

Por

tfolio

Pane

l A: R

eal E

stat

e In

tera

cted

with

Lev

erag

e

(1)

(2)

(3)

Dep

ende

nt V

aria

ble:

Val

ue L

oadi

ng

gLo

g co

mm

erci

al re

al e

stat

e0.

001

2.01

0.00

79.

870.

000

-0.8

8Lo

g re

side

ntia

l rea

l est

ate

× Le

vera

ge ra

tio-0

.001

-4.2

8-0

.004

-4.8

80.

000

-1.4

0Lo

g co

mm

erci

al re

al e

stat

e ×

Leve

rage

ratio

-0.0

01-3

.13

0.00

0-0

.45

-0.0

01-3

.48

Leve

rage

ratio

-0.0

12-4

.11

-0.0

40-5

.10

-0.0

04-2

.29

Dd

tVi

blV

lL

diPa

nel B

: Chi

ldre

n

Estim

ate

t-sta

tEs

timat

et-s

tat

Estim

ate

t-sta

tD

umm

y fo

r hav

ing

child

ren

0.08

717

.21

0.02

82.

170.

038.

20D

umm

y fo

r hav

ing

twin

s-0

.020

-2.6

3-0

.039

-1.8

3-0

.01

-1.1

5

(1)

(2)

(3)

Dep

ende

nt V

aria

ble:

Val

ue L

oadi

ngRi

sky

Portf

olio

Stoc

k Po

rtfol

ioFu

nd P

ortfo

lio

Tabl

e VI

Econ

omic

Sig

nific

ance

This

tabl

ere

ports

the

impa

cton

the

valu

elo

adin

gof

life-

cycl

eva

riatio

nin

age

and

finan

cial

char

acte

ristic

s.W

eus

eas

benc

hmar

ksa

30-y

ear

old

hous

ehol

dhe

ad,a

50-y

ear-o

ldho

useh

old

head

,and

a70

-yea

rol

dho

useh

old

head

,to

whi

chw

eas

sign

the

aver

age

char

acte

ristic

sof

hous

ehol

dsin

thei

rre

spec

tive

coho

rtsin

2003

The

impa

ctof

chan

ges

inw

eas

sign

the

aver

age

char

acte

ristic

sof

hous

ehol

dsin

thei

rre

spec

tive

coho

rtsin

2003

.Th

eim

pact

ofch

ange

sin

char

acte

ristic

sis

asse

ssed

usin

gth

eba

selin

ere

gres

sion

coef

ficie

nts

inTa

ble

III.A

llva

riabl

esar

ede

scrib

edin

Tabl

eA.

30→

5050→

7030→

5050→

7030→

5050→

70O

bser

ved

chan

ge in

val

ue lo

adin

g0.

090.

140.

230.

250.

020.

04

Risk

y Po

rtfol

ioSt

ock

Portf

olio

Fund

Por

tfolio

gg

Pred

icte

d ch

ange

Fina

ncia

l Cha

ract

eris

tics

Log

finan

cial

wea

lth

0.01

50.

008

0.04

20.

022

0.01

00.

005

Log

resi

dent

ial r

eal e

stat

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001

0.00

00.

005

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000

0.00

0Lo

g co

mm

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al re

al e

stat

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001

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010

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000

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vera

gera

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000

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002

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000

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vera

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tio0.

000

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00.

002

0.00

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000

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uman

Cap

ital a

nd In

com

e R

isk

Log

hum

an c

apita

l 0.

024

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70.

048

0.07

30.

010

0.01

5Lo

g in

com

e-0

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lf-em

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men

t dum

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ploy

men

t dum

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0.00

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001

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00.

001

0.00

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ditio

nali

ncom

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latili

ty0

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tiona

l inc

ome

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tility

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000

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000

Dem

ogra

phic

Cha

ract

eris

tics

Age

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50.

055

0.17

70.

177

0.01

20.

012

Mal

e ho

useh

old

head

dum

my

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0.00

0-0

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High

sch

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001

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003

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001

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igh

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ol d

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001

0.00

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id

tid

000

20

001

000

10

000

000

10

001

Econ

omic

s ed

ucat

ion

dum

my

0.00

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000

0.00

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igra

tion

dum

my

0.00

00.

003

0.00

00.

007

0.00

00.

000

Fam

ily s

ize

-0.0

35-0

.015

-0.0

24-0

.011

-0.0

16-0

.007

Cha

nge

due

to a

ge a

nd fi

nanc

ial c

hara

cter

istic

s0.

090

0.09

40.

278

0.28

70.

028

0.02

7Fr

actio

n du

e to

age

61

.12%

58.1

0%63

.61%

61.5

4%41

.14%

42.9

0%

Tabl

e VI

ISy

stem

atic

Lab

or In

com

e R

isk

This

tabl

ein

vest

igat

esth

efa

ctor

stru

ctur

eof

indu

stry

-leve

linc

ome

grow

than

dits

impl

icat

ions

forh

ouse

hold

finan

cial

portf

olio

s.Fo

rea

chof

the

70tw

o-di

git

indu

strie

s,w

ere

gres

sse

ctor

alin

com

egr

owth

onag

greg

ate

inco

me

grow

than

dre

port

inPa

nel

Ath

edi

strib

utio

nof

the

corre

spon

ding

slop

esan

dR

2co

effic

ient

sPa

nelB

repo

rtspo

oled

regr

essi

ons

ofth

eho

useh

old

portf

olio

valu

edi

strib

utio

nof

the

corre

spon

ding

slop

esan

dR

coef

ficie

nts.

Pane

lBre

ports

pool

edre

gres

sion

sof

the

hous

ehol

dpo

rtfol

iova

lue

load

ing

on(i)

the

load

ing

ofho

useh

old

inco

me

onag

greg

ate

inco

me,

(ii)s

tand

ard

char

acte

ristic

s,an

d(ii

i)ye

ar,i

ndus

try,a

ndco

unty

fixed

effe

cts.

The

hous

ehol

din

com

elo

adin

gis

defin

edas

the

wei

ghte

dav

erag

elo

adin

gof

the

sect

ors

inw

hich

the

adul

tsin

the

hous

ehol

dar

eem

ploy

ed.T

hefu

llre

sults

are

repo

rted

inth

eIn

tern

etAp

pend

ix.T

heco

mpu

tatio

nsar

eba

sed

onth

ere

pres

enta

tive

pane

lofh

ouse

hold

sov

erth

e19

99to

2007

perio

dde

fined

inS

ectio

n2.

2.St

anda

rder

rors

are

clus

tere

dat

the

hous

ehol

dle

vel.

Pane

lA:C

ross

Sect

iona

lDis

tribu

tion

ofIn

com

eEx

posu

reto

Aggr

egat

eIn

com

eSh

ocks

Mea

n10

th25

th50

th75

th90

thLo

adin

g of

sec

tora

l inc

ome

on a

ggre

gate

inco

me

1.03

0.81

0.95

1.05

1.15

1.22

R2

0.88

0.74

0.83

0.92

0.95

0.96

Pane

l B: I

ncom

e Ex

posu

re to

Agg

rega

te In

com

e Sh

ocks

Pane

l A: C

ross

-Sec

tiona

l Dis

tribu

tion

of In

com

e Ex

posu

re to

Agg

rega

te In

com

e Sh

ocks

Dep

ende

ntVa

riabl

e:Va

lue

Load

ing

Estim

ate

t-sta

tEs

timat

et-s

tat

Estim

ate

t-sta

tLo

adin

g of

sec

tora

l inc

ome

on a

ggre

gate

inco

me

-0.2

05-1

0.05

-0.2

00-3

.86

-0.0

77-5

.54

Con

ditio

nal i

ncom

e vo

latili

ty-0

.342

-20.

45-0

.330

-10.

30-0

.111

-12.

23

Dep

ende

nt V

aria

ble:

Val

ue L

oadi

ngRi

sky

Portf

olio

Stoc

k Po

rtfol

ioFu

nd P

ortfo

lio(1

)(2

)(3

)

Tabl

e VI

IIA

ctiv

e R

ebal

anci

ng o

f the

Val

ue L

oadi

ng

This

tabl

ere

ports

pool

edre

gres

sion

sof

the

activ

ech

ange

inth

eva

lue

load

ing

on(i)

the

pass

ive

chan

gein

the

valu

elo

adin

gan

d(ii

)the

lagg

edva

lue

load

ing.

We

cond

uctt

hean

alys

isat

the

leve

loft

heris

kypo

rtfol

ioin

colu

mns

(1)a

nd(2

),th

est

ock

portf

olio

inco

lum

ns(3

)and

(4),

and

the

fund

portf

olio

inco

lum

ns(5

)an

d(6

)Fo

rea

chpo

rtfol

iow

ere

port

the

regr

essi

onw

ithan

dw

ithou

tla

gged

hous

ehol

dsch

arac

teris

tics

All

varia

bles

are

dem

eane

dea

chye

arTh

ean

d(6

).Fo

rea

chpo

rtfol

io,

we

repo

rtth

ere

gres

sion

with

and

with

out

lagg

edho

useh

olds

char

acte

ristic

s.A

llva

riabl

esar

ede

mea

ned

each

year

.Th

eco

mpu

tatio

nsar

eba

sed

onth

ere

pres

enta

tive

pane

lofh

ouse

hold

sov

erth

e19

99to

2007

perio

dde

fined

inSe

ctio

n2.

2.St

anda

rder

rors

are

clus

tere

dat

the

hous

ehol

dle

vel.

Dep

ende

nt V

aria

ble:

Act

ive C

hang

e of

Val

ue L

oadi

ng

(6)

Fund

Por

tfolio

(1)

(2)

(3)

(4)

Risk

y Po

rtfol

ioSt

ock

Portf

olio

(5)

Estim

ate

t-sta

tEs

timat

et-s

tat

Estim

ate

t-sta

tEs

timat

et-s

tat

Estim

ate

t-sta

tEs

timat

et-s

tat

Valu

e Lo

adin

g Va

riabl

esPa

ssive

cha

nge

in th

e va

lue

load

ing

-0.3

56-2

7.63

-0.3

56-2

7.61

-0.3

72-2

7.30

-0.3

75-2

7.40

-0.2

83-2

7.95

-0.2

84-2

7.98

Lagg

ed v

alue

load

ing

-0.1

16-4

1.95

-0.1

19-4

2.55

-0.0

78-3

8.24

-0.0

82-3

9.15

-0.1

10-5

4.30

-0.1

11-5

4.41

Lagg

ed F

inan

cial

Cha

ract

eris

tics

(6)

(1)

(2)

(3)

(4)

(5)

Log

finan

cial

wea

lth

0.00

24.

810.

005

6.08

0.00

00.

90Lo

g re

side

ntia

l rea

l est

ate

0.00

01.

970.

001

3.71

0.00

0-1

.96

Log

com

mer

cial

real

est

ate

0.00

01.

640.

001

5.09

0.00

01.

65Le

vera

ge ra

tio0.

001

2.30

0.00

0-0

.04

0.00

00.

22La

gged

Inco

me

Log

hum

an c

apita

l -0

.020

-14.

49-0

.031

-12.

31-0

.009

-11.

79Lo

g in

com

e-0

.002

-1.5

80.

008

3.21

0.00

00.

36Se

lf-em

ploy

men

t dum

my

-0.0

06-2

.79

-0.0

06-1

.51

-0.0

01-0

.69

Unem

ploy

men

t dum

my

-0.0

04-2

.28

-0.0

04-1

.27

0.00

00.

00C

ondi

tiona

l inc

ome

vola

tility

-0.0

51-1

1.21

-0.0

28-3

.61

-0.0

11-4

.92

Lagg

ed D

emog

raph

ic C

hara

cter

istic

Fam

ily s

ize

0.00

614

.40

0.00

11.

830.

002

9.10

yAd

just

ed R

26.

85%

0.07

05.

27%

0.05

47.

06%

0.07

1Nu

mbe

r of o

bser

vatio

ns40

6,56

140

6,56

122

1,14

322

1,14

335

5,44

335

5,44

3

Tabl

e IX

Valu

e Lo

adin

gs o

f New

Par

ticip

ants

This

tabl

ere

ports

pool

edre

gres

sion

sof

ane

wpa

rtici

pant

’sva

lue

load

ing

onso

cioe

cono

mic

char

acte

ristic

san

dye

ar,i

ndus

try,a

ndco

unty

fixed

effe

cts.

We

cond

uctt

hees

timat

ion

onho

useh

olds

that

ente

rth

est

ock

mar

ket

betw

een

1999

to20

07in

the

repr

esen

tativ

esa

mpl

ede

fined

inSe

ctio

n2

2Th

een

ter

the

stoc

km

arke

tbe

twee

n19

99to

2007

inth

ere

pres

enta

tive

sam

ple

defin

edin

Sect

ion

2.2.

The

anal

ysis

isba

sed

onda

tain

the

year

ofen

try.A

llva

riabl

esar

ede

scrib

edin

Tabl

eA.

(1)

(2)

(3)

Risk

y Po

rtfol

ioSt

ock

Portf

olio

Dep

ende

nt V

aria

ble:

Val

ue L

oadi

ng Fund

Por

tfolio

Estim

ate

t-sta

tEs

timat

et-s

tat

Estim

ate

t-sta

tFi

nanc

ial C

hara

cter

istic

sLo

g fin

anci

al w

ealth

0.

015

2.25

0.10

66.

820.

014

3.78

Log

resi

dent

ial r

eal e

stat

e-0

.002

-1.4

6-0

.001

-0.4

3-0

.001

-0.9

9Lo

g co

mm

erci

al re

al e

stat

e0.

000

0.13

0.00

92.

270.

000

0.39

Leve

rage

ratio

0.01

54.

980.

037

3.79

0.00

0-0

.22

Leve

rage

ratio

0.01

54.

980.

037

3.79

0.00

00.

22H

uman

Cap

ital a

nd In

com

e R

isk

Log

hum

an c

apita

l -0

.036

-1.4

9-0

.059

-1.2

6-0

.005

-0.3

8Lo

g in

com

e-0

.048

-2.1

2-0

.041

-0.8

7-0

.018

-1.9

5Se

lf-em

ploy

men

t dum

my

-0.1

89-4

.51

-0.2

43-3

.43

0.00

10.

08Un

empl

oym

ent d

umm

y-0

.029

-1.4

2-0

.126

-2.2

00.

012

1.10

Con

ditio

nali

ncom

evo

latili

ty-0

328

-599

-025

3-2

21-0

065

-211

Con

ditio

nal i

ncom

e vo

latili

ty-0

.328

-5.9

9-0

.253

-2.2

1-0

.065

-2.1

1D

emog

raph

ic C

hara

cter

istic

sAg

e0.

001

2.08

0.00

74.

380.

000

1.08

Mal

e ho

useh

old

head

dum

my

-0.1

09-7

.88

-0.1

26-3

.57

-0.0

20-2

.37

High

sch

ool d

umm

y0.

005

0.30

0.02

10.

490.

007

0.74

Post

-hig

h sc

hool

dum

my

-0.0

58-3

.72

-0.0

89-2

.46

-0.0

12-1

.33

Econ

omic

sed

ucat

ion

dum

my

003

61

870

000

000

001

61

33Ec

onom

ics

educ

atio

n du

mm

y-0

.036

-1.8

70.

000

0.00

-0.0

16-1

.33

Imm

igra

tion

dum

my

-0.0

43-2

.31

-0.0

61-1

.37

0.01

71.

64Fa

mily

siz

e0.

030

5.07

0.01

00.

690.

006

1.88

Adju

sted

R2

2.06

%3.

73%

0.44

%Nu

mbe

r of o

bser

vatio

ns13

,927

4,77

910

,472

Tabl

e X

Exis

ting

vs. N

ew P

artic

ipan

ts

This

tabl

ere

ports

apo

oled

regr

essi

onof

the

valu

elo

adin

gon

(i)ag

edu

mm

ies

forp

artic

ipat

ing

hous

ehol

ds,

(ii)

age

dum

mie

sfo

rho

useh

olds

that

ente

rris

kyas

set

mar

kets

durin

gth

eye

ar,

(iii)

all

the

othe

rch

arac

teris

tics

ofth

eba

selin

ere

gres

sion

and

year

indu

stry

and

coun

tyfix

edef

fect

sAg

edu

mm

ies

are

char

acte

ristic

sof

the

base

line

regr

essi

on,

and

year

,in

dust

ry,

and

coun

tyfix

edef

fect

s.Ag

edu

mm

ies

are

cum

ulat

ive.

The

com

puta

tions

are

base

don

the

repr

esen

tativ

esa

mpl

eof

hous

ehol

dsov

erth

e19

99to

2007

perio

dde

fined

inSe

ctio

n2.

2.Al

lvar

iabl

esar

edi

scus

sed

inTa

ble

A.St

anda

rder

rors

are

clus

tere

dat

the

hous

ehol

dle

vel.

The

full

resu

ltsar

eav

aila

ble

inth

eIn

tern

etAp

pend

ix.

Dep

ende

nt V

aria

ble:

Val

ue L

oadi

ngRi

sky

Portf

olio

Stoc

kPo

rtfol

ioFu

ndPo

rtfol

io

Estim

ate

t-sta

tEs

timat

et-s

tat

Estim

ate

t-sta

tA

ge D

umm

ies

30 a

nd a

bove

0.00

30.

450.

052

3.29

0.00

0-0

.07

35 a

nd a

bove

0.00

51.

230.

047

4.28

0.00

0-0

.05

40d

b0

010

287

003

94

140

005

211

Risk

y Po

rtfol

ioSt

ock

Portf

olio

Fund

Por

tfolio

(1)

(2)

(3)

40 a

nd a

bove

0.01

02.

870.

039

4.14

0.00

52.

1145

and

abo

ve0.

012

3.55

0.05

86.

620.

002

0.91

50 a

nd a

bove

0.01

54.

490.

040

5.17

0.00

00.

1555

and

abo

ve0.

019

5.98

0.03

75.

600.

002

1.07

60 a

nd a

bove

0.01

13.

490.

022

3.36

0.00

31.

5965

and

abo

ve0.

019

3.52

0.02

11.

920.

006

1.66

70 a

nd a

bove

0.05

85.

620.

139

6.47

0.02

33.

46N

ew E

ntra

nt D

umm

ies

New

entra

nt a

ged

30+

-0.0

91-5

.99

-0.2

52-6

.14

-0.0

07-0

.83

New

entra

nt a

ged

35+

-0.0

07-0

.35

-0.0

07-0

.12

-0.0

08-0

.62

New

entra

nt a

ged

40+

-0.0

32-1

.42

-0.0

90-1

.48

0.00

40.

29Ne

w en

trant

age

d 45

+-0

.007

-0.2

8-0

.025

-0.4

10.

000

-0.0

2Ne

w en

trant

age

d 50

+0.

001

0.07

0.03

70.

690.

005

0.40

New

entra

nt a

ged

55+

-0.0

20-0

.93

-0.0

29-0

.61

-0.0

09-0

.65

New

entra

nt a

ged

60+

-0.0

01-0

.04

-0.0

68-1

.23

-0.0

02-0

.12

New

entra

nt a

ged

65+

-0.1

19-2

.96

-0.0

28-0

.39

-0.0

10-0

.43

New

entra

nt a

ged

70+

-0.0

10-0

.45

-0.0

13-0

.26

0.03

22.

58

Tabl

e XI

Stoc

ks M

ost W

idel

y H

eld

by S

wed

ish

Hou

seho

lds

The

tabl

ere

ports

the

ten

stoc

ksth

atar

em

ost

wid

ely

held

bySw

edis

hho

useh

olds

atth

een

dof

2003

.St

ocks

are

sorte

dby

the

prop

ortio

nof

hous

ehol

dsth

atho

ldth

emdi

rect

ly(fi

rstc

olum

n).W

eal

sore

port

the

stoc

k’s

perc

enta

gein

aggr

egat

eho

useh

old

finan

cial

wea

lth(s

econ

dco

lum

n),

the

stoc

k’s

perc

enta

geof

the

tota

lmar

ketc

apita

lizat

ion

ofal

lfirm

slis

ted

onSw

edis

hex

chan

ges

(third

colu

mn)

the

stoc

k’s

perc

enta

geof

the

free

the

stoc

ks

perc

enta

geof

the

tota

lmar

ketc

apita

lizat

ion

ofal

lfirm

slis

ted

onSw

edis

hex

chan

ges

(third

colu

mn)

,the

stoc

ks

perc

enta

geof

the

free

float

-adj

uste

dm

arke

tca

pita

lizat

ion

ofal

lfir

ms

liste

don

Swed

ish

exch

ange

s(fo

urth

colu

mn)

,th

est

ock’

sva

lue

load

ing

(fifth

colu

mn)

,an

dth

epe

rcen

tile

ofth

est

ock’

sbo

ok-to

-mar

ket

ratio

(six

thco

lum

n).

The

anal

ysis

isco

nduc

ted

onth

ere

pres

enta

tive

pane

ldef

ined

inSe

ctio

n2.

2.Th

eag

greg

ate

hous

ehol

dfin

anci

alw

ealth

used

inth

ese

cond

colu

mn

isth

eto

tala

mou

ntof

wea

lthow

ned

byris

kyas

setm

arke

tpar

ticip

ants

.In

the

last

row

we

cons

ider

the

aggr

egat

epo

rtfol

ioof

the

top

ten

popu

lar

stoc

ks.T

heva

lue

load

ings

and

book

-to-m

arke

trat

iope

rcen

tiles

are

base

don

stoc

kav

erag

es,w

here

stoc

ksar

ew

eigh

ted

byth

eirs

hare

sof

aggr

egat

eho

useh

old

portf

olio

repo

rted

inth

ese

cond

colu

mn.

Eric

sson

60.4

6%21

.69%

7.49

%8.

70%

-1.2

225

.41%

Telia

46.5

0%4.

02%

6.48

%4.

16%

-1.0

044

.19%

Swed

bank

24.5

4%3.

76%

2.74

%2.

75%

0.11

46.8

5%SE

B23

.57%

5.52

%2.

69%

3.14

%0.

7456

.21%

% o

f Hou

seho

ld

Stoc

k W

ealth

% o

f Sto

ckho

lder

s O

wnin

g C

ompa

ny%

of S

wedi

sh

Stoc

kmar

ket

Valu

e Lo

adin

gB/

M Q

uant

ile%

of S

wedi

sh

Free

Flo

at

SEB

23.5

7%5.

52%

2.69

%3.

14%

0.74

56.2

1%Vo

lvo14

.58%

5.00

%3.

18%

3.36

%0.

4168

.94%

H&M

11.3

9%4.

75%

5.21

%3.

76%

-0.0

74.

29%

Bille

rud

10.7

8%1.

11%

0.22

%0.

25%

-0.0

646

.26%

Astra

Zene

ca9.

66%

5.38

%4.

81%

3.79

%0.

0968

.23%

Noki

a8.

71%

3.78

%23

.77%

31.1

4%-0

.08

14.6

9%In

vest

or8

61%

248

%1

95%

159

%0

2780

77%

Inve

stor

8.61

%2.

48%

1.95

%1.

59%

0.27

80.7

7%57

.49%

58.5

3%62

.64%

-0.4

139

.21%

Aggr

egat

e po

rtfol

io o

f pop

ular

sto

cks

Tabl

e XI

IPo

rtfo

lios

of P

opul

ar a

nd P

rofe

ssio

nally

Clo

se S

tock

s

This

tabl

ere

ports

pool

edre

gres

sion

sof

the

valu

elo

adin

gof

hous

ehol

dsu

bpor

tfolio

son

hous

ehol

dch

arac

teris

tics

inth

epr

esen

ceof

year

,in

dust

ry,

and

coun

tyfix

edef

fect

s.Ev

ery

subp

ortfo

lioin

the

tabl

eis

asu

bset

ofth

est

ock

portf

olio

.W

eco

nsid

erth

epo

rtfol

ioof

popu

lar

stoc

ksin

colu

mn

(1)

the

portf

olio

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defin

edin

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tion

2.2.

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aria

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ble

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on(i)

the

num

ber

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ars

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nelw

hen

the

hous

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pate

sin

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char

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fixed

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and

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base

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72

Tabl

e XV

IC

omm

unic

atio

n

This

tabl

ere

ports

pool

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gres

sion

sof

the

valu

elo

adin

gon

year

fixed

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cts

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acte

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ins

com

mun

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r(“H

igh

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mun

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),an

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with

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mm

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gin

frequ

ently

with

each

othe

r(“L

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valu

elo

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com

pute

dat

the

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colu

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(1)a

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)th

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)and

(4)

and

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lue

load

ing

isco

mpu

ted

atth

ele

velo

fthe

risky

portf

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inco

lum

ns(1

)and

(2),

the

stoc

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mns

(3)a

nd(4

),an

dth

efu

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(5)a

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).A

twin

pair

iscl

assi

fied

as“H

igh

Com

mun

icat

ion”

ifth

efre

quen

cyof

med

iate

dco

mm

unic

atio

nan

dth

efre

quen

cyof

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edia

ted

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mun

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ion

are

both

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eth

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edia

n,an

das

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Com

mun

icat

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othe

rwis

e.Th

eco

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atio

nsu

bsam

ples

are

obta

ined

from

the

1999

to20

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nel

ofpa

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gho

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olds

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anad

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mbe

r of o

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,230

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8815

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17,4

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36,0

08

Tabl

e XV

IIA

CE

Dec

ompo

sitio

n

This

tabl

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ports

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com

posi

tion

ofth

eva

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with

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sov

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99to

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port

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Low

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Tabl

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Def

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adin

gin

year

tis

the

wea

lth-w

eigh

ted

aver

age

year

-tlo

adin

gof

hous

ehol

dsin

the

coho

rt.Th

efig

ure

isba

sed

onth

epa

nelo

fall

Sw

edis

hdi

rect

stoc

khol

ders

over

the

1999

to20

07pe

riod.

04

02

0.3

0.4

oading

0

0.1

0.2

Value Lo

02

-0.10

04

-0.3

- 0.2

Age

-0.4

3035

4045

5055

6065

7075