FIGHT OR FLIGHT? PORTFOLIO REBALANCING BY INDIVIDUAL ... · FIGHT OR FLIGHT? PORTFOLIO REBALANCING BY INDIVIDUAL INVESTORS ... in perceived investment opportunities, on the other

FIGHT OR FLIGHT? PORTFOLIO REBALANCINGBY INDIVIDUAL INVESTORS∗

LAURENT E. CALVET

JOHN Y. CAMPBELL

PAOLO SODINI

This paper investigates the dynamics of individual portfolios in a unique dataset containing the disaggregated wealth of all households in Sweden. Between1999 and 2002, we observe little aggregate rebalancing in the financial portfolioof participants. These patterns conceal strong household-level evidence of activerebalancing, which on average offsets about one-half of idiosyncratic passive vari-ations in the risky asset share. Wealthy, educated investors with better diversifiedportfolios tend to rebalance more actively. We find some evidence that householdsrebalance toward a greater risky share as they become richer. We also study thedecisions to trade individual assets. Households are more likely to fully sell di-rectly held stocks if those stocks have performed well, and more likely to exitdirect stockholding if their stock portfolios have performed well; but these rela-tionships are much weaker for mutual funds, a pattern that is consistent withprevious research on the disposition effect among direct stockholders and perfor-mance sensitivity among mutual fund investors. When households continue tohold individual assets, however, they rebalance both stocks and mutual funds tooffset about one-sixth of the passive variations in individual asset shares. House-holds rebalance primarily by adjusting purchases of risky assets if their riskyportfolios have performed poorly, and by adjusting both fund purchases and fullsales of stocks if their risky portfolios have performed well. Finally, the tendencyfor households to fully sell winning stocks is weaker for wealthy investors withdiversified portfolios of individual stocks.

I. INTRODUCTION

What drives time series variations in the asset allocationof individual investors? How do households adjust their risk

∗We thank Statistics Sweden for providing the data. We received helpful com-ments from Markus Brunnermeier, Rene Garcia, David McCarthy, Stefan Nagel,Paolo Zaffaroni, three anonymous referees, and seminar participants at Bergen,Boston University, the Federal Reserve Bank of Minneapolis, the Financial Ser-vices Authority of the UK, Harvard University, Imperial College, MIT, OxfordUniversity, Pompeu Fabra, SIFR, the Stockholm School of Economics, the SwedishFinancial Supervisory Authority, Universite Libre de Bruxelles, the University ofHelsinki, the University of Venice, the Wharton School, the 2007 Brazilian FinanceSociety meeting in Sao Paulo, the 2007 Summer Finance Conference in Gerzensee,the Fall 2007 NBER Asset Pricing Meeting, and the 2008 AFA meetings in NewOrleans. The project benefited from excellent research assistance by Daniel Sunes-son. This material is based upon work supported by the Agence Nationale de laRecherche under a Chaire d’Excellence to Calvet, BFI under a Research Grantto Sodini, the HEC Foundation, the National Science Foundation under Grant0214061 to Campbell, the Riksbank, and the Wallander and Hedelius [email protected], john [email protected], [email protected].

C© 2009 by the President and Fellows of Harvard College and the Massachusetts Institute ofTechnology.The Quarterly Journal of Economics, February 2009

301

302 QUARTERLY JOURNAL OF ECONOMICS

exposure in response to the portfolio returns that they experi-ence? Are household portfolios characterized by inertia or highturnover? Financial theory suggests a wide range of motives foractive trading and rebalancing at the household level. Realized re-turns on financial assets induce mechanical variations in portfolioallocation, to which an investor is passively exposed. An investormight fight passive changes by actively rebalancing her portfoliowhen asset returns are expected to be time-invariant. Changesin perceived investment opportunities, on the other hand, mightlead the investor to adopt a flight strategy that would amplify thedecline in the share of the worst-performing assets. Furthermore,trading decisions may reflect not only asset allocation objectives,but also a disposition to hold losing and sell winning securities(Shefrin and Statman 1985; Odean 1998).

Equilibrium considerations suggest that aggregate flows fromthe household sector provide limited and potentially misleadinginformation on individual rebalancing. Consider, for instance, aneconomy in which households own all financial assets. If the aggre-gate value of risky securities falls, the average share of risky as-sets in household portfolios must necessarily fall as well. Thus, theaverage individual investor cannot fight aggregate variations inequity returns. When households have heterogeneous portfolios,however, there can still be substantial rebalancing at the individ-ual level. For instance, it is an open question whether householdswith higher passive losses tend to buy or sell risky assets.

The empirical investigation of household rebalancing there-fore requires high-quality and comprehensive micro data.Traditional data sets do not meet these requirements and haveunsurprisingly led to conflicting answers on household behavior.Surveys, which have been widely used in the household financeliterature, only report the allocation of household financial wealthinto broad asset classes (e.g., Bilias, Georgarakos, and Haliassos[2008]). They permit the analysis of changes in the share of riskyassets in the financial portfolio, but not the computation of activeand passive changes. Thus, surveys cannot tell us whether house-holds attempt to offset passive variations in their risky share.

Account data sets, such as 401k and brokerage accounts,present a partial view of financial wealth and do not permit thecomputation of the risky share. Research based on discount bro-kerage accounts finds evidence of intense trading activity (e.g.,Odean [1999]; Barber and Odean [2000]), whereas substantial in-ertia is observed in 401k accounts (e.g., Madrian and Shea [2001];

PORTFOLIO REBALANCING BY INDIVIDUAL INVESTORS 303

Choi et al. [2002, 2004]; Agnew, Balduzzi, and Sunden [2003];Ameriks and Zeldes [2004]). These seemingly contradictory re-sults may result from selection bias in account data sets. For ex-ample, households may choose a discount broker precisely becausethey are (over)confident in their ability to process information andintend to engage in high-frequency trading. And households maytrade less actively in retirement accounts than in other accountsthat they control.

The Swedish data set used in Calvet, Campbell, and Sodini(2007, henceforth “CCS 2007”) allows us to overcome these issues.We assembled data supplied by Statistics Sweden into a panelcovering four years (1999–2002) and the entire country (about 4.8million households). The information available on each residentis systematically compiled by financial institutions and corpora-tions and includes demographic characteristics, wealth portfolio,and income. Our administrative data set is therefore more re-liable than self-reported data sets, such as surveys. The wealthinformation is highly disaggregated and provides the worldwideassets owned by the resident at the end of a tax year. All financialassets held outside retirement accounts are reported, includingbank accounts, mutual funds, and stocks. However, the databasereports neither the exact date of a sale nor information on assetpurchases.

In CCS 2007, we found that household portfolios of risky as-sets have important idiosyncratic exposure, accounting for justover half the variance of return for the median household. Un-derdiversification causes only modest welfare losses for most ofthe population ex ante; however, the realized returns on house-hold portfolios are heterogeneous ex post. In this paper we exploitthis cross-sectional variation to analyze the determinants of port-folio rebalancing. The Swedish data set is well suited for suchan investigation because we can compute the risky share of ev-ery household and decompose its changes into passive and activecomponents.

Our main results are the following. First, we study the dy-namics of the risky asset share among participating households.The equal-weighted share of household financial wealth investedin risky assets fell from 57% in 1999 to 45% in 2002, a declinethat implies very weak active rebalancing by the Swedish house-hold sector as a whole in response to the equity bear market ofthe early 2000s. In striking contrast to this aggregate result, indi-vidual households actively rebalanced their portfolios in response


to their own returns. Household-level regressions show that onaverage, active rebalancing compensates for about one-half of id-iosyncratic passive variations in the risky share.

We estimate a partial adjustment model for the risky share,with heterogeneous adjustment speeds, and find that financiallysophisticated households holding well-diversified portfolios adjustmore rapidly toward their target risky share. We also find someevidence that the target risky share increases when householdsbecome richer, consistent with theories of declining relative riskaversion, portfolio insurance, or habit formation (Brennan andSchwartz 1988; Constantinides 1990; Dybvig 1995; Campbell andCochrane 1999; Carroll 2000, 2002).

Second, we study patterns of entry to and exit from riskyfinancial markets. The overall stock market participation rate in-creased slightly between 1999 and 2002. At the microeconomiclevel, household demographics influence entry and exit as onewould expect: financially sophisticated households, with greaterincome, wealth, and education, are more likely to enter and lesslikely to exit. We are able to go beyond this familiar result tosee how portfolio characteristics influence exit decisions. We findthat households with initially more aggressive investment strate-gies are generally less likely to exit, although poorly diversifiedhouseholds and those with extremely high initial risky sharesare slightly more likely to exit. If we consider mutual funds anddirectly held stocks as separate asset classes, we find that house-holds are slightly more likely to exit mutual fund holding whentheir mutual funds have performed badly, but much more likelyto exit direct stockholding when their stocks have performed well.

Third, we explore decisions to adjust positions in individualstocks and mutual funds. We begin by examining decisions tofully sell positions. We find that the absolute value of the returnon a stock or fund has a positive effect on the probability thata household will sell it. This effect is much stronger for stockswith positive returns (winners) than for stocks with negative re-turns (losers), but the asymmetry is much weaker for mutualfunds. We allow portfolio and household characteristics to influ-ence the strength of these return effects, and find that wealthyinvestors with diversified portfolios of individual stocks have aweaker propensity to dispose of winning stocks and a strongerpropensity to dispose of losers.

We also estimate a rebalancing model for positions that arenot fully sold. We find that the passive change in the share of a


stock or mutual fund in the risky portfolio does explain the activechange, but the effect is weaker than we found when we treated allrisky assets as a homogeneous asset class. Instead of a rebalancingcoefficient of one-half, we obtain coefficients of about one-sixth,which are only slightly greater for stocks than for mutual funds.Thus the difference in household decision making with respect tostocks and mutual funds shows up primarily in full sales ratherthan in partial rebalancing decisions.

Finally, we investigate the relation between asset-level trad-ing decisions and portfolio rebalancing. Households primarily re-balance by using a small number of trading strategies. When ahousehold is unlucky, in the sense that its risky portfolio performsworse than average, rebalancing is mostly driven by adjustmentsin purchases of risky assets. Conversely, when the household islucky, in the sense that its risky portfolio performs better than av-erage, the household rebalances primarily by adjusting full salesof stocks and purchases of mutual funds.

Both our entry and exit results and our results on asset-leveltrading decisions are consistent with two branches of the litera-ture. The disposition effect, that investors hold losing stocks andsell winning stocks, has been documented using account data ondirect stockholdings by Odean (1998) and many others; Grinblattand Han (2005), Frazzini (2006), and Goetzmann and Massa(2008) present evidence that this behavior may contribute to mo-mentum in stock returns. The literature on mutual fund flows, onthe other hand, finds evidence of performance chasing by individ-ual investors (Ippolito 1992; Gruber 1996; Chevalier and Ellison1997; Sirri and Tufano 1998; Frazzini and Lamont 2008; Ivkovicand Weisbenner 2007). We find similar patterns using differentdata and a different approach for classifying stocks and fundsas losers or winners. Dhar and Zhu (2006) have recently foundthat households with higher self-reported income are less proneto the disposition effect in stock trading; our results are broadlyconsistent with this, although we find that wealth and portfoliodiversification are more relevant than income in predicting thestrength of the disposition effect.

The organization of the paper is as follows. In Section IIwe present some basic facts about the evolution of risk takingamong Swedish households in the period 1999–2002. In SectionIII, we assess the magnitude of active rebalancing by decompos-ing household-level portfolio variations into their passive and ac-tive components. In Section IV, we estimate a partial adjustment


model of portfolio risk and use it to ask which types of householdsadjust their portfolios more rapidly. This section also asks whetherincreases in financial wealth increase households’ desired risk ex-posure. Section V explores entry and exit decisions and asset-levelrebalancing in relation to the disposition effect. In Section VI, welink households’ asset-level decisions to their rebalancing strate-gies. Section VII concludes. An Appendix available online presentsdetails of data construction and estimation methodology.

II. HOW HAS RISK TAKING CHANGED OVER TIME?

II.A. Data Description and Definitions

Swedish households pay taxes on both income and wealth.For this reason, the national Statistics Central Bureau (SCB),also known as Statistics Sweden, has a parliamentary mandate tocollect highly detailed information on the finances of every house-hold in the country. We compiled the data supplied by SCB intoa panel covering four years (1999–2002) and the entire popula-tion of Sweden (about 4.8 million households). The informationavailable on each resident can be grouped into three main cat-egories: demographic characteristics, income, and disaggregatedwealth.

Demographic information includes age, gender, marital sta-tus, nationality, birthplace, education, and place of residence. Thehousehold head is defined as the individual with the highest in-come. The education variable includes high-school and post-high-school dummies for the household head.

Income is reported by individual source. For capital income,the database reports the income (interest, dividends) that hasbeen earned on each bank account or each security. For laborincome, the database reports gross labor income and businesssector.

The panel’s distinguishing feature is that it contains highlydisaggregated wealth information. We observe the worldwide as-sets owned by each resident on December 31 of each year, includ-ing bank accounts, mutual funds, and stocks. The information isprovided for each individual account or each security referencedby its International Security Identification Number (ISIN). Thedatabase also records contributions made during the year to pri-vate pension savings, as well as debt outstanding at year end andinterest paid during the year.


We will refer to the following asset classes throughout thepaper. Cash consists of bank account balances and money marketfunds. Stocks refer to direct holdings only. Risky mutual funds areclassified as either bond funds or equity funds. The latter categoryis broadly defined to include any fund that invests a fraction ofits assets in stocks; that is, balanced funds are counted as equityfunds.1 Risky assets include stocks and risky mutual funds.

Following CCS 2007, we measure a household’s total financialwealth as the sum of its holdings in these asset classes, excludingfrom consideration illiquid assets such as real estate or consumerdurables, defined contribution retirement accounts, capital insur-ance products that combine return guarantees with risky assetholdings, and directly held bonds. Also, our measure of wealth isgross wealth and does not subtract mortgage or other householddebt. CCS 2007 summarizes the relative magnitudes of all thesecomponents of Swedish household balance sheets.

A participant is a household whose financial wealth includesrisky assets. In Table I, Panel A, we report summary statistics onthe assets held by participating households. To facilitate interna-tional comparisons, we convert all financial quantities into U.S.dollars. Specifically, the Swedish krona traded at $0.1127 at theend of 2002, and this fixed conversion factor is used throughoutthe paper. The aggregate value of risky holdings declined by aboutone-half during the bear market. Between 1999 and 2002, house-hold stockholdings fell from $62 to $30 billion, and fund holdingsfrom $53 to $29 billion. Cash, on the other hand, increased from$49 to $57 billion over the same period.

In the same panel we also report aggregate statistics on stockand fund holdings compiled by the SCB and by the Swedish mu-tual fund association, Fondbolagens Forening (FF).2 The officialstatistics are incomplete because the SCB does not specificallyreport the aggregate cash holdings of participants and the FF se-ries only start in 2000. The aggregate estimates obtained withour data set closely match available official statistics. In the Ap-pendix, we also match official aggregate statistics on flows intostocks and mutual funds quite closely. The aggregate flow intoan asset class is generally quite modest and never exceeds a few

1. The managers of balanced funds periodically rebalance their holdings ofcash and risky assets to maintain a stable risky share. We do not try to measurethis form of rebalancing, but treat balanced funds like any other mutual funds,assuming that they have stable risk characteristics.

2. These statistics can be downloaded at www.scb.se and www.fondbolagen.se.


TA

BL

EI

SU

MM

AR

YS

TA

TIS

TIC

S

1999

2000

2001

2002

Mic

roO

ffici

alM

icro

Offi

cial

Mic

roO

ffici

alM

icro

Offi

cial

data

stat

isti

csda

tast

atis

tics

data

stat

isti

csda

tast

atis

tics

A.A

ggre

gate

hol

din

gsof

part

icip

ants

(bil

lion

dol

lars

)S

tock

s61

.764

.354

.654

.645

.347

.129

.832

.2R

isky

mu

tual

fun

ds53

.450

.452

.744

.045

.429

.429

.5E

quit

yfu

nds

49.5

46.6

48.8

39.7

40.9

24.8

25.2

Bon

dfu

nds

3.9

3.8

3.9

4.3

4.5

4.5

4.3

Cas

h48

.747

.554

.356

.7T

otal

fin

anci

alw

ealt

h16

3.9

152.

514

3.7

115.

9

B.P

arti

cipa

tion

and

aver

age

risk

ysh

are

Fra

ctio

nof

hou

seh

olds

hol

din

g:R

isky

asse

ts61

.6%

66.4

%66

.0%

64.9

%In

divi

dual

stoc

ks30

.0%

37.2

%37

.3%

38.5

%R

isky

mu

tual

fun

ds54

.6%

58.7

%58

.2%

56.4

%B

oth

indi

vidu

alst

ocks

and

risk

yfu

nds

23.0

%29

.5%

29.5

%29

.9%

Ave

rage

risk

ysh

are

(equ

al-w

eigh

ted)

56.5

%56

.6%

52.3

%45

.2%

Ave

rage

risk

ysh

are

(wea

lth

-wei

ghte

d)74

.9%

73.7

%66

.1%

54.7

%

C.A

sset

retu

rns

Inte

rest

rate

3.1%

3.9%

4.1%

4.1%

MS

CI

Sw

eden

inde

x79

.2%

−18.

0%−2

6.8%

−48.

6%P

oole

din

dex

(equ

al-w

eigh

ted)

NA

−4.3

%−1

1.1%

−32.

1%P

oole

din

dex

(val

ue-

wei

ghte

d)N

A−6

.1%

−11.

6%−3

2.2%

MS

CI

wor

ldin

dex

(in

Sw

edis

hkr

onor

)27

.2%

−7.1

%−1

1.3%

−37.

9%M

SC

IW

orld

inde

x(i

nU

.S.d

olla

rs)

19.0

%−1

8.5%

−20.

7%−2

2.4%

Not

es.T

he

tabl

ere

port

ssu

mm

ary

stat

isti

csof

aggr

egat

eh

oldi

ngs

ofpa

rtic

ipan

ts(P

anel

A),

part

icip

atio

nra

tes

and

the

risk

ysh

are

(Pan

elB

),an

das

set

retu

rns

(Pan

elC

).P

anel

Aal

soin

clu

des

the

offi

cial

stat

isti

csre

port

edby

Sta

tist

ics

Sw

eden

(sto

cks)

and

Fon

dbol

agen

sF

oren

ing

(fu

nds

)wh

enth

eyar

eav

aila

ble.

Th

epa

rtic

ipat

ion

rate

sin

Pan

elB

are

com

pute

don

the

ran

dom

subs

ampl

eof

100,

000

hou

seh

olds

con

side

red

thro

ugh

out

the

text

,wh

erea

sth

eav

erag

eri

sky

shar

e(P

anel

B)

and

pool

edin

dex

(Pan

elC

)ar

eba

sed

onth

esu

bset

ofpa

rtic

ipan

tsat

the

end

ofea

chye

arin

the

ran

dom

subs

ampl

e.


percentage points of the total household wealth invested in theclass. Thus, the strong reduction in aggregate risky holdings re-ported in Table I, Panel A, primarily results from price movementsand not from large outflows from the household sector.

Following CCS 2007, we define the following variables foreach household h. The complete portfolio contains all the stocks,mutual funds, and cash owned by the household. The risky port-folio contains stocks and mutual funds but excludes cash. Therisky share wh,t at date t is the weight of the risky portfolio inthe complete portfolio. Because the risky share is model-free, weuse it extensively throughout the paper. The household’s riskyportfolio is also characterized by its standard deviation σh,t andby its systematic exposure βh,t and Sharpe ratio Sh,t relative to aglobal equity benchmark, the MSCI World Index. The definitionand estimation of these quantities are discussed in the Appendix.

The results presented in this paper are based on householdsthat exist throughout the 1999–2002 period. We impose no con-straint on the participation status of these households, but requirethat they satisfy the following financial requirements at the endof each year. First, disposable income must be strictly positive andthe three-year rolling average must be at least 1,000 Swedish kro-nor ($113). Second, financial wealth must be no smaller than 3,000kronor ($339). For computational convenience, we have selected arandom panel of 100,000 households from the filtered population.Unless stated otherwise, all the results in the paper are based onthis fixed subsample, and unreported work confirms the strongrobustness of the reported estimates to the choice of alternativesubsamples.

II.B. Cross-Sectional Dynamics of Participation and Risk Taking

Household participation in risky asset markets increasedfrom 62% to 65% between 1999 and 2002, as is reported in Table I,Panel B. The inflow is equal to 20% of nonparticipating house-holds, or about 8% of the entire population. The outflow is 7%of participants, or about 4% of the entire household population.These patterns are consistent with the “participation turnover”documented for U.S. data (Hurst, Luoh, and Stafford 1998;Vissing-Jorgensen 2002). In Section V, we will further investigatethe microeconomic and portfolio determinants of entry and exit.

In studying rebalancing in the next two sections, we focus ineach year on the large group of households that maintain par-ticipation in risky asset markets throughout the year. Between


1999 and 2002, the equal-weighted average risky share wh,t ofthese households fell from 57% to 45% (Table I, Panel B). As illus-trated in Figure IA, this lower mean reflects a downward shift inthe cross-sectional distribution of wh,t, which is most pronouncedin the tails.

The downward shift in the risky share translates into a down-ward shift in complete portfolio risk. We illustrate in Figure IBhow the standard deviation of the complete portfolio, wh,tσh,t,

varies with the risky share, wh,t. The relation is almost linearand has similar slopes in all years. Consistent with this finding,we verify in the Appendix that the standard deviation of the riskyportfolio, σh,t, has a stable cross-sectional distribution over timeand is almost a flat function of the risky share.3

These results imply that Swedish households adjust theiroverall risk exposure primarily by scaling up or down their riskyportfolios, passively or actively, rather by altering their composi-tion. This justifies our emphasis on modeling wh,t in the next twosections.

III. PASSIVE AND ACTIVE REBALANCING OF THE RISKY SHARE

III.A. Decomposition of the Risky Share

The change in a household’s risky share is partly determinedby the household’s active trades and partly by the returns on itsrisky securities. For instance, the risky share tends to fall me-chanically in a severe bear market. For this reason, we now de-compose the change in the risky share between year t and yeart + 1, wh,t+1 − wh,t, into a passive change driven by the returnson risky assets and an active change resulting from householdrebalancing decisions. This decomposition is empirically mean-ingful because of the comprehensive individual asset informationavailable in our data set.

The passive risky return 1 + rh,t+1 is the proportional changein value of a household’s risky portfolio if the household does nottrade risky assets during the year. It is easily computed fromthe initial risky portfolio and asset returns. Let w∗

h, j,t denotethe share of asset j (1 ≤ j ≤ J) in the risky portfolio. If the in-vestor does not trade between date t and date t + 1, the risky

3. Of course, the stability of average σh,t across risky share bins does not implythat all households own the same risky portfolio. We will indeed show in SectionIII that there is substantial heterogeneity in individual portfolio returns.


FIG

UR

EI

Por

tfol

ioC

har

acte

rist

ics

ofP

arti

cipa

nts

Not

es.P

anel

Ail

lust

rate

sth

ecr

oss-

sect

ion

aldi

stri

buti

onof

the

risk

ysh

are

wh,

tat

the

end

ofye

ars

1999

to20

02.P

anel

Bre

port

sth

ere

lati

onbe

twee

nth

est

anda

rdde

viat

ion

ofth

eco

mpl

ete

port

foli

ow

h,tσ

h,t

and

the

risk

ysh

are

wh,

t.


portfolio value at t + 1 is its value at t times its gross return1 + rh,t+1 = ∑J

j=1 w∗h, j,t(1 + rj,t+1). We compute returns rj,t+1 ex-

cluding dividends, as is appropriate if households consume divi-dends rather than reinvesting them, but we verify in the Appendixthat our empirical results are essentially unchanged when divi-dends are included in returns.

The passive risky share is the risky share at the end of theyear if the household does not trade risky assets during the year.It is a function of the initial risky share and the passive riskyreturn,

wph,t+1 = ωp(wh,t; rh,t+1),

where

ωp(w; r) ≡ w(1 + r)w(1 + r) + (1 − w)(1 + r f )

.

The passive change is the change in the risky share duringthe year if the household trades no risky assets during the year:

Ph,t+1 = wph,t+1 − wh,t.

It is equal to zero if the investor is initially invested exclusively incash (wh,t = 0) or exclusively in risky assets (wh,t = 1). The passivechange is a hump-shaped function of the initial share if r > r f , asinvestors presumably expect, but a U-shaped function of the initialshare if r < r f , as in our data from the bear market of 2000–2002.

The active change in the risky share, Ah,t+1 = wh,t+1 − wph,t+1,

is the movement in the risky share that does not result mechani-cally from realized returns and thus reflects portfolio rebalancing.The total change in the risky share can be written as the sum ofthe active and passive changes:

wh,t+1 − wh,t = Ph,t+1 + Ah,t+1.

We will also use the analogous decomposition in logs,

ln(wh,t+1) − ln(wh,t) = ph,t+1 + ah,t+1,

where ph,t+1= ln(wph,t+1)− ln(wh,t) and ah,t+1= ln(wh,t+1)− ln(wp

h,t+1)respectively denote the passive and active changes in logs.

These decompositions treat changes in riskless asset hold-ings, caused by saving, dissaving, or dividends received on riskyassets, as active rebalancing. An alternative approach would be to


calculate the passive risky share that would result from householdsaving or dissaving, assumed to take place through accumulationor decumulation of riskless assets, in the absence of any tradesin risky assets. This alternative decomposition is attractive to theextent that households build up and run down their riskless bal-ances for liquidity reasons that are unrelated to their investmentpolicies. We do not pursue this alternative decomposition furtherhere, but in our structural model of active rebalancing we do allowfor a white noise error that may capture high-frequency savingseffects.

III.B. Rebalancing Regressions

Changes in a household’s risky share tend to be strongly af-fected by the initial level of the risky share. One reason for thisis purely mechanical: the total change wh,t+1 − wh,t is bounded be-tween −wh,t and 1 − wh,t in the presence of short sales and leverageconstraints. In addition, there may be behavioral reasons, includ-ing sluggish rebalancing and high-frequency variation in risklessbalances, that the risky share may be subject to transitory shocksand gradual reversion to a long-term mean.

The scatterplots in Figure II show the passive, active, andtotal changes in levels between 2001 and 2002 versus the initialrisky share for a subsample of 10,000 households. In Panel A, thepassive change is a U-shaped function of the initial share, as oneexpects in a bear market. Panel B reveals that the active changeis close to zero for a sizeable group of households, who trade verylittle or not at all during the year. This observation is consistentwith the inertia documented on other data sets. There is, however,considerable heterogeneity, and substantially positive or negativevalues of the active change are observed for many households.Moreover, the active change appears to decrease with the initialshare, which suggests that the risky share tends to revert towardits cross-sectional mean. In Panel C, the total change is containedin the band defined by −wh,t and 1 − wh,t. The U-shaped influenceof passive change is apparent, consistent with the inertia of somehouseholds. Overall, the scatterplots reveal substantial hetero-geneity and strong dependence with respect to the initial riskyshare wh,t.

The effects of passive change and initial portfolio weighton active change are clearly visible when we group householdsinto bins according to their initial portfolio weight and plot the


FIGURE IIScatterplots of Risky Share

Notes. The scatterplots illustrate the passive change (Panel A), active change(Panel B), and total change (Panel C) versus the initial risky share for a randomsample of 10,000 households. All changes are computed in levels between 2001and 2002 and are expressed in natural units.

equal-weighted average within each bin of the total change, thepassive change and the active change. Figure III shows the re-sults for the entire period 2000–2002 (Panel A) and for each yearseparately (Panels B–D). Because of the bear market during oursample period, the passive change is a U-shaped function of theinitial exposure wh,t. Active rebalancing is hump-shaped and over-all decreasing with the initial share, which is consistent with bothmean reversion in the risky share and a tendency to offset passivechanges.

In Table II, we investigate the household-level relation be-tween active and passive changes by estimating a rebalancingregression. We compare results in levels and in logs, but write therebalancing regression here in logs,

ah,t+1 = γ0,t+1 + γ1 ph,t+1 + γ2(ln wh,t − ln wh,t) + uh,t+1,(1)

where ln wh,t denotes the equal-weighted average of the log riskyshare. The regressor (ln wh,t − ln wh,t) is included to capture thedependence of the active change on the initial risky share that


FIGURE IIIRisky Share Decomposition

Notes. The figure illustrates the equal-weighted average of the total change,active change, and passive change in bins of the initial risky share. The changesare reported in percentages and computed in levels over the entire period (PanelA) and over each year of the sample (Panels B–D).

was illustrated in Figure II. We estimate (1) by OLS, both forpooled data, with year fixed effects, and for each separate crosssection. We include in the regression only households that partici-pate both in year t and in year t + 1. This allows us to disentangleinframarginal rebalancing from entry and exit decisions.

A fully passive household would be characterized by zero re-gression coefficients: γ0,t+1 = γ1 = γ2 = 0. The estimates of γ1 arein fact close to −0.5 in the pooled regressions, and range between−0.8 and −0.4 in yearly cross sections. Thus we find that house-holds offset about one-half of the passive change through activerebalancing. We obtain negative estimates of γ2, implying thathouseholds with a large initial risky share have reduced theirrisk exposure more aggressively than other investors.

To understand the parameter estimates in Table II we now ex-amine a few numerical examples. Consider a household investedin the value-weighted average household portfolio, with an ini-tial share equal to the average equal-weighted share at the endof 2001: wh,t = 52.3%. In 2002, the average household portfolio

316 QUARTERLY JOURNAL OF ECONOMICST

AB

LE

IIR

EG

RE

SS

ION

OF

AC

TIV

EC

HA

NG

EO

NP

AS

SIV

EC

HA

NG

E

All

year

s20

0020

0120

02

Est

imat

et-

stat

Est

imat

et-

stat

Est

imat

et-

stat

Est

imat

et-

stat

A.I

nle

vels

Pas

sive

chan

ge−0

.504

−53.

10−0

.640

−27.

90−0

.608

−28.

70−0

.443

−35.

50In

itia

lris

kysh

are

(dem

ean

ed)

−0.1

86−1

44.0

0−0

.194

−88.

10−0

.176

−82.

40−0

.189

−78.

70In

terc

ept

0.02

633

.40

−0.0

30−3

4.10

−0.0

22−1

8.20

1999

dum

my

0.02

840

.40

2000

dum

my

−0.0

27−3

8.10

2001

dum

my

−0.0

27−2

6.70

Nu

mbe

rof

obse

rvat

ion

s18

7,78

060

,341

64,1

1963

,320

Adj

ust

edR

20.

120.

120.

100.

10

B.I

nlo

gsP

assi

vech

ange

inlo

gs−0

.442

−45.

40−0

.745

−35.

80−0

.534

−24.

30−0

.423

−30.

40L

ogin

itia

lris

kysh

are

(dem

ean

ed)

−0.1

99−1

37.0

0−0

.275

−123

.00

−0.1

52−6

2.90

−0.1

61−5

6.40

Inte

rcep

t0.

098

37.5

0−0

.067

−22.

50−0

.066

−15.

0019

99du

mm

y0.

112

42.8

020

00du

mm

y−0

.060

−22.

7020

01du

mm

y−0

.071

−20.

30N

um

ber

ofob

serv

atio

ns

187,

780

60,3

4164

,119

63,3

20A

dju

sted

R2

0.14

0.24

0.09

0.10

Not

es.T

his

tabl

ere

port

sth

epo

oled

and

year

lyre

gres

sion

sof

the

acti

vech

ange

onth

epa

ssiv

ech

ange

,bot

hin

leve

ls(P

anel

A)

and

inlo

gs(P

anel

B).

Year

tre

gres

sion

sin

clu

deh

ouse

hol

dsth

atpa

rtic

ipat

ein

risk

yas

set

mar

kets

both

atth

een

dof

year

t−1

and

atth

een

dof

year

t.P

oole

dre

gres

sion

sar

eba

sed

onal

lth

eda

tapo

ints

use

din

the

year

lyre

gres

sion

s,th

atis

,on

hou

seh

olds

that

part

icip

ate

intw

oco

nse

cuti

veye

ars.


yields −32.1%, and the household’s corresponding passive shareis 41.7%. We infer from the pooled regression reported in Table II,Panel A, that the predicted active change equals 2.4%.

Now consider an unlucky household with the same initialshare but with a realized risky return of −55.1%. Among all par-ticipants, the household is in the 5th percentile of the risky returndistribution. The corresponding passive change is −20.0% and thepredicted active change then equals 7.1%. Alternatively, considera lucky household with the same initial share but a realized riskyreturn in the 95th percentile (rh,t = −7.3%). The active change isthen −1.4%. Agents with returns below the cross-sectional aver-age buy risky assets from agents with higher returns. An agentwith an average share and an average return, on the other hand,makes fewer trades. The intuition that extreme agents trade morethan average agents is familiar in equilibrium models.4

The rebalancing regression also predicts the effect of the ini-tial share. For instance, a household that has an initial share inthe 5th percentile (wh,t = 3.9%) and owns the value-weighted av-erage household portfolio will select an active change of 6.2%.Similarly, a household with an initial share in the 95th per-centile (wh,t = 95.5%) with an average portfolio will select an ac-tive change of −10.8%. Thus, the initial share and the realizedreturn both have substantial quantitative effects on the activechange.

In the online Appendix, we verify the robustness of the rebal-ancing regression by classifying households into initial risky sharebins and regressing the active change onto the passive changewithin each bin. The estimate of the slope coefficient γ1 is stronglyand significantly negative in each risky share bin, so householdswith high and low involvement in risky financial markets all ap-pear to be rebalancing actively.

The rebalancing coefficient is unusually large and negative inthe lowest risky share bin, particularly when we run the regres-sion in levels. This likely results in part from a boundary effect.During the bear market, most households with low initial expo-sure (wh,t ≈ 0) incur small passive losses in levels, which bringthem closer to the short sales constraint wh,t+1 ≥ 0. Such house-holds may substantially increase their risky shares, which willbe associated with strongly positive active changes. Very negative

4. See Calvet, Gonzalez-Eiras, and Sodini (2004) for an example based onidiosyncratic nontradable risk.


active changes, on the other hand, are infeasible in levels (but arestill in principle feasible in logs). The levels regression in the low-est risky share bin is therefore driven by observations with smallnegative passive and large positive active changes, resulting ina very negative slope coefficient γ1. The coefficient in the lowestrisky share bin is somewhat less anomalous when we run the re-gression in logs, which encourages us to use a log specification inour subsequent analysis.

III.C. Robustness Checks

Churning. One might worry that households do not delib-erately rebalance the risky share, but instead randomly buy andsell risky assets—that is, churn their portfolios. Portfolio turnovercauses measurement error in the passive share, so churning bi-ases the regression coefficient of the active change on the passivechange toward −1. In this case, our results tell us that there isactive trading, but are not informative about deliberate rebalanc-ing. A simple robustness check consists of confining attention tohouseholds that do not purchase new risky assets during the year.In other words, we exclude any household that has strictly posi-tive holdings in period t + 1 of a risky security that it did not ownat all in period t. In the Appendix, we report that the correspond-ing rebalancing propensity is about −0.3, which is weaker thanthe estimates in Table II but still substantial.5 These estimatesare conservative because they exclude households that purchasenew assets as part of an active rebalancing strategy. This analysissuggests that churning alone cannot explain the strongly negativeestimates of the passive change coefficient reported in our rebal-ancing regressions.

Automatic Investment Plans. Automatic investment plansare another source of apparent rebalancing. Consider a house-hold that invests a fixed monetary amount in a basket of riskyassets every year and makes no other trades. The active changeis then a decreasing function of the risky portfolio’s performance,while the passive change increases with performance. Automaticinvestment schemes can therefore generate a negative correlationbetween active and passive changes.

5. The Appendix also estimates the adjustment model of the next sectionfor households that purchase no new assets. The estimated adjustment speed isactually slightly higher for these households.


Automatic investment plans typically imply the purchase ofthe same assets every year. We have already found, in our robust-ness check for churning, that households that purchase no newassets have a weaker rebalancing propensity, suggesting that au-tomatic investment plans cannot be driving our results. Further, ahousehold that only trades automatically will neither buy nor sellassets and will therefore own the same set of assets at the end ofyears t and t + 1. We show in the Appendix that such householdsalso have rebalancing propensities that are slightly lower thanthose reported in Table II. It is thus very unlikely that automaticinvestments account for our results.

We complement this analysis with a regression on a sim-ulated data set of automatic savers. The automatic investmentis assumed to be an exogenous percentage s of initial financialwealth. We set s equal to the average ratio in each year of sav-ings to financial wealth for households purchasing no new assetsduring the year. In the Appendix, we regress the implied activechange on the passive change and obtain only modest rebalancingpropensities. Results are similar when we set s equal to a constant3%, close to the time-series average of the yearly s ratios used inour main approach.

Cash Balances. Random fluctuations in cash balances are an-other concern. In the next section we develop a partial adjustmentmodel that allows high-frequency shocks to affect the target riskyshare. In the Appendix, we instead use a bootstrap simulationto investigate this issue. Specifically, we assume that householdsdo not rebalance or trade risky assets during the year and thattheir cash balances cbh,t follow the process cbh,t+1 = Rcb

h,t+1cbh,t.

The shocks Rcbh,t+1 are i.i.d. across households and sampled from

the empirical cross-sectional distribution of the growth rates ofcash balances. The simulation generates only modest rebalanc-ing propensities, which shows that random fluctuations in cashbalances cannot explain the rebalancing results of this section.

IV. AN ADJUSTMENT MODEL OF THE RISKY SHARE

IV.A. Specification

The regressions of Section III have shown that rebalancing isinfluenced by both the initial share and the passive change. Thismotivates us to specify a model in which households sluggishly


adjust their portfolios toward a desired risky share wdh,t+1. Our

approach is based on three main assumptions.First, the natural log of the observed risky share wh,t+1 is

a weighted average of the log desired share wdh,t+1 and the log

passive share wph,t+1:

ln(wh,t+1) = φh ln(wd

h,t+1

) + (1 − φh) ln(w

ph,t+1

) + εh,t+1.(2)

The error εh,t+1 is assumed to be i.i.d., resulting from measure-ment error, high-frequency variations in riskless balances, andany other idiosyncratic factors influencing portfolio composition.The coefficient φh controls the household’s speed of adjustment. Itcan take any real value, but values between zero and one are eco-nomically most sensible. If φh = 1, the household adjusts instan-taneously, and the observed share is equal to the desired shareplus an error: ln(wh,t+1) = ln(wd

h,t+1) + εh,t+1. A sluggish house-hold (φh < 1), on the other hand, is also sensitive to the passiveshare.

Second, the speed of adjustment coefficient φh is a linear func-tion of observable characteristics,

φh = ϕ0 + ϕ′xh,t,(3)

where the vector xh,t is independent of the errors εh,t and εh,t+1.

This specification captures the empirical relation between speedof adjustment and measures of financial sophistication such aswealth and education.

Third, the level of the log desired risky share can vary in anarbitrary manner across households, but the change in the logdesired risky share is related to household characteristics by

� ln(wd

h,t+1

) = δ0,t+1 + δ′t+1xh,t.(4)

This equation has a convenient interpretation when householdshave constant relative risk aversion γh and returns on the riskyasset are i.i.d. The desired risky share of household h is thenwd

h,t+1 = Sh,t+1/(γhσh,t+1), and changes in the log of the desiredrisky share, � ln(wd

h,t+1) = � ln(Sh,t+1/σh,t+1), are driven only byperceived variations in investment opportunities and not by riskaversion. Equation (4) can then be viewed as expressing changesin perceived investment opportunities as a time-specific functionof an intercept and household characteristics. This convenientproperty does not hold in levels, which supports our choice of a


log specification. In practice, many other factors can, of course,alter the desired risky share, including changes in real estateholdings, human capital, background risk, or wealth if the agenthas decreasing relative risk aversion.

IV.B. Estimation

We now turn to the estimation of the adjustment model. In ourspecification, a household’s target share ln(wd

h,t+1) is not observed,but its change � ln(wd

h,t+1) is a parametric function of characteris-tics (4). The first step is therefore to difference the portfolio share(2):

� ln(wh,t+1

) = φh� ln(wd

h,t+1

) + (1 − φh)� ln(wph,t+1) + εh,t+1 − εh,t.

We then substitute out φh and � ln(wdh,t+1), using (3) and (4), and

obtain the reduced-form specification

� ln(wh,t+1) = at+1 + b0� ln(w

ph,t+1

) + b′xh,t� ln(w

ph,t+1

)

+ c′t+1xh,t + x′

h,t Dt+1xh,t + εh,t+1 − εh,t.(5)

The reduced-form coefficients relate as follows to the structuralparameters of the adjustment model: at+1 = ϕ0δ0,t+1, b0 = 1 − ϕ0,

b = −ϕ, ct+1 = δt+1ϕ0 + δ0,t+1ϕ, and Dt+1 = ϕδ′t+1.

The error term of the reduced-form specification (5), εh,t+1 −εh,t, follows a first-order moving average process. A high real-ization of εh,t feeds into a high share wh,t and a high passiveshare w

ph,t+1, which implies that the error εh,t+1 − εh,t and the

regressor � ln(wph,t+1) = ln ωp(wh,t; rh,t+1) − ln(wp

h,t) are negativelycorrelated. Because of this problem, (5) cannot be consistently esti-mated by ordinary least squares (OLS). We handle this by findinga set of instruments that are correlated with the explanatory vari-ables in (5) but uncorrelated with the error term. The period-(t + 1) zero-rebalancing passive change,

ln ωp(wph,t; rh,t+1

) − ln(w

ph,t

),

is the passive change that would be observed at t + 1 if the house-hold did not rebalance at t. Because rebalancing is limited, thisvariable should be positively correlated with the actual change inthe passive share. At the same time, if the return rh,t+1 is inde-pendent of the errors, the period-(t + 1) zero-rebalancing passivechange is uncorrelated with εh,t and can therefore be used in a setof instruments.


TABLE IIIADJUSTMENT MODEL WITHOUT CHARACTERISTICS

OLS IV

Estimate t-stat Estimate t-stat

Reduced-form parametersChange in log passive share −0.121 −44.20 0.363 39.30Intercept 2001 −0.111 −42.00 −0.139 −46.30Intercept 2002 −0.246 −92.10 −0.119 −31.50

Structural parametersAdjustment speed ϕ0 1.121 410.00 0.637 68.90Target change δ0,2001 −0.099 −41.50 −0.219 −35.60Target change δ0,2002 −0.219 −95.60 −0.186 −39.90

Number of observations 120,067 120,067Adjusted R2 0.08

Notes. This table reports the IV and OLS estimates of the adjustment model without characteristics. Theestimation is based on households that participate in risky asset markets at the end of two consecutive years.

In Table III we estimate the adjustment model for the specialcase where all households have the same adjustment speed andtarget change, which corresponds to the restriction ϕ = δ = 0. TheOLS regression of the total log change in the risky share on thepassive log change and a time fixed effect,

� ln(wh,t+1) = at+1 + b0� ln(w

ph,t+1

) + uh,t+1,

gives an estimate for b0 of −0.12. This estimate is negatively bi-ased, because the regressor � ln(wp

h,t+1) and the residual uh,t+1are negatively correlated. When we correct the bias by runningan instrumental variables (IV) estimation with an intercept, thelog of the initial passive share, and the zero-rebalancing passivechange as instruments, we obtain a considerably higher b0 esti-mate of 0.36. The difference is economically meaningful. The OLSestimate implies an adjustment speed ϕ0 = 1 − b0 that is largerthan unity, whereas the IV estimate implies an adjustment speedϕ0 = 0.64, which is broadly consistent with the rebalancing re-gressions in Section III.

The next step is to estimate the adjustment model, allow-ing observable household characteristics to affect the adjustmentspeed and the change in the desired log risky share. The setof characteristics includes demographic, financial, and portfoliocharacteristics. The first category includes age, household size,and dummies for households that have high-school education,


post-high-school education, or missing education data (most com-mon among older and immigrant households) or are immigrants.The second category includes disposable income, contributions toprivate pension plans as a fraction of a three-year average of dis-posable income, log financial wealth, log real estate wealth, log oftotal debt liabilities, and dummies for households that are retired,unemployed, self-employed (“entrepreneurs”), and students. Thethird category, which is unique to our data set, includes the stan-dard deviation of the risky portfolio and the Sharpe ratio of therisky portfolio.

The inclusion of financial wealth Fht as a household charac-teristic creates a new difficulty. Financial wealth depends on therandom cash balance observed at the end of year t, and is thereforecorrelated with the measurement error εh,t. A natural solution isto use as an instrument passive financial wealth Fh,t−1(1 + rc

h,t),where 1 + rc

h,t ≡ wh,t−1(1 + rh,t) + (1 − wh,t−1)(1 + r f ) denotes thegross passive return on the complete portfolio. This leads us todefine the following set of instruments: (1) an intercept; (2) theperiod-t log passive share; (3) the period-(t + 1) zero-rebalancingpassive change; (4) the period-t passive financial wealth; (5) a vec-tor of characteristics uncorrelated with the measurement error;and (6) the log passive share, zero-rebalancing passive change,passive financial wealth, and characteristics interacted with thevector of characteristics.

To simplify the estimation of the full model (5), given that wehave a short panel with only two years of data after differencing,we estimate the reduced form assuming constant coefficients overtime on the household characteristics: ct+1 = c, and Dt+1 = D. Thestructural restrictions of the model then imply that the time fixedeffects at+1 are also constant, as are the structural coefficients inthe equation for the change in the desired risky share, (4): δ0,t+1 =δ0 and δt+1 = δ. We do not impose constant time fixed effects butfind them to be almost identical in 2001 and 2002.

IV estimates of this model are reported in the first set ofcolumns of Table IV. Each characteristic is standardized to havezero cross-sectional mean. We also normalize to unity the cross-sectional standard deviation of each continuous characteristic,so the reported regression coefficient reveals the effect of a one-standard-deviation change in the characteristic. To keep the tableat a manageable size, we only report the structural vectors ϕ andδ of the full regression. The median value of the speed param-eter is 0.73 and the average log target change is −0.18 in 2002.

324 QUARTERLY JOURNAL OF ECONOMICST

AB

LE

IVA

DJU

ST

ME

NT

MO

DE

LW

ITH

CH

AR

AC

TE

RIS

TIC

S

Spe

edT

arge

tch

ange

Spe

edT

arge

tch

ange

Est

imat

et-

stat

Est

imat

et-

stat

Est

imat

et-

stat

Est

imat

et-

stat

A.P

aram

eter

esti

mat

esIn

terc

epts

Adj

ust

men

tsp

eed

ϕ0

0.71

869

.80

0.74

168

.60

Tar

get

chan

geδ 0

.200

1−0

.154

−8.3

0−0

.121

−6.2

1T

arge

tch

ange

δ 0.2

002

−0.1

59−8

.59

−0.1

36−6

.96

Por

tfol

ioch

arac

teri

stic

sS

tan

dard

devi

atio

nof

risk

ypo

rtfo

lio

0.07

52.

29−0

.379

−8.8

80.

080

2.29

−0.2

59−5

.25

Sh

arpe

rati

oof

risk

ypo

rtfo

lio

1.14

79.

23−0

.538

−6.1

81.

213

9.23

−0.5

10−5

.75

Fin

anci

alch

arac

teri

stic

sD

ispo

sabl

ein

com

e0.

010

2.18

0.01

20.

670.

007

1.36

−0.0

24−1

.27

Pri

vate

pen

sion

prem

ia/in

com

e−0

.009

−1.6

1−0

.008

−1.3

4−0

.010

−1.6

3−0

.012

−1.9

5L

ogfi

nan

cial

wea

lth

0.04

03.

370.

052

10.7

00.

052

4.12

0.09

310

.60

Ch

ange

inlo

gfi

nan

cial

wea

lth

0.27

75.

76L

ogre

ales

tate

wea

lth

0.00

70.

83−0

.081

−6.3

70.

009

0.96

−0.1

25−8

.13

Log

tota

llia

bili

ty0.

031

2.64

0.02

32.

380.

032

2.60

0.04

13.

99R

etir

eddu

mm

y−0

.078

−2.1

60.

019

0.43

−0.0

87−2

.29

0.01

70.

37U

nem

ploy

men

tdu

mm

y−0

.055

−2.0

00.

051

1.00

−0.0

66−2

.27

0.05

81.

12E

ntr

epre

neu

rdu

mm

y−0

.069

−1.8

10.

001

0.04

−0.0

77−1

.91

−0.0

05−0

.23

Stu

den

tdu

mm

y−0

.051

−0.9

60.

077

0.60

−0.0

45−0

.81

0.05

50.

42


TA

BL

EIV

(CO

NT

INU

ED

)

Spe

edT

arge

tch

ange

Spe

edT

arge

tch

ange

Est

imat

et-

stat

Est

imat

et-

stat

Est

imat

et-

stat

Est

imat

et-

stat

Dem

ogra

phic

char

acte

rist

ics

Age

0.00

11.

75−0

.002

−4.4

90.

002

2.21

−0.0

02−3

.99

Hou

seh

old

size

−0.0

14−2

.37

−0.0

17−3

.59

−0.0

17−2

.70

−0.0

13−2

.77

Hig

h-s

choo

ldu

mm

y0.

086

3.80

−0.0

03−0

.29

0.08

83.

68−0

.009

−0.9

6P

ost-

hig

h-s

choo

ldu

mm

y0.

042

2.48

−0.0

02−0

.26

0.04

72.

66−0

.008

−0.9

3D

um

my

for

un

avai

labl

eed

uca

tion

data

0.09

31.

980.

111

1.84

0.10

72.

170.

115

1.87

Imm

igra

tion

dum

my

0.00

00.

02−0

.021

−2.1

40.

002

0.08

−0.0

24−2

.37

B.C

ross

-sec

tion

ald

istr

ibu

tion

(200

1)5t

hpe

rcen

tile

0.53

2−0

.286

0.53

8−0

.368

25th

perc

enti

le0.

662

−0.2

260.

679

−0.2

3050

thpe

rcen

tile

0.72

9−0

.178

0.75

3−0

.145

75th

perc

enti

le0.

787

−0.0

920.

818

−0.0

2595

thpe

rcen

tile

0.86

20.

046

0.90

20.

197

Not

es.T

his

tabl

ere

port

sth

ees

tim

ates

ofth

ead

just

men

tm

odel

(Pan

elA

)an

dth

eim

plie

dcr

oss-

sect

ion

aldi

stri

buti

onof

the

spee

dpa

ram

eter

and

targ

etch

ange

in20

01(P

anel

B).

Th

ees

tim

atio

nis

base

don

hou

seh

olds

that

part

icip

ate

inri

sky

asse

tm

arke

tsat

the

end

oftw

oco

nse

cuti

veye

ars

and

for

wh

ich

the

imm

igra

tion

dum

my

isav

aila

ble.

Dis

posa

ble

inco

me

isav

erag

edov

erth

epr

evio

us

thre

eye

ars.

All

char

acte

rist

ics

are

dem

ean

edye

arby

year

,an

dco

nti

nu

ous

fin

anci

alch

arac

teri

stic

sot

her

than

the

chan

gein

fin

anci

alw

ealt

har

en

orm

aliz

edto

hav

eu

nit

stan

dard

devi

atio

nin

each

cros

sse

ctio

n.P

ortf

olio

char

acte

rist

ics

are

inn

atu

ralu

nit

s.


These numbers are consistent with the rebalancing regressions inSection III and imply a modest revision in the target share. Theestimates of ϕ and δ are robust to the inclusion or exclusion of thenonlinear interacted term x′

h,t Dxh,t.

The most striking result in Table IV is that the speed of ad-justment tends to increase with variables associated with finan-cial sophistication, such as education, disposable income, debt,and financial and real estate wealth. In addition, householdswith well-diversified portfolios (high Sharpe ratios) have higheradjustment speeds. Entrepreneurial activity, retirement, unem-ployment, and household size, on the other hand, reduce the ad-justment speed.

Changes in the target risky share are less consistently re-lated to household characteristics. Households with greater fi-nancial wealth reduce their target less. Real estate wealth, on theother hand, works in the opposite direction, perhaps because thebooming Swedish real estate market reduced the attractivenessof stocks to real-estate-oriented investors in 2001 and 2002. Well-diversified and aggressive households, with higher Sharpe ratiosand riskier portfolios, reduce their target share more; but so dolarger, older, and immigrant households, which normally investcautiously (CCS 2007).

The estimates in Table IV can be used to compute the pre-dicted speed parameter and target change of every household.The cross-sectional distribution of the speed φh has a medianvalue of 0.73, a 5th percentile of 0.53, and a 95th percentile of0.86, numbers that seem quite reasonable. Similarly, the targetchange � ln(wd

h,t+1) has a 5th percentile of −29%, a median valueof −18%, and a 95th percentile of 5%, which again seem quitereasonable in a severe bear market.

In the Appendix, we report the average characteristic valuesfor households at different percentiles of the predicted adjustmentspeed and target change distributions. The average household atthe 5th percentile of adjustment speed has disposable income ofabout $20,700 and wealth of $10,500, 47% of which is invested inrisky assets with a Sharpe ratio of 0.22. Of these slowly adjust-ing households, 35% have a high school education, and 6% have acollege education. At the 95th percentile of adjustment speed, bycontrast, the average household has disposable income of $42,800and wealth of $56,200, 70% of which is invested in risky assetswith a Sharpe ratio of 0.31. Of these rapidly adjusting house-holds, 95% have a high school education, and 71% have a college


education. In short, rapidly adjusting households appear to bemore financially sophisticated than slowly adjusting households.

The Appendix also reports results when we estimate the ad-justment model separately for the years 2001 and 2002, allowingboth adjustment speeds and target changes to vary across years.The results are qualitatively similar to those reported in Table IV,although adjustment speeds appear to be higher in 2001 than in2002, and some of the individual characteristics have coefficientsthat are unstable over time.

IV.C. Impact of Wealth Changes on Risk Taking

In the first set of columns of Table IV, we have used slowlyevolving demographic and financial variables as household char-acteristics that may influence portfolio adjustment. We now ex-plore the possibility that portfolio decisions are also affected byhigh-frequency variation in financial wealth. Several branchesof financial theory suggest that an increase in financial wealthmight increase the propensity to take risk. In the presence of bor-rowing constraints, individuals become less risk-averse as theybecome richer and thus less likely to face a binding constraintin the future. Even in the absence of such frictions, investorswith decreasing relative risk aversion tend to hold a higher riskyshare as their wealth goes up. Carroll (2000, 2002) builds sucha utility function and documents a positive empirical relationbetween household wealth and the risky share. Models of port-folio insurance (Brennan and Schwartz 1988) and habit forma-tion (Constantinides 1990; Dybvig 1995; Campbell and Cochrane1999) also imply a positive relation between wealth and the riskyshare, because richer agents are more willing to take risk astheir wealth moves further above the minimum level requiredby the portfolio insurance policy or the present value of the futurehabit.

In the second set of columns of Table IV, we reestimate theadjustment model using the contemporaneous change in log fi-nancial wealth as an additional household characteristic that canaffect the change in the target. We add one instrument to the pre-vious set, the zero-rebalancing return on the complete portfolio,that is, the return that each household would earn at t + 1 if itdid not rebalance at t. We find that an increase in log financialwealth leads to a higher target risky share, while the impact ofother characteristics is largely unchanged.


In the Appendix, we show that results are similar when weestimate the model separately for the years 2001 and 2002, al-though the effect of the change in log financial wealth on thetarget risky share is stronger in 2002. The Appendix also reportssimilar results when we allow the change in log financial wealthto affect the adjustment speed as well as the desired risky share.We find little variation in the change in log financial wealth acrossrapidly adjusting and slowly adjusting households, but large vari-ation across households with high and low predicted changes inthe target risky share. Households at the 5th percentile of the tar-get change experience average financial wealth declines of 44%,whereas households at the 95th percentile of the target changeexperience average financial wealth increases of 31%.

In a recent study, Brunnermeier and Nagel (2008) assess theempirical relation between wealth and the risky share using U.S.data from the Panel Study of Income Dynamics (PSID). Theyregress the change in the risky share (in levels) on the changein financial wealth (in logs) and control variables and obtain asmall and slightly negative wealth coefficient. They attribute thisresult to inertia. When a household saves in the form of cash dur-ing the year and only partially rebalances its financial portfolio,its risky share tends to fall mechanically.

In the Appendix, we estimate the Brunnermeier–Nagel re-gression on our Swedish data set. The OLS estimate of the regres-sion coefficient is slightly negative, consistent with Brunnermeierand Nagel’s finding; but it becomes positive when we estimate theregression by instrumental variables. Similarly, we find a positiverelation between the change in the risky share and the laggedchange in financial wealth. These results are robust to separateestimation of the regression in the years 2001 and 2002. Thus,controlling for endogeneity problems and household inertia, theSwedish data do show some evidence for a positive link betweenwealth changes and risk taking.

IV.D. Connection with Rebalancing Regressions

The adjustment model of this section is intimately related tothe rebalancing regressions we estimated in Section III. Whenthe adjustment speed and target changes are the same for allhouseholds, the adjustment model implies

(6) ah,t+1 = φδ0,t+1 − φph,t+1 − φ(

ln wh,t − ln wdh,t

) + εh,t+1,


which is analogous but not exactly identical to the rebalancingregression (1). The similarity suggests that the adjustment speedshould be close to the regression coefficients reported in Table II,Panel B; this is indeed the case in our empirical results becausethe median speed φh and the linear regression coefficient −γ1 areboth about one-half. The distance to the target, ln wh,t − ln wd

h,t,

represents the only difference between (1) and (6). We can there-fore view (1) as a reduced-form specification in which all house-holds have the same speed of adjustment and the distance to thetarget, ln wh,t − ln wd

h,t, is proxied by a rescaled distance to thecross-sectional mean, λ(ln wh,t − ln wh,t).

We show in the Appendix that the adjustment model withheterogeneous agents leads to additional terms in the rebalanc-ing regressions. Section IV thus improves on the specification inSection III by (1) providing more precise micro foundations; (2)correctly estimating the structural parameters even though thetarget is unobserved; and (3) allowing for heterogeneity in theadjustment speed and the target change.

In the Appendix, we investigate the role of household char-acteristics in the rebalancing regression (1). The reduction in therisky share is in all years less pronounced for households withhigher financial wealth or debt. These households were more will-ing to maintain their proportional investments in risky assetsthan were other Swedish households. We interact demographicvariables with passive changes and find that wealthier and moresophisticated households tend to have more negative regressioncoefficients of active changes on passive changes; that is, they re-balance more actively. These findings confirm the results of theadjustment model. They are limited, however, in that they do notallow us to disentangle household inertia from changes in thetarget risky share, as the adjustment model can do.

V. ASSET-CLASS PARTICIPATION, ASSET-LEVEL REBALANCING,AND THE DISPOSITION EFFECT

We now study household decisions to participate in assetclasses (risky assets, mutual funds, and direct stockholding) andto rebalance positions in individual assets. We relate these deci-sions to the disposition effect, the tendency to realize gains andhold on to losses, where gains and losses are measured relativeto the asset’s purchase price. The disposition effect was identified


by Shefrin and Statman (1985) and has been widely documented(e.g., Odean [1998]).

The Swedish data set unfortunately does not provide thedates or prices of individual asset trades. For this reason, we usean asset’s return during the year as an alternative measure ofperformance. This implies that we are assessing the robustness ofthe disposition effect to this alternative benchmark. The Swedishdata set has an important compensating advantage: we can relatethe disposition effect to demographic and financial characteristicsof households, as well as the characteristics of the portfolios theyhold. This allows us to extend the recent finding of Dhar and Zhu(2006) that households with higher self-reported income are lessprone to the disposition effect.

V.A. Entry and Exit at the Risky Portfolio and Asset Class Levels

We begin by investigating the probability that a householdenters or exits risky asset markets. In Table V, we run pooled pro-bit regressions of the form yh,t = �(x′

h,tγ + γ0,t), where yh,t denotesparticipation status, xh,t is the vector of characteristics, and γ0,t isa time-dependent intercept. The time interval is annual, and wepool data from 2000, 2001, and 2002. To facilitate comparison, wereport in the last three rows the rates of entry to and exit fromrisky asset markets in each year.

In the first set of three columns, we analyze the probabilitythat a nonparticipant in year t holds risky assets in year t + 1.The first two columns report coefficients and t-statistics, whereasthe third column assesses the economic magnitude of the coeffi-cient estimates by reporting the impact on the entry probabilityof increasing a nondummy regressor by one standard deviation,or setting a dummy variable equal to one. This impact dependson the base rate of entry; we report it for the year 2001, in whichthe overall entry rate was 5.7%, much lower than the rate in 2000and slightly higher than the rate in 2002.

The probability of entry increases with standard measuresof financial sophistication such as education, financial and realestate wealth, and the private pension savings rate. Conversely,the exit probability decreases with variables associated with in-vestor sophistication, as shown in the middle three columns ofTable V. The entry and exit regressions are thus remarkablysymmetric and consistent with earlier research on cross-sectionaldata. Household characteristics that predict participation in a


TA

BL

EV

EN

TR

YA

ND

EX

ITP

RO

BA

BIL

ITIE

S

En

try

Exi

t

Est

imat

et-

stat

Ch

ange

Est

imat

et-

stat

Ch

ange

Est

imat

et-

stat

Ch

ange

Por

tfol

ioch

arac

teri

stic

sIn

itia

lris

kysh

are

−0.5

71−2

4.70

−0.8

1%S

tan

dard

devi

atio

nof

risk

ypo

rtfo

lio

−0.2

78−5

.20

−0.2

0%D

um

my

for

risk

ysh

are

>95

%0.

066

1.82

0.09

%S

har

pera

tio

ofri

sky

port

foli

o−0

.461

−4.6

4−0

.16%

Gai

nof

risk

ypo

rtfo

lio

0.47

66.

270.

16%

Los

sof

risk

ypo

rtfo

lio

0.39

66.

400.

37%

Fin

anci

alch

arac

teri

stic

sD

ispo

sabl

ein

com

e0.

093

13.7

00.

88%

0.01

53.

330.

09%

0.01

43.

040.

08%

Pri

vate

pen

sion

prem

ia/in

com

e0.

029

5.86

0.26

%−0

.077

−6.3

8−0

.42%

−0.0

68−5

.71

−0.3

6%L

ogfi

nan

cial

wea

lth

0.22

129

.40

2.33

%−0

.356

−46.

60−1

.47%

−0.2

91−3

6.00

−1.2

1%L

ogre

ales

tate

wea

lth

0.10

415

.70

1.00

%−0

.069

−10.

70−0

.38%

−0.0

74−1

1.40

−0.3

8%L

ogto

tall

iabi

lity

0.07

18.

480.

66%

−0.0

26−3

.09

−0.1

5%−0

.017

−2.0

2−0

.09%

Ret

irem

ent

dum

my

0.00

0−0

.01

0.00

%0.

019

0.74

0.11

%0.

014

0.54

0.08

%U

nem

ploy

men

tdu

mm

y−0

.039

−2.3

1−0

.33%

0.09

34.

700.

60%

0.08

74.

320.

52%

En

trep

ren

eur

dum

my

0.03

61.

000.

33%

−0.0

31−0

.86

−0.1

8%−0

.066

−1.8

2−0

.34%

Stu

den

tdu

mm

y−0

.130

−4.3

6−1

.01%

0.16

44.

721.

12%

0.18

15.

161.

21%


TA

BL

EV

( CO

NT

INU

ED

)

En

try

Exi

t

Est

imat

et-

stat

Ch

ange

Est

imat

et-

stat

Ch

ange

Est

imat

et-

stat

Ch

ange

Dem

ogra

phic

char

acte

rist

ics

Age

−0.0

18−2

8.50

−2.1

8%0.

005

7.53

0.52

%0.

005

6.99

0.46

%H

ouse

hol

dsi

ze−0

.054

−9.2

4−0

.51%

0.01

52.

740.

12%

−0.0

12−2

.04

−0.0

8%H

igh

sch

oold

um

my

0.11

57.

271.

11%

−0.0

58−3

.47

−0.3

2%−0

.049

−2.9

0−0

.26%

Pos

t-h

igh

sch

oold

um

my

0.13

88.

901.

36%

−0.0

62−4

.01

−0.3

4%−0

.050

−3.1

7−0

.26%

Du

mm

yfo

ru

nav

aila

ble

edu

cati

onda

ta−0

.090

−3.1

2−0

.72%

−0.0

01−0

.02

0.00

%0.

002

0.06

0.01

%Im

mig

rati

ondu

mm

y−0

.095

−6.0

3−0

.76%

0.20

111

.80

1.43

%0.

197

11.4

01.

33%

Rat

eof

entr

y/ex

itin

2000

15.8

%1.

9%1.

9%R

ate

ofen

try/

exit

in20

015.

7%3.

4%3.

4%R

ate

ofen

try/

exit

in20

024.

4%3.

9%3.

9%

Not

es.T

he

tabl

ere

port

sth

epo

oled

prob

itre

gres

sion

ofa

hou

seh

old’

sen

try

toan

dex

itfr

omri

sky

asse

tm

arke

ts.E

xpla

nat

ory

vari

able

sin

clu

deye

arly

fixe

def

fect

san

dpo

rtfo

lio,

fin

anci

al,a

nd

dem

ogra

phic

char

acte

rist

ics.

Dis

posa

ble

inco

me

isav

erag

edov

erth

epr

evio

us

thre

eye

ars.

For

each

regr

essi

on,w

ere

port

the

lin

ear

coef

fici

ent,

t-st

atis

tic,

and

mar

gin

alef

fect

ofea

chpr

edic

tin

gva

riab

le.

All

char

acte

rist

ics

are

dem

ean

edye

arby

year

,an

dco

nti

nu

ous

fin

anci

alch

arac

teri

stic

sar

en

orm

aliz

edto

hav

eu

nit

stan

dard

devi

atio

nin

each

cros

sse

ctio

n.P

ortf

olio

char

acte

rist

ics

are

inn

atu

ralu

nit

s.T

he

mar

gin

alef

fect

isas

sess

edby

com

puti

ng

the

impa

cton

the

depe

nde

nt

vari

able

(in

leve

ls)

ofin

crea

sin

ga

non

dum

my

regr

esso

rby

one

stan

dard

devi

atio

n,o

rse

ttin

ga

dum

my

vari

able

equ

alto

one,

inye

ar20

01.W

eex

clu

deh

ouse

hol

dsfo

rw

hic

hth

eim

mig

rati

ondu

mm

yis

un

avai

labl

e.


cross section (e.g., Guiso, Haliassos, and Jappelli [2002]; CCS[2007]) also help to explain entry and exit in the panel.

In the last three columns of Table V, we investigate how theexit probability is affected by portfolio characteristics. The exitprobability decreases with the initial risky share and the stan-dard deviation of the risky portfolio. More aggressive agents havehigher risk tolerance and are less likely to give up the benefitsof financial risk taking entirely. A complementary explanation isthat households sluggishly adjust their portfolios, as in Section IV;more aggressive investors are then more reluctant to dispose ofall their risky assets. The exit probability, however, increases withextreme risk taking, as measured by a dummy equal to unity if therisky share exceeds 95%. Extreme risk takers presumably havelimited understanding of financial risk and may be prone to panicselling in a bear market.

A household is also more likely to exit if it is undiversified(low Sharpe ratio Sh,t). Finally, we consider the performance of therisky portfolio during the year and distinguish between positivereturns (winning portfolios) and negative returns (losing portfo-lios). Large absolute returns tend to increase the probability ofexit, and the effect is slightly more pronounced for winners thanfor losers.

We next investigate the separate roles of direct stock andmutual fund holdings. It is useful to decompose the overall Sharperatio into its stock and fund components. Let Dh,t denote the shareof direct stockholdings in the risky portfolio, SD,h,t and SF,h,t theSharpe ratios of the stock and fund portfolios, and σD,h,t and σF,h,ttheir standard deviations. The Sharpe ratio of the risky portfoliosatisfies

Sh,t = CD,h,t + CF,h,t,

where the components of the Sharpe ratio attributable to eachasset class are given by

CD,h,t = SD,h,tDh,tσD,h,t

σh,t,

CF,h,t = SF,h,t(1 − Dh,t)σF,h,t

σh,t.

The contribution of an asset class to the overall Sharpe ratio islarge if the household holds a diversified portfolio of assets in theclass, which represents a large fraction of its risk exposure.


In Table VI, we investigate exit decisions by asset class fora subset of households that initially own both stocks and mutualfunds. As in the previous tables, we include financial and demo-graphic variables and the risky portfolio’s standard deviation. Wealso consider the contributions of stocks and funds to the overallSharpe ratio and the performance during the year of the house-hold’s stocks and funds. These variables are used to explain theprobability that a participating household disposes of all its riskyassets (first three columns), all its directly held stocks (next threecolumns), or all its mutual funds (last three columns).

Most of the patterns in Table VI are consistent with those al-ready shown in Table V. The striking new result in Table VI is thatthe exit probability from risky asset markets increases stronglywith the performance of winning stock portfolios. Winning stockportfolio performance has an even greater impact on the probabil-ity of exiting direct stockholding, as shown in the second column ofthe table. These findings are consistent with the disposition effecton direct stockholdings: the owner of a winning stock portfolio ismore likely to sell it and leave financial markets. There is littleevidence in the table of a comparable disposition effect for mutualfunds. The owner of a winning mutual fund portfolio is slightlymore likely to liquidate it, but the effect is small and statisti-cally insignificant; losses have a larger effect on the mutual fundsales probability, consistent with the literature on performancesensitivity of mutual fund holdings (Ippolito 1992; Gruber 1996;Chevalier and Ellison 1997; Sirri and Tufano 1998; Frazzini andLamont 2008; Ivkovic and Weisbenner 2007).

V.B. Dynamics of Individual Asset Shares

We now investigate how a household adjusts during year t + 1the asset positions that it holds at the end of year t. Because exitdecisions have been addressed in Section V.A, we consider onlyhouseholds that own risky assets in both years. Also, we confineattention to assets that are held at the end of year t and do notattempt to model household decisions to purchase new assets.

A household owning a stock at the end of 2000 had, by the endof 2001, partially sold it with probability 27%, fully sold it withprobability 16%, held on to it with probability 22%, and boughtadditional units of the same stock with probability 35%. Partialsales and partial purchases are thus more frequent than full salesor continued holdings. As reported in the Appendix, this propertyholds in all years and is even more pronounced for mutual funds:


TA

BL

EV

IIM

PA

CT

OF

ST

OC

KA

ND

MU

TU

AL

FU

ND

HO

LD

ING

SO

NE

XIT

PR

OB

AB

ILIT

Y

Ris

kyas

sets

Sto

cks

Mu

tual

fun

ds

Est

imat

et-

stat

Ch

ange

Est

imat

et-

stat

Ch

ange

Est

imat

et-

stat

Ch

ange

Por

tfol

ioch

arac

teri

stic

sIn

itia

lris

kysh

are

−0.2

92−4

.11

−0.0

8%−0

.178

−4.7

0−0

.25%

−0.3

68−1

0.20

−0.4

8%S

tan

dard

devi

atio

nof

risk

ypo

rtfo

lio

0.17

01.

150.

02%

0.37

34.

620.

26%

0.38

24.

830.

25%

Sto

ckco

mpo

nen

tof

Sh

arpe

rati

o−1

.276

−2.8

8−0

.10%

−3.3

02−1

3.40

−1.1

7%−0

.279

−1.2

3−0

.12%

Fu

nd

com

pon

ent

ofS

har

pera

tio

−1.1

90−3

.42

−0.1

2%−0

.762

−4.0

8−0

.43%

−1.7

54−9

.53

−0.8

5%G

ain

ofst

ock

port

foli

o0.

268

2.57

0.04

%0.

694

13.2

00.

56%

−0.1

06−1

.20

−0.0

7%L

oss

ofst

ock

port

foli

o0.

080

0.80

0.02

%0.

015

0.29

0.02

%−0

.046

−0.8

4−0

.05%

Gai

nof

mu

tual

fun

dpo

rtfo

lio

0.15

70.

260.

00%

−0.3

38−0

.85

−0.0

5%0.

444

1.67

0.07

%L

oss

ofm

utu

alfu

nd

port

foli

o0.

436

1.84

0.07

%−0

.091

−0.6

7−0

.07%

0.26

32.

240.

20%

Fin

anci

alch

arac

teri

stic

sD

ispo

sabl

ein

com

e0.

014

1.78

0.02

%0.

012

1.62

0.07

%−0

.046

−0.8

40.

08%

Pri

vate

pen

sion

prem

ia/in

com

e−0

.136

−3.5

9−0

.13%

−0.0

33−2

.35

−0.1

9%0.

444

1.67

−0.3

9%L

ogfi

nan

cial

wea

lth

−0.1

69−7

.78

−0.1

5%−0

.208

−17.

80−0

.98%

0.26

32.

24−0

.75%

Log

real

esta

tew

ealt

h−0

.060

−3.3

1−0

.06%

−0.0

33−3

.26

−0.1

8%0.

014

2.30

−0.2

4%L

ogto

tall

iabi

lity

−0.0

26−1

.01

−0.0

3%0.

017

1.30

0.10

%−0

.076

−5.0

6−0

.04%

Ret

irem

ent

dum

my

0.04

30.

560.

05%

0.13

03.

280.

85%

−0.1

57−1

4.60

0.16

%U

nem

ploy

men

tdu

mm

y0.

196

3.05

0.29

%0.

003

0.07

0.02

%−0

.044

−4.6

10.

77%

En

trep

ren

eur

dum

my

−0.0

33−0

.33

−0.0

4%−0

.105

−2.0

5−0

.55%

−0.0

08−0

.61

−0.0

1%S

tude

nt

dum

my

0.15

51.

330.

22%

0.07

71.

150.

48%

0.02

80.

771.

77%


TA

BL

EV

I( C

ON

TIN

UE

D)

Ris

kyas

sets

Sto

cks

Mu

tual

fun

ds

Est

imat

et-

stat

Ch

ange

Est

imat

et-

stat

Ch

ange

Est

imat

et-

stat

Ch

ange

Dem

ogra

phic

char

acte

rist

ics

Age

0.00

20.

970.

04%

−0.0

03−2

.59

−0.2

3%0.

122

3.39

0.27

%H

ouse

hol

dsi

ze0.

027

1.58

0.04

%0.

040

4.72

0.31

%−0

.003

−0.0

6−0

.43%

Hig

hsc

hoo

ldu

mm

y−0

.012

−0.2

2−0

.01%

0.00

50.

180.

03%

0.24

93.

96−0

.19%

Pos

t-h

igh

sch

oold

um

my

0.01

40.

300.

02%

0.00

50.

230.

03%

0.00

32.

87−0

.26%

Du

mm

yfo

ru

nav

aila

ble

edu

cati

onda

ta0.

122

1.52

0.16

%0.

143

3.24

0.95

%−0

.066

−7.2

7−0

.22%

Imm

igra

tion

dum

my

0.14

92.

760.

21%

0.11

23.

730.

72%

−0.0

36−1

.31

0.91

%

Rat

eof

exit

in20

000.

3%2.

9%2.

2%R

ate

ofex

itin

2001

0.5%

3.2%

3.0%

Rat

eof

exit

in20

020.

6%2.

6%3.

3%

Not

es.T

he

tabl

ere

port

sa

pool

edpr

obit

regr

essi

onth

ata

hou

seh

old

sell

sal

lit

sri

sky

asse

ts(fi

rst

set

ofth

ree

colu

mn

s),a

llit

sst

ocks

(mid

dle

set

ofth

ree

colu

mn

s),a

nd

all

its

risk

ym

utu

alfu

nds

(las

tse

tof

thre

eco

lum

ns)

.We

incl

ude

year

lyfi

xed

effe

cts,

and

aver

age

out

disp

osab

lein

com

eov

erth

epr

evio

us

thre

eye

ars.

For

each

regr

essi

onw

ere

port

the

lin

ear

coef

fici

ent,

t-st

atis

tic,

and

mar

gin

alef

fect

ofea

chpr

edic

tin

gva

riab

le.A

llch

arac

teri

stic

sar

ede

mea

ned

year

byye

ar,a

nd

con

tin

uou

sfi

nan

cial

char

acte

rist

ics

are

nor

mal

ized

toh

ave

un

itst

anda

rdde

viat

ion

inea

chcr

oss

sect

ion

.Por

tfol

ioch

arac

teri

stic

sar

ein

nat

ura

lun

its.

Th

em

argi

nal

effe

ctis

asse

ssed

byco

mpu

tin

gth

eim

pact

onth

ede

pen

den

tva

riab

le(i

nle

vels

)ofi

ncr

easi

ng

an

ondu

mm

yre

gres

sor

byon

est

anda

rdde

viat

ion

,or

sett

ing

adu

mm

yva

riab

leeq

ual

toon

e,in

year

2001

.Th

ere

gres

sion

sar

eba

sed

onth

ese

tof

hou

seh

olds

that

hol

dbo

thst

ocks

and

fun

ds.W

eex

clu

deh

ouse

hol

dsfo

rw

hic

hth

eim

mig

rati

ondu

mm

yis

un

avai

labl

e.


the probability of a fund’s full sale is less than 10% whereas theprobability of a partial purchase is about 60%. The high proba-bility of partial purchases for funds is attributable in part to thedividend reinvestment plans offered by most investment compa-nies. Even though full sales are a relatively limited phenomenon,we begin by studying them separately given their importance inthe disposition effect literature.

Full Sales. In Table VII, we analyze the probability that ahousehold owning a risky asset j at date t has fully disposed ofit by the end of year t + 1. The regression distinguishes betweenstocks and funds, and between assets with positive returns (win-ners) and negative returns (losers) during the year. We includehousehold demographic and financial characteristics, and portfo-lio characteristics, both directly and interacted with the returnon the asset during the year.6 The table reports the coefficientson the characteristics interacted with return but omits the directcoefficients; the full regression is reported in the Appendix.

The probability of fully selling an asset increases with theabsolute value of its performance during the year. Householdsare more likely to fully sell extreme losers or extreme winnersthan stocks and funds that have close to zero returns. For stocks,this effect is much more pronounced for winners than for losers,which seems consistent with a disposition effect on direct stock-holdings. For funds, performance also impacts the probability of afull sale, but there is substantially less asymmetry between losersand winners. The tendency to sell losing funds is consistent withthe literature on performance sensitivity. For winning funds, how-ever, household behavior is both driven by the tendency to hold onto winners, for instance because they are run by skilled managers,but also a tendency to sell winning assets in order to rebalance therisky portfolio. The connection between asset trades and portfoliorebalancing will be further investigated in Section VI.

Financial and portfolio characteristics have a substantial im-pact on the probability of a full sale of stock. Wealthier householdswith riskier overall portfolios and better diversified stock portfo-lios are less likely to sell a winning stock but more likely to sell alosing stock. These effects reduce the asymmetry between winnersand losers and therefore weaken the disposition effect on direct

6. In Table VII, we assign a value of zero to the characteristic of an asset classif the household has no investments in this class. A similar convention is used inthe remainder of the section.


TA

BL

EV

IIP

RO

BA

BIL

ITY

OF

FU

LLY

SE

LL

ING

AN

AS

SE

T

Sto

cks

Fu

nds

Win

ner

sL

oser

sW

inn

ers

Los

ers

Est

imat

et-

stat

Est

imat

et-

stat

Est

imat

et-

stat

Est

imat

et-

stat

Gai

n/lo

ssof

asse

tj

0.92

846

.80

0.11

27.

000.

359

8.87

0.36

413

.70

Por

tfol

ioch

arac

teri

stic

sin

tera

cted

wit

hga

in/

loss

Init

ialr

isky

shar

e−0

.394

−4.1

2−0

.053

−0.8

4−0

.320

−1.9

8−0

.160

−1.7

7S

tan

dard

devi

atio

nof

risk

ypo

rtfo

lio

−0.6

56−4

.13

0.24

11.

880.

061

0.13

0.64

72.

49S

tock

com

pon

ent

ofS

har

pera

tio

−1.6

90−3

.40

1.68

64.

28−1

.703

−1.4

1−1

.281

−1.8

2F

un

dco

mpo

nen

tof

Sh

arpe

rati

o0.

359

0.78

0.84

22.

32−1

.496

−1.4

60.

387

0.67

Fin

anci

alch

arac

teri

stic

sin

tera

cted

wit

hga

in/

loss

Dis

posa

ble

inco

me

−0.0

07−0

.52

−0.0

24−2

.83

−0.0

18−0

.31

−0.0

66−2

.48

Pri

vate

pen

sion

prem

ia/in

com

e0.

003

0.19

−0.0

28−1

.65

0.05

71.

190.

004

0.07

Log

fin

anci

alw

ealt

h−0

.083

−3.5

50.

072

4.09

0.09

61.

870.

149

4.61

Log

real

esta

tew

ealt

h−0

.038

−1.8

0−0

.007

−0.4

1−0

.029

−0.7

20.

012

0.50

Log

tota

llia

bili

ty−0

.094

−4.1

20.

046

2.57

−0.0

15−0

.30

−0.0

21−0

.69

Ret

irem

ent

dum

my

−0.0

60−0

.83

0.00

90.

160.

101

0.61

0.20

72.

19U

nem

ploy

men

tdu

mm

y0.

069

1.02

−0.0

20−0

.34

0.01

60.

110.

003

0.03

En

trep

ren

eur

dum

my

0.03

00.

390.

013

0.20

−0.1

21−0

.67

0.26

02.

28S

tude

nt

dum

my

0.14

81.

130.

055

0.51

−0.5

73−2

.35

−0.1

94−1

.27


TA

BL

EV

II(C

ON

TIN

UE

D)

Sto

cks

Fu

nds

Win

ner

sL

oser

sW

inn

ers

Los

ers

Est

imat

et-

stat

Est

imat

et-

stat

Est

imat

et-

stat

Est

imat

et-

stat

Dem

ogra

phic

char

acte

rist

ics

inte

ract

edw

ith

gain

/lo

ssA

ge0.

010

5.35

0.00

21.

61−0

.008

−1.9

70.

014

5.35

Hou

seh

old

size

0.00

50.

320.

003

0.21

0.05

41.

75−0

.045

−2.2

2H

igh

sch

oold

um

my

−0.0

14−0

.23

0.04

40.

94−0

.147

−1.2

7−0

.161

−2.4

1P

ost-

hig

hsc

hoo

ldu

mm

y0.

016

0.36

−0.0

48−1

.53

−0.0

96−1

.14

−0.0

29−0

.57

Du

mm

yfo

ru

nav

aila

ble

edu

cati

onda

ta0.

040

0.39

0.12

61.

57−0

.099

−0.4

3−0

.168

−1.5

1Im

mig

rati

ondu

mm

y−0

.116

−1.7

40.

010

0.22

0.04

20.

38−0

.105

−1.4

3

Non

inte

ract

edco

ntr

olS

har

eof

asse

tj

inri

sky

port

foli

o−0

.816

−39.

20−0

.847

−57.

40N

um

ber

ofob

serv

atio

ns

139,

155

285,

223

67,6

2347

8,65

9

Not

es.T

his

tabl

ere

port

sa

pool

edpr

obit

regr

essi

onth

ata

hou

seh

old

full

yse

lls

anas

set.

We

dist

ingu

ish

betw

een

stoc

ksan

dfu

nds

and

clas

sify

ase

curi

tyas

aw

inn

er(l

oser

)if

ityi

elds

apo

siti

ve(n

egat

ive)

retu

rndu

rin

gth

eye

ar.E

ach

regr

essi

onin

clu

des

the

foll

owin

gse

tof

expl

anat

ory

vari

able

s:ye

arly

fixe

def

fect

s(u

nrep

orte

d),g

ain

/loss

ofas

set

j,sh

are

ofas

set

jin

the

risk

ypo

rtfo

lio,

non

inte

ract

edch

arac

teri

stic

s(u

nre

port

ed),

and

inte

ract

edch

arac

teri

stic

s.T

he

gain

/loss

ofas

set

jis

the

abso

lute

valu

eof

the

asse

t’spe

rfor

man

cedu

rin

gth

eye

ar,a

nd

disp

osab

lein

com

eis

aver

aged

over

the

prev

iou

sth

ree

year

s.T

he

esti

mat

ion

isba

sed

onh

ouse

hol

dsth

atpa

rtic

ipat

ein

risk

yas

set

mar

kets

atth

een

dof

two

con

secu

tive

year

san

dfo

rw

hic

hth

eim

mig

rati

ondu

mm

yis

avai

labl

e.T

he

shar

eof

asse

tj

inth

eri

sky

port

foli

ois

ast

and-

alon

ere

gres

sor

that

isn

otin

tera

cted

wit

hpe

rfor

man

ce.A

llch

arac

teri

stic

sar

ede

mea

ned

year

byye

ar,a

nd

con

tin

uou

sfi

nan

cial

char

acte

rist

ics

are

nor

mal

ized

toh

ave

un

itst

anda

rdde

viat

ion

inea

chcr

oss

sect

ion

.Por

tfol

ioch

arac

teri

stic

sar

ein

nat

ura

lun

its.

Sta

nda

rder

rors

are

robu

stan

dcl

ust

ered

byh

ouse

hol

d.


stockholdings. Losing funds are also more likely to be sold bywealthier households, but we otherwise find little significance ofcharacteristics in the regressions that predict full sales of mutualfunds.

Overall, the table reveals that more sophisticated and ag-gressive households, with larger portfolios of directly held stocks,appear to be less prone to the disposition effect.

Partial Sales, Continued Holdings, and Partial Purchases.We have shown in previous sections that households fight idiosyn-cratic passive variations in the risky share. We now investigatewhether they also rebalance individual security positions in therisky portfolio. The analysis excludes full sales, but considers par-tial sales, continued holdings, and partial purchases. In the Ap-pendix, we check that the evidence of asset-level rebalancing iseven stronger when we include full sales.

For each household h, we define w∗h, j,t as the share of asset

j in the risky (not the complete) portfolio at the end of period t,rh,t+1 as the net return on the risky portfolio between t and t + 1,and rj,t+1 as the net return on asset j. If the household does nottrade risky assets in the next period, the asset’s passive share atthe end of t + 1 is

w∗ph, j,t+1 = w∗

h, j,t1 + rj,t+1

1 + rh,t+1.

Let A∗h, j,t+1 = w∗

h, j,t+1 − w∗ph, j,t+1 and P∗

h, j,t+1 = w∗ph, j,t+1 − w∗

h, j,t de-note the corresponding active and passive changes in levels, anda∗

h, j,t+1 and p∗h, j,t+1 their equivalents in logs.

We estimate a rebalancing model for individual assets. InTable VIII, we report the pooled and yearly regressions of thelog active change a∗

h, j,t+1 on the initial share and the log passivechange p∗

h, j,t+1, both for stocks (Panel A) and funds (Panel B). Therebalancing coefficient is only slightly more negative for stocksthan for funds, and approximately equal to one-sixth. Thus, whenwe focus on partial sales, the rebalancing coefficient is roughlyconstant across funds and stocks. In the Appendix, we report therebalancing regressions in levels and also find evidence of activerebalancing at the asset level. The rebalancing coefficients in lev-els are somewhat larger in absolute value but also more variableacross regressions.

The interpretation of Table VIII is complicated by theambiguous predictions of financial theory. In a mean-variance


TA

BL

EV

III

AS

SE

T-L

EV

EL

AC

TIV

EC

HA

NG

EIN

TH

ER

ISK

YP

OR

TF

OL

IO

All

year

s20

0020

0120

02

Est

imat

et-

stat

Est

imat

et-

stat

Est

imat

et-

stat

Est

imat

et-

stat

A.S

tock

sP

assi

vech

ange

ofst

ock

jin

logs

−0.1

79−6

1.10

−0.0

69−9

.87

−0.1

62−3

2.20

−0.2

20−5

6.70

Log

init

ialr

isky

shar

eof

stoc

kj

−0.0

38−4

6.10

−0.0

58−3

4.60

−0.0

41−2

6.40

−0.0

23−1

6.70

Inte

rcep

t−0

.271

−44.

60−0

.127

−25.

70−0

.068

−15.

2020

00du

mm

y−0

.204

−49.

1020

01du

mm

y−0

.118

−35.

6020

02du

mm

y−0

.117

−36.

00N

um

ber

ofob

serv

atio

ns

354,

294

90,9

5112

6,76

913

6,57

4A

dju

sted

R2

0.05

0.03

0.04

0.06

B.M

utu

alfu

nd

sP

assi

vech

ange

offu

nd

jin

logs

−0.1

65−2

2.70

−0.1

89−1

0.40

−0.1

53−1

1.50

−0.1

62−1

8.60

Log

init

ialr

isky

shar

eof

fun

dj

−0.0

91−9

3.90

−0.1

09−6

4.50

−0.1

04−7

1.00

−0.0

62−4

9.10

Inte

rcep

t−0

.268

−82.

70−0

.183

−66.

80−0

.126

−50.

5020

00du

mm

y−0

.230

−95.

6020

01du

mm

y−0

.155

−69.

4020

02du

mm

y−0

.191

−83.

90N

um

ber

ofob

serv

atio

ns

498,

502

147,

930

175,

692

174,

880

Adj

ust

edR

20.

070.

080.

080.

04

Not

es.T

his

tabl

ere

port

sth

ere

gres

sion

ofas

set

j’sac

tive

chan

gein

logs

onth

eas

set’s

pass

ive

chan

gean

din

itia

lsh

are

inth

eri

sky

port

foli

o,bo

thfo

rst

ocks

(Pan

elA

)an

dfo

rm

utu

alfu

nds

(Pan

elB

).W

ein

clu

depa

rtia

lsal

es,c

onti

nu

edh

oldi

ngs

,an

dpa

rtia

lpu

rch

ases

,bu

tex

clu

defu

llsa

les

and

purc

has

esof

secu

riti

esth

atar

en

oth

eld

atal

lat

the

begi

nn

ing

ofth

epe

riod

.Th

ees

tim

atio

nis

base

don

hou

seh

olds

that

part

icip

ate

inri

sky

asse

tm

arke

tsat

the

end

oftw

oco

nse

cuti

veye

ars.

Sta

nda

rder

rors

are

robu

stan

dcl

ust

ered

byh

ouse

hol

d.


portfolio choice problem with constant investment opportuni-ties, the optimal weights of individual assets in the risky port-folio are time-invariant, which implies complete rebalancing witha∗

h, j,t+1 = −p∗h, j,t+1; but partial rebalancing might be justified by

a variety of factors such as changes in risk and return expec-tations, taxes, transactions costs, or Bayesian updating of beliefsabout mutual fund management. In the Appendix, we ask how thepropensity to rebalance individual assets varies with householdand portfolio characteristics, but do not find any strong effects.

In this section we have shown that households are much morelikely to fully sell a stock when it has performed well, and slightlymore likely to sell a losing fund. This finding is consistent with theliterature on the disposition effect. If a household continues to owna stock or mutual fund, however, its behavior is well described bya rebalancing model with a rebalancing coefficient of about one-sixth, and the rebalancing propensity is almost the same for stocksand for mutual funds.

VI. TRADING DECISIONS AND RISKY PORTFOLIO REBALANCING

We now investigate how rebalancing of the complete portfo-lio, which was documented in Sections III and IV, relates to thedynamics of individual asset holdings discussed in Section V. Wefocus on the contributions of different asset classes and transac-tion types to the overall active change of the risky share.

The active change of the risky share between dates t and t + 1can be decomposed as

(7) Ah,t+1 =∑

j

(wh, j,t+1 − w

ph, j,t+1

),

where wh, j,t+1 denotes the share of asset j in the complete portfolioat the end of year t + 1, and w

ph, j,t+1 is the passive share of asset j in

the complete portfolio if the agent does not trade during the year:

wph, j,t+1 = wh, j,t(1 + rj,t+1)

wh,t(1 + rh,t) + (1 − wh,t)(1 + r f ).

The individual terms on the right-hand side of (7) can begrouped into specific asset classes c (fund or stock) and transactiontypes θ (purchase or sale):

Ah,t+1 =∑

c,θ

Ah,c,θ,t+1.


FIGURE IVContribution of Sales and Purchases to Portfolio Rebalancing

Notes. The figure decomposes the portfolio rebalancing coefficient into its var-ious components: (partial or full) purchases or sales of stocks and funds by luckyand unlucky households. For each transaction type and household group, we reportthe rebalancing coefficient and t-statistic of the corresponding rebalancing regres-sion in levels, with yearly fixed effects and the risky share included as controls. Ahousehold is defined as lucky (unlucky) if the passive return on its risky portfolioduring the year is higher (lower) than the cross-sectional average.

We can also distinguish between partial sales, full sales, partialpurchases (assets already held by the household), and full pur-chases (new types of assets).

Each component of the active change Ah,c,θ,t+1 can be re-gressed on the passive change of the risky portfolio Ph,t+1, thedemeaned initial risky share wh,t − wh,t, and a time-dependent in-tercept. The corresponding rebalancing coefficients γ1,c,θ,t+1 addup to the overall rebalancing coefficient:

∑c,θ γ1,c,θ,t+1 = γ1. This

decomposition allows us to identify how various asset classes andtransaction types contribute to portfolio rebalancing.

Given our findings in Section V on the disposition effect, itis also useful to distinguish between transactions carried out bylucky and unlucky households. Specifically, a household is classi-fied as lucky if the return on its risky portfolio exceeds the popu-lation average during the same year. In Figure IV, we report the


rebalancing coefficients γ1,c,θ,t+1 and their t-statistics, and referthe reader to the Appendix for the full regression results. Luckyand unlucky households both have a rebalancing coefficient ofabout −0.5, which is slightly larger in absolute value for luckyhouseholds. Stocks and funds play an important role in rebalanc-ing for both groups of investors. We have verified that our resultsare almost identical when the median return is used as an alter-native cutoff, or when we consider households in the top third andbottom third of risky portfolio performance.

When we disaggregate sales and purchases, however, we dosee important differences between lucky and unlucky households.Lucky households rebalance primarily by adjusting their stocksales and fund purchases. The rebalancing coefficient of −0.58 isalmost entirely explained by the coefficients of full sales (−0.29)and partial purchases (−0.24). That is, lucky households, whichneed to reduce their holdings of risky assets, achieve this objectiveby fully selling more stocks and buying less mutual funds thanthey would otherwise.

Unlucky households, on the other hand, mainly rebalance byadjusting their purchases of new assets. The rebalancing coef-ficient (equal to −0.50) is primarily driven by stock purchases(−0.30) and fund purchases (−0.13). The impact of asset sales ismarginal.

Households thus primarily rebalance by increasing their (full)stock sales and reducing their fund purchases when they arelucky, and increasing their purchases of risky assets (primarilystocks) when they are unlucky. These common strategies mini-mize transaction costs. The exception is the full sale of stocksby lucky households, which may be caused by the dispositioneffect.

In the Appendix, we investigate how these common rebal-ancing strategies are influenced by household characteristics. Wefind that wealthier households with more diversified portfoliostend to rebalance more strongly through purchases of risky assetsbut less through stock sales. Thus, it appears that more sophisti-cated households are less prone to the disposition effect and makegreater use of rebalancing strategies that minimize transactioncosts. In addition, we find possible substitution effects betweenpurchasing strategies. Households with substantial and diversi-fied portfolios of funds rebalance more strongly through fund pur-chases and less through stock purchases.


VII. CONCLUSIONS

The burgeoning field of household finance asks how house-holds manage their financial affairs, and whether they act in away that is consistent with the normative literature on portfoliochoice. Our previous paper (CCS 2007) studied household diversi-fication at a point in time, whereas this paper looks at changes inhousehold behavior over time, specifically decisions to scale up ordown the share of risky assets in the total portfolio, to enter or exitrisky financial markets, to fully sell individual risky assets, and toscale up or down the share of individual assets in the risky port-folio. These decisions are important in themselves and are alsoparticularly revealing because households rarely delegate thesedecisions to financial intermediaries.

Although other papers have looked at the dynamics of house-hold risk taking, our data set gives us the unique ability to relatehousehold decisions to the properties of the initial portfolio. Whenstudying movements in the risky asset share, we find that house-holds overall have a surprisingly large propensity to rebalance,offsetting about half the passive changes in the risky share withactive changes.

We can identify the propensity to rebalance, distinguishing itfrom mean-reversion in the risky share and aggregate shifts in thedesired risky share, because households have heterogeneous port-folios which generate cross-sectional variation in passive changesin the risky share. This implies that households can rebalance ac-tively even in a closed economy general equilibrium; householdswith lower than average returns can buy stocks from householdswith higher than average returns. The microeconomic nature ofour results means that we have little to say about rebalancing bythe Swedish household sector as a whole in response to aggregateshocks, but such rebalancing appears to be quite limited in ourshort data set.

We develop an adjustment model with different target riskyshares across households. We find that more educated and wealth-ier households, holding better diversified portfolios, tend to rebal-ance more actively. Wealthier households also reduced their targetrisky shares less during the bear market of the early 2000s.

Some financial theories imply that as a household becomesricher, its target risky share should increase. This will be the case,for example, if households have declining risk aversion, followportfolio insurance policies, or derive utility from consumption


that is surplus to a slowly evolving habit level. We find evidenceconsistent with this view once we control for sluggish portfolioadjustment.

Our results on decisions to exit risky financial markets andto fully sell individual risky positions are intriguingly consistentwith previous research on the disposition effect among stockhold-ers and performance sensitivity among mutual fund investors. Wefind that high returns on directly held stock portfolios stronglyincrease the probability that households will exit direct stockhold-ing, whereas this effect is almost absent for mutual fund portfolios.At the level of individual assets, a household is considerably morelikely to fully sell a stock during a year if it is a “winner” that hasperformed well during the year, but this tendency is again muchweaker for mutual funds. One possible explanation for thesepatterns is that individual investors buy stocks that they perceiveto be undervalued and maintain fixed beliefs about fundamentalvalue in the face of market price movements; but they buymutual funds that they perceive to be well managed and updatetheir beliefs about manager skill in response to mutual fundperformance.

These asymmetries between winners and losers, and betweenstocks and mutual funds, disappear when households adjust theshares of individual risky assets within their risky portfolios,without fully selling them. Rebalancing for assets that householdscontinue to own is close to symmetric, and it actively offsets aboutone-sixth of the passive changes in individual asset shares causedby individual asset performance.

We ask how households use partial and full sales and pur-chases of stocks and mutual funds to alter their overall riskyshares. We find that households primarily rebalance by increas-ing their full sales of stock and reducing their fund purchaseswhen they have higher than average portfolio returns, and byincreasing their purchases of risky assets when they have lowerthan average returns.

Finally, we examine how household characteristics affect de-cisions to trade individual assets. We find that wealthy investorswith high risky shares and diversified portfolios of individualstocks are less likely to fully sell winning stocks and more likelyto fully sell losing stocks. Dhar and Zhu (2006) have documented asimilar pattern for U.S. investors with high self-reported income.Any model of the disposition effect must explain this finding thatit is not uniform across the population.


Many papers in the household finance literature, includingCampbell (2006) and CCS 2007, have reported that educated,better-off households conform more closely to the predictions ofstandard finance theory. Rebalancing behavior and decisions tofully sell stock positions are two more examples of this phe-nomenon.

HEC PARIS AND NATIONAL BUREAU OF ECONOMIC RESEARCH

HARVARD UNIVERSITY AND NATIONAL BUREAU OF ECONOMIC RESEARCH

STOCKHOLM SCHOOL OF ECONOMICS

REFERENCES

Agnew, J., P. Balduzzi, and A. Sunden, “Portfolio Choice and Trading in a Large401(k) Plan,” American Economic Review, 93 (2003), 193–215.

Ameriks, John, and Stephen Zeldes, “How Do Household Portfolio Shares Varywith Age?” Working Paper, Columbia University, 2004.

Barber, Brad M., and Terrance Odean, “Trading is Hazardous to your Wealth: TheCommon Stock Investment Performance of Individual Investors,” Journal ofFinance, 55 (2000), 773–806.

Bilias, Yannis, Dimitris Georgarakos, and Michael Haliassos, “Portfolio Inertia andStock Market Fluctuations,” Working Paper, Athens University of Economicsand Business and Goethe University Frankfurt, 2008.

Brennan, Michael, and Eduardo Schwartz, “Time Invariant Portfolio InsuranceStrategies,” Journal of Finance, 43 (1988), 283–299.

Brunnermeier, Markus, and Stefan Nagel, “Do Wealth Fluctuations GenerateTime-Varying Risk Aversion? Micro-evidence on Individuals’ Asset Alloca-tion,” American Economic Review, 98 (2008), 713–736.

Calvet, Laurent E., John Y. Campbell, and Paolo Sodini, “Down or Out: Assessingthe Welfare Costs of Household Investment Mistakes,” Journal of PoliticalEconomy, 115 (2007), 707–747.

Calvet, Laurent E., Martın Gonzalez-Eiras, and Paolo Sodini, “Financial Innova-tion, Market Participation and Asset Prices,” Journal of Financial and Quan-titative Analysis, 39 (2004), 431–459.

Campbell, John Y., “Household Finance,” Journal of Finance, 61 (2006), 1553–1604.

Campbell, John Y., and John Cochrane, “By Force of Habit: A Consumption BasedExplanation of Aggregate Stock Market Behavior,” Journal of Political Econ-omy, 107 (1999), 205–251.

Carroll, Christopher D., “Why Do the Rich Save So Much?” in Does Atlas Shrug?The Economic Consequences of Taxing the Rich, J. B. Slemrod, ed. (Cambridge,MA: Harvard University Press, 2000).

——, “Portfolios of the Rich,” in Household Portfolios, Luigi Guiso, Michael Halias-sos, and Tullio Jappelli, eds. (Cambridge, MA: MIT Press, 2002).

Chevalier, Judith, and Glenn Ellison, “Risk Taking by Mutual Funds as a Responseto Incentives,” Journal of Political Economy, 105 (1997), 1167–2000.

Choi, James J., David Laibson, Brigitte Madrian, and Andrew Metrick, “DefinedContribution Pensions: Plan Rules, Participant Decisions, and the Path ofLeast Resistance,” in Tax Policy and the Economy 16, James M. Poterba, ed.(Cambridge, MA: MIT Press, 2002).

——, “For Better or for Worse: Default Effects and 401(k) Savings Behavior,” in Per-spectives on the Economics of Aging, David Wise, ed. (Chicago, IL: Universityof Chicago Press, 2004).

Constantinides, George M., “Habit Formation: A Resolution of the Equity PremiumPuzzle,” Journal of Political Economy, 98 (1990), 519–543.


Dhar, Ravi, and Ning Zhu, “Up Close and Personal: Investor Sophistication andthe Disposition Effect,” Management Science, 52 (2006), 726–740.

Dybvig, Philip H., “Duesenberry’s Ratcheting of Consumption: Optimal DynamicConsumption and Investment Policies Given Intolerance for any Decline inStandard of Living,” Review of Economic Studies, 62 (1995), 287–313.

Frazzini, Andrea, “The Disposition Effect and Underreaction to News,” Journal ofFinance, 61 (2006), 2017–2046.

Frazzini, Andrea, and Owen Lamont, “Dumb Money: Mutual Fund Flows and theCross-Section of Stock Returns,” Journal of Financial Economics, 88 (2008),299–322.

Goetzmann, William N., and Massimo Massa, “Disposition Matters: Volume,Volatility, and Price Impact of a Behavioral Bias,” Journal of Portfolio Man-agement, Winter (2008), 103–125.

Grinblatt, Mark, and Bing Han, “Prospect Theory, Mental Accounting, and Mo-mentum,” Journal of Financial Economics, 78 (2005), 311–339.

Gruber, Martin, “Another Puzzle: The Growth in Actively Managed Mutual Funds,”Journal of Finance, 51 (1996), 783–810.

Guiso, Luigi, Michael Haliassos, and Tullio Japelli, Household Portfolios (Cam-bridge, MA: MIT Press, 2002).

Hurst, Erik, Ming Ching Luoh, and Frank Stafford, “The Wealth Dynamics ofAmerican Families, 1984–94,” Brookings Papers on Economic Activity, 1998(1998), 267–329.

Ippolito, Richard, “Consumer Reaction to Measures of Poor Quality: Evidence fromthe Mutual Fund Industry,” Journal of Law and Economics, 35 (1992), 45–70.

Ivkovic, Zoran, and Scott Weisbenner, “‘Old’ Money Matters: The Sensitivity ofMutual Fund Redemption Decisions to Past Performance,” Working Paper,University of Illinois, 2007.

Madrian, Brigitte and Dennis Shea, “The Power of Suggestion: Inertia in 401(k)Participation and Savings Behavior,” Quarterly Journal of Economics, 116(2001), 1149–1188.

Odean, Terrance, “Are Investors Reluctant to Realize Their Losses?” Journal ofFinance, 53 (1998), 1775–1798.

——, “Do Investors Trade Too Much?” American Economic Review, 89 (1999),1279–1298.

Shefrin, Hersh, and Meir Statman, “The Disposition to Sell Winners Too Early andRide Losers Too Long: Theory and Evidence,” Journal of Finance, 40 (1985),777–782.

Sirri, Erik, and Peter Tufano, “Costly Search and Mutual Fund Flows,” Journal ofFinance, 53 (1998), 1589–1622.

Tracy, Joseph, and Henry Schneider, “Stocks in the Household Portfolio: A LookBack at the 1990s,” Current Issues in Economics and Finance, 7 (2001): 4.

Vissing-Jorgensen, Annette, “Towards an Explanation of Household PortfolioChoice Heterogeneity: Nonfinancial Income and Participation Cost Struc-tures,” NBER Working Paper 8884, 2002.

Documents

FIGHT OR FLIGHT? PORTFOLIO REBALANCING BY INDIVIDUAL ... · FIGHT OR FLIGHT? PORTFOLIO REBALANCING BY INDIVIDUAL INVESTORS ... in perceived investment opportunities, on the other