boys will be boys - faculty & research

BOYS WILL BE BOYS GENDER OVERCONFIDENCEAND COMMON STOCK INVESTMENT

BRAD M BARBER AND TERRANCE ODEAN

Theoretical models predict that overcondent investors trade excessively Wetest this prediction by partitioning investors on gender Psychological researchdemonstrates that in areas such as nance men are more overcondent thanwomen Thus theory predicts that men will trade more excessively than womenUsing account data for over 35000 households from a large discount brokeragewe analyze the common stock investments of men and women from February 1991through January 1997 We document that men trade 45 percent more thanwomen Trading reduces menrsquos net returns by 265 percentage points a year asopposed to 172 percentage points for women

Itrsquos not what a man donrsquot know that makes him a fool but what he does knowthat ainrsquot so

Josh Billings nineteenth century American humorist

It is difcult to reconcile the volume of trading observed inequity markets with the trading needs of rational investors Ra-tional investors make periodic contributions and withdrawalsfrom their investment portfolios rebalance their portfolios andtrade to minimize their taxes Those possessed of superior infor-mation may trade speculatively although rational speculativetraders will generally not choose to trade with each other It isunlikely that rational trading needs account for a turnover rate of76 percent on the New York Stock Exchange in 19981

We believe there is a simple and powerful explanation forhigh levels of trading on nancial markets overcondence Hu-man beings are overcondent about their abilities their knowl-edge and their future prospects Odean [1998] shows that over-condent investorsmdashwho believe that the precision of theirknowledge about the value of a security is greater than it actually

We are grateful to the discount brokerage rm that provided us with thedata for this study and grateful to Paul Thomas David Moore Paine Webber andthe Gallup Organization for providing survey data We appreciate the commentsof Diane Del Guercio David Hirshleifer Andrew Karolyi Timothy LoughranEdward Opton Jr Sylvester Schieber Andrei Shleifer Martha Starr-McCluerRichard Thaler Luis Viceira and participants at the University of AlbertaArizona State University INSEAD the London Business School the University ofMichigan the University of Vienna the Institute on Psychology and Markets theConference on Household Portfolio Decision-making and Asset Holdings at theUniversity of Pennsylvania and the Western Finance Association Meetings Allerrors are our own Terrance Odean can be reached at (530) 752-5332 orodean] ucdavisedu Brad Barber can be reached at (530) 752-0512 orbmbarber] ucdavisedu

1 NYSE Fact Book for the Year 1999

copy 2001 by the President and Fellows of Harvard College and the Massachusetts Institute ofTechnologyThe Quarterly Journal of Economics February 2001

261

ismdashtrade more than rational investors and that doing so lowerstheir expected utilities Greater overcondence leads to greatertrading and to lower expected utility

A direct test of whether overcondence contributes to exces-sive market trading is to separate investors into those more andthose less prone to overcondence One can then test whethermore overcondence leads to more trading and to lower returnsSuch a test is the primary contribution of this paper

Psychologists nd that in areas such as nance men are moreovercondent than women This difference in overcondenceyields two predictions men will trade more than women and theperformance of men will be hurt more by excessive trading thanthe performance of women To test these hypotheses we partitiona data set of position and trading records for over 35000 house-holds at a large discount brokerage rm into accounts opened bymen and accounts opened by women Consistent with the predic-tions of the overcondence models we nd that the averageturnover rate of common stocks for men is nearly one and a halftimes that for women While both men and women reduce theirnet returns through trading men do so by 094 percentage pointsmore a year than do women

The differences in turnover and return performance are evenmore pronounced between single men and single women Singlemen trade 67 percent more than single women thereby reducingtheir returns by 144 percentage points per year more than dosingle women

The remainder of this paper is organized as follows Wemotivate our test of overcondence in Section I We discuss ourdata and empirical methods in Section II Our main results arepresented in Section III We discuss competing explanationsfor our results in Section IV and make concluding remarks inSection V

I A TEST OF OVERCONFIDENCE

IA Overcondence and Trading on Financial Markets

Studies of the calibration of subjective probabilities nd thatpeople tend to overestimate the precision of their knowledge[Alpert and Raiffa 1982 Fischhoff Slovic and Lichtenstein1977] see Lichtenstein Fischhoff and Phillips [1982] for a re-view of the calibration literature Such overcondence has been

262 QUARTERLY JOURNAL OF ECONOMICS

observed in many professional elds Clinical psychologists [Os-kamp 1965] physicians and nurses [Christensen-Szalanski andBushyhead 1981 Baumann Deber and Thompson 1991] invest-ment bankers [Stael von Holstein 1972] engineers [Kidd 1970]entrepreneurs [Cooper Woo and Dunkelberg 1988] lawyers[Wagenaar and Keren 1986] negotiators [Neale and Bazerman1990] and managers [Russo and Schoemaker 1992] have all beenobserved to exhibit overcondence in their judgments (For fur-ther discussion see Lichtenstein Fischhoff and Phillips [1982]and Yates [1990])

Overcondence is greatest for difcult tasks for forecastswith low predictability and for undertakings lacking fast clearfeedback [Fischhoff Slovic and Lichtenstein 1977 LichtensteinFischhoff and Phillips 1982 Yates 1990 Grifn and Tversky1992] Selecting common stocks that will outperform the marketis a difcult task Predictability is low feedback is noisy Thusstock selection is the type of task for which people are mostovercondent

Odean [1998] develops models in which overcondent inves-tors overestimate the precision of their knowledge about thevalue of a nancial security2 They overestimate the probabilitythat their personal assessments of the securityrsquos value are moreaccurate than the assessments of others Thus overcondentinvestors believe more strongly in their own valuations andconcern themselves less about the beliefs of others This intensi-es differences of opinion And differences of opinion cause trad-ing [Varian 1989 Harris and Raviv 1993] Rational investors onlytrade and only purchase information when doing so increasestheir expected utility (eg Grossman and Stiglitz [1980]) Over-condent investors on the other hand lower their expected util-ity by trading too much they hold unrealistic beliefs about howhigh their returns will be and how precisely these can be esti-mated and they expend too many resources (eg time andmoney) on investment information [Odean 1998] Overcondent

2 Other models of overcondent investors include De Long Shleifer Sum-mers and Waldmann [1991] Benos [1998] Kyle and Wang [1997] Daniel Hirsh-leifer and Subramanyam [1998] Gervais and Odean [1998] and Caballe andSakovics [1998] Kyle and Wang argue that when traders compete for duopolyprots overcondent traders may reap greater prots However this prediction isbased on several assumptions that do not apply to individuals trading commonstocks

Odean [1998] points out that overcondence may result from investors over-estimating the precision of their private signals or alternatively overestimatingtheir abilities to correctly interpret public signals

263BOYS WILL BE BOYS

investors also hold riskier portfolios than do rational investorswith the same degree of risk aversion [Odean 1998]

Barber and Odean [2000] and Odean [1999] test whetherinvestors decrease their expected utility by trading too muchUsing the same data analyzed in this paper Barber and Odeanshow that after accounting for trading costs individual investorsunderperform relevant benchmarks Those who trade the mostrealize by far the worst performance This is what the models ofovercondent investors predict With a different data set Odean[1999] nds that the securities individual investors buy subse-quently underperform those they sell When he controls for li-quidity demands tax-loss selling rebalancing and changes inrisk aversion investorsrsquo timing of trades is even worse Thisresult suggests that not only are investors too willing to act on toolittle information but they are too willing to act when they arewrong

These studies demonstrate that investors trade too much andto their detriment The ndings are inconsistent with rationalityand not easily explained in the absence of overcondence Nev-ertheless overcondence is neither directly observed nor manip-ulated in these studies A yet sharper test of the models thatincorporate overcondence is to partition investors into thosemore and those less prone to overcondence The models predictthat the more overcondent investors will trade more and realizelower average utilities To test these predictions we partition ourdata on gender

IB Gender and Overcondence

While both men and women exhibit overcondence men aregenerally more overcondent than women [Lundeberg Fox andPuncochar 1994]3 Gender differences in overcondence arehighly task dependent [Lundeberg Fox and Puncochar 1994]Deaux and Farris [1977] write ldquoOverall men claim more abilitythan do women but this difference emerges most strongly on masculine task[s]rdquo Several studies conrm that differences incondence are greatest for tasks perceived to be in the masculinedomain [Deaux and Emswiller 1974 Lenney 1977 Beyer andBowden 1997] Men are inclined to feel more competent than

3 While Lichtenstein and Fishhoff [1981] do not nd gender differences incalibration of general knowledge Lundeberg Fox and Puncochar [1994] arguethat this is because gender differences in calibration are strongest for topics in themasculine domain


women do in nancial matters [Prince 1993] Indeed casual ob-servation reveals that men are disproportionately represented inthe nancial industry We expect therefore that men will gen-erally be more overcondent about their ability to make nancialdecisions than women

Additionally Lenney [1977] reports that gender differencesin self-condence depend on the lack of clear and unambiguousfeedback When feedback is ldquounequivocal and immediately avail-able women do not make lower ability estimates than menHowever when such feedback is absent or ambiguous womenseem to have lower opinions of their abilities and often do under-estimate relative to menrdquo Feedback in the stock market is am-biguous All the more reason to expect men to be more condentthan women about their ability to make common stockinvestments

Gervais and Odean [1998] develop a model in which investorovercondence results from self-serving attribution bias Inves-tors in this model infer their own abilities from their successesand failures Due to their tendency to take too much credit fortheir successes they become overcondent Deaux and Farris[1977] Meehan and Overton [1986] and Beyer [1990] nd thatthe self-serving attribution bias is greater for men than forwomen And so men are likely to become more overcondent thanwomen

The previous study most like our own is Lewellen Lease andSchlarbaumrsquos [1977] analysis of survey answers and brokeragerecords (from 1964 through 1970) of 972 individual investorsLewellen Lease and Schlarbaumrsquos report that men spend moretime and money on security analysis rely less on their brokersmake more transactions believe that returns are more highlypredictable and anticipate higher possible returns than dowomen In all these ways men behave more like overcondentinvestors than do women

Additional evidence that men are more overcondent inves-tors than women comes from surveys conducted by the GallupOrganization for PaineWebber Gallup conducted the survey f-teen times between June 1998 and January 2000 There wereapproximately 1000 respondents per survey In addition to otherquestions respondents were asked ldquoWhat overall rate of returndo you expect to get on your portfolio in the NEXT twelvemonthsrdquo and ldquoThinking about the stock market more generallywhat overall rate of return do you think the stock market will


provide investors during the coming twelve monthsrdquo On averageboth men and women expected their own portfolios to outperformthe market However men expected to outperform by a greatermargin (28 percent) than did women (21 percent) The differencein the average anticipated outperformance of men and women isstatistically signicant (t = 33)4

In summary we have a natural experiment to (almost) di-rectly test theoretical models of investor overcondence A ratio-nal investor only trades if the expected gain exceeds the transac-tions costs An overcondent investor overestimates the precisionof his information and thereby the expected gains of trading Hemay even trade when the true expected net gain is negative Sincemen are more overcondent than women this gives us two test-able hypotheses

H1 Men trade more than women

H2 By trading more men hurt their performance more than dowomen

It is these two hypotheses that are the focus of our inquiry5

II DATA AND METHODS

IIA Household Account and Demographic Data

Our main results focus on the common stock investments of37664 households for which we are able to identify the gender ofthe person who opened the householdrsquos rst brokerage accountThis sample is compiled from two data sets

Our primary data set is information from a large discountbrokerage rm on the investments of 78000 households for the sixyears ending in December 1996 For this period we have end-of-month position statements and trades that allow us to reasonablyestimate monthly returns from February 1991 through January

4 Some respondents answered that they expected market returns as high as900 We suspect that these respondents were thinking of index point moves ratherthan percentage returns Therefore we have dropped from our calculations re-spondents who gave answers of more than 100 to this question If alternativelywe Windsorize answers over 900 at 100 there is no signicant change in ourresults

5 Overcondence models also imply that more overcondent investors willhold riskier portfolios In Section III we present evidence that men hold riskiercommon stock portfolios than women However gender differences in portfoliorisk may be due to differences in risk tolerance rather than (or in addition to)differences in overcondence


1997 The data set includes all accounts opened by the 78000 house-holds at this discount brokerage rm Sampled households wererequired to have an open account with the discount brokerage rmduring 1991 Roughly half of the accounts in our analysis wereopened prior to 1987 while half were opened between 1987 and1991 On average men opened their rst account at this brokerage47 years before the beginning of our sample period while womenopened theirs 43 years before During the sample period menrsquosaccounts held common stocks for 58 months on average and wom-enrsquos for 59 months The median number of months men held com-mon stocks is 70 For women it is 71

In this research we focus on the common stock investmentsof households We exclude investments in mutual funds (bothopen- and closed-end) American depository receipts (ADRs) war-rants and options Of the 78000 sampled households 66465 hadpositions in common stocks during at least one month the re-maining accounts either held cash or investments in other thanindividual common stocks The average household had approxi-mately two accounts and roughly 60 percent of the market valuein these accounts was held in common stocks These householdsmade over 3 million trades in all securities during our sampleperiod common stocks accounted for slightly more than 60 per-cent of all trades The average household held four stocks worth$47000 during our sample period although each of these guresis positively skewed6 The median household held 26 stocksworth $16000 In aggregate these households held more than$45 billion in common stocks in December 1996

Our secondary data set is demographic information compiled byInfobase Inc (as of June 8 1997) and provided to us by the broker-age house These data identify the gender of the person who openeda householdrsquos rst account for 37664 households of which 29659(79 percent) had accounts opened by men and 8005 (21 percent) hadaccounts opened by women In addition to gender Infobase providesdata on marital status the presence of children age and householdincome We present descriptive statistics in Table I Panel A Thesedata reveal that the women in our sample are less likely to bemarried and to have children than men The mean and median agesof the men and women in our sample are roughly equal The womenreport slightly lower household income although the difference isnot economically large

6 Throughout the paper portfolio values are reported in current dollars


TA

BL

EI

DE

SC

RIP

TIV

ES

TA

TIS

TIC

SF

OR

DE

MO

GR

AP

HIC

SO

FF

EM

AL

EA

ND

MA

LE

HO

US

EH

OL

DS

Var

iabl

e

All

hous

ehol

dsM

arri

edho

use

hold

sS

ingl

eho

useh

olds

Wom

enM

en

Dif

fere

nce

(wom

enndash

men

)W

omen

Men

Dif

fere

nce

(wom

enndash

men

)W

omen

Men

Dif

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nce

(wom

enmdash

men

)

Pan

elA

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foba

seda

ta

Nu

mbe

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hou

seho

lds

800

529

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NA

489

419

741

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230

66

326

NA

Per

cen

tage

mar

ried

680

757

27

7P

erce

nta

gew

ith

chil

dren

252

322

27

033

640

42

68

106

105

01

Mea

nag

e50

950

30

649

951

12

12

530

482

48

Med

ian

age

480

480

00

480

480

00

500

460

40

Mea

nin

com

e($

000)

730

756

22

681

279

61

656

762

82

61

w

ith

inco

me

gt$1

250

0011

211

72

05

142

130

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59

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5

Pan

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f-re

port

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hou

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263

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orth

($00

0)90

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450

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500

250

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175

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n10

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100

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100

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e60

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52

145

625

745

212

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220

10th

Per

cent

ile

270

370

210

035

037

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20

200

350

215

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quit

yto

net

wor

th(

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133

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()

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87

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613

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ple

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ith

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ear

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gend

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ildr

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and

inco

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are

from

Info

base

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asof

June

1997

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elf-

repo

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data

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info

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lied

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ntbr

oker

age

rm

atth

eti

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the

acco

unt

isop

ened

byth

epe

rson

onop

enin

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coun

tIn

com

eis

repo

rted

wit

hin

eigh

tra

nges

whe

reth

eto

pra

nge

isgr

eate

rth

an$1

250

00W

eca

lcul

ate

mea

nsus

ing

the

mid

poin

tof

each

ran

gean

d$1

250

00fo

rth

eto

pra

nge

Equ

ity

toN

etW

orth

()i

sth

epr

opor

tion

ofth

em

arke

tva

lue

ofco

mm

onst

ock

inve

stm

ent

atth

isdi

scou

ntbr

oker

age

rm

asof

Janu

ary

1991

toto

tals

elf-

repo

rted

net

wor

thw

hen

the

hou

seho

ldop

ened

its

rst

acco

unt

atth

isbr

oker

age

Tho

seh

ouse

hold

sw

ith

apr

opor

tion

equ

ity

tone

tw

orth

grea

ter

than

100

perc

ent

are

dele

ted

wh

enca

lcul

atin

gm

ean

san

dm

edia

ns

Nu

mbe

rof

obse

rvat

ion

sfo

rea

chva

riab

leis

slig

htl

yle

ssth

anth

en

umbe

rof

repo

rted

hous

ehol

ds


In addition to the data from Infobase we also have a limitedamount of self-reported data collected at the time each householdrst opened an account at the brokerage (and not subsequentlyupdated) which we summarize in Table I Panel B Of particularinterest to us are two variables net worth and investment expe-rience For this limited sample (about one-third of our total sam-ple) the distribution of net worth for women is slightly less thanthat for men although the difference is not economically largeFor this limited sample we also calculate the ratio of the marketvalue of equity (as of the rst month that the account appears inour data set) to self-reported net worth (which is reported at thetime the account is opened) This provides a measure albeitcrude of the proportion of a householdrsquos net worth that is in-vested in the common stocks that we analyze (If this ratio isgreater than one we delete the observation from our analysis)The mean household holds about 13 percent of its net worth in thecommon stocks we analyze and there is little difference in thisratio between men and women

The differences in self-reported experience by gender arequite large In general women report having less investmentexperience than men For example 478 percent of women reporthaving good or extensive investment experience while 625 per-cent of men report the same level of experience

Married couples may inuence each otherrsquos investment deci-sions In some cases the spouse making investment decisions maynot be the spouse who originally opened a brokerage accountThus we anticipate that observable differences in the investmentactivities of men and women will be greatest for single men andsingle women To investigate this possibility we partition ourdata on the basis of marital status The descriptive statistics fromthis partition are presented in the last six columns of Table I Formarried households we observe very small differences in ageincome the distribution of net worth and the ratio of net worthto equity Married women in our sample are less likely to havechildren than married men and they report having less invest-ment experience than men

For single households some differences in demographics be-come larger The average age of the single women in our sample isve years older than that of the single men the median is four yearsolder The average income of single women is $6100 less than thatof single men and fewer report having incomes in excess of$125000 Similarly the distribution of net worth for single women


is lower than that of single men Finally single women reporthaving less investment experience than single men

IIB Return Calculations

To evaluate the investment performance of men and womenwe calculate the gross and net return performance of each house-hold The net return performance is calculated after a reasonableaccounting for the market impact commissions and bid-askspread of each trade

For each trade we estimate the bid-ask spread component oftransaction costs for purchases (sprdb

) or sales (sprds) as

sprds 5 S Pds

cl

Pds

s 2 1 D and sprdb 5 2 S Pdb

cl

Pdb

b 2 1 D Pds

cl and Pdb

cl are the reported closing prices from the Center forResearch in Security Prices (CRSP) daily stock return les on theday of a sale and purchase respectively Pds

s and Pdbb are the

actual sale and purchase price from our account database Ourestimate of the bid-ask spread component of transaction costsincludes any market impact that might result from a trade It alsoincludes an intraday return on the day of the trade The commis-sion component of transaction costs is calculated to be the dollarvalue of the commission paid scaled by the total principal value ofthe transaction both of which are reported in our account data

The average purchase costs an investor 031 percent whilethe average sale costs an investor 069 percent in bid-ask spreadOur estimate of the bid-ask spread is very close to the trading costof 021 percent for purchases and 063 percent for sales paid byopen-end mutual funds from 1966 to 1993 [Carhart 1997]7 Theaverage purchase in excess of $1000 cost 158 percent in commis-sions while the average sale in excess of $1000 cost 145 percent8

We calculate trade-weighted (weighted by trade size) spreadsand commissions These gures can be thought of as the total cost

7 Odean [1999] nds that individual investors are more likely to both buyand sell particular stocks when the prices of those stocks are rising This tendencycan partially explain the asymmetry in buy and sell spreads Any intraday pricerises following transactions subtract from our estimate of the spread for buys andadd to our estimate of the spread for sells

8 To provide more representative descriptive statistics on percentage com-missions we exclude trades less than $1000 The inclusion of these trades resultsin a round-trip commission cost of 5 percent on average (21 percent for purchasesand 31 percent for sales)


of conducting the $24 billion in common stock trades (approxi-mately $12 billion each in purchases and sales) Trade-sizeweighting has little effect on spread costs (027 percent for pur-chases and 069 percent for sales) but substantially reduces thecommission costs (077 percent for purchases and 066 percent forsales)

In sum the average trade in excess of $1000 incurs a round-trip transaction cost of about 1 percent for the bid-ask spread andabout 3 percent in commissions In aggregate round-trip tradescost about 1 percent for the bid-ask spread and about 14 percentin commissions

We estimate the gross monthly return on each common stockinvestment using the beginning-of-month position statementsfrom our household data and the CRSP monthly returns le In sodoing we make two simplifying assumptions First we assumethat all securities are bought or sold on the last day of the monthThus we ignore the returns earned on stocks purchased from thepurchase date to the end of the month and include the returnsearned on stocks sold from the sale date to the end of the monthSecond we ignore intramonth trading (eg a purchase on March6 and a sale of the same security on March 20) although we doinclude in our analysis short-term trades that yield a position atthe end of a calendar month Barber and Odean [2000] provide acareful analysis of both of these issues and document that thesesimplifying assumptions yield trivial differences in our returncalculations

Consider the common stock portfolio for a particular house-hold The gross monthly return on the householdrsquos portfolio (Rht

gr)is calculated as

Rhtgr 5 O

i= 1

sht

pitRitgr

where pit is the beginning-of-month market value for the holdingof stock i by household h in month t divided by the beginning-of-month market value of all stocks held by household h Rit

gr is thegross monthly return for that stock and sht is the number ofstocks held by household h in month t

For security i in month t we calculate a monthly return netof transaction costs (Rit

net) as

(1 1 Ritnet) 5 (1 1 Rit

gr) (1 2 cits )(1 1 c it 2 1

b )


where cits is the cost of sales scaled by the sales price in month t

and c it 2 1b is the cost of purchases scaled by the purchase price in

month t 2 1 The cost of purchases and sales include the com-missions and bid-ask spread components which are estimatedindividually for each trade as previously described Thus for asecurity purchased in month t 2 1 and sold in month t both c it

s

and c it 2 1b are positive for a security that was neither purchased

in month t 2 1 nor sold in month t both cits and c it 2 1

b are zeroBecause the timing and cost of purchases and sales vary acrosshouseholds the net return for security i in month t will varyacross households The net monthly portfolio return for eachhousehold is

Rhtnet 5 O

i= 1

sht

pitRitnet

(If only a portion of the beginning-of-month position in stock i waspurchased or sold the transaction cost is only applied to theportion that was purchased or sold)

We estimate the average gross and net monthly returnsearned by men as

RMtgr 5

1nmt

Oh= 1

nmt

Rhtgr and RMnet 5

1nmt

Oh= 1

nmt

Rhtnet

where nmt is the number of male households with common stockinvestment in month t There are analogous calculations forwomen

IIC Turnover

We calculate the monthly portfolio turnover for each house-hold as one-half the monthly sales turnover plus one-half themonthly purchase turnover9 In each month during our sampleperiod we identify the common stocks held by each household atthe beginning of month t from their position statement To cal-culate monthly sales turnover we match these positions to sales

9 Sell turnover for household h in month t is calculated as S i= 1sht pit min (1

SitHit) where Sit is the number of shares in security i sold during the month pitis the value of stock i held at the beginning of month t scaled by the total value ofstock holdings and H it is the number of shares of security i held at the beginningof month t Buy turnover is calculated as S i= 1

sht pit+ 1 min (1 BitHit+ 1) where Bitis the number of shares of security i bought during the month


during month t The monthly sales turnover is calculated as theshares sold times the beginning-of-month price per share dividedby the total beginning-of-month market value of the householdrsquosportfolio To calculate monthly purchase turnover we matchthese positions to purchases during month t 2 1 The monthlypurchase turnover is calculated as the shares purchased timesthe beginning-of-month price per share divided by the total be-ginning-of-month market value of the portfolio10

IID The Effect of Trading on Return Performance

We calculate an ldquoown-benchmarkrdquo abnormal return for indi-vidual investors that is similar in spirit to those proposed byLakonishok Shleifer and Vishny [1992] and Grinblatt and Tit-man [1993] In this abnormal return calculation the benchmarkfor household h is the month t return of the beginning-of-yearportfolio held by household h11 denoted Rht

b It represents thereturn that the household would have earned if it had merely heldits beginning-of-year portfolio for the entire year The gross or netown-benchmark abnormal return is the return earned by house-hold h less the return of household hrsquos beginning-of-year portfolio( ARht

gr = Rhtgr 2 Rht

b or ARhtnet = Rht

net 2 Rhtb ) If the household did

not trade during the year the own-benchmark abnormal returnwould be zero for all twelve months during the year

In each month the abnormal returns across male householdsare averaged yielding a 72-month time-series of mean monthlyown-benchmark abnormal returns Statistical signicance is cal-culated using t-statistics based on this time-series ARt

gr[ s ( ARt

gr) Ouml 72] where

ARtgr 5

1nmt

Ot= 1

nmt

(Rhtgr 2 Rht

b )

10 If more shares were sold than were held at the beginning of the month(because for example an investor purchased additional shares after the begin-ning of the month) we assume the entire beginning-of-month position in thatsecurity was sold Similarly if more shares were purchased in the precedingmonth than were held in the position statement we assume that the entireposition was purchased in the preceding month Thus turnover as we havecalculated it cannot exceed 100 percent in a month

11 When calculating this benchmark we begin the year on February 1 Wedo so because our rst monthly position statements are from the month end ofJanuary 1991 If the stocks held by a household at the beginning of the year aremissing CRSP returns data during the year we assume that stock is invested inthe remainder of the householdrsquos portfolio


There is an analogous calculation of net abnormal returns formen gross abnormal returns for women and net abnormal re-turns for women12

The advantage of the own-benchmark abnormal return mea-sure is that it does not adjust returns according to a particularrisk model No model of risk is universally accepted furthermoreit may be inappropriate to adjust investorsrsquo returns for stockcharacteristics that they do not associate with risk The own-benchmark measure allows each household to self-select the in-vestment style and risk prole of its benchmark (ie the portfolioit held at the beginning of the year) thus emphasizing the effecttrading has on performance

IIE Security Selection

Our theory says that men will underperform women becausemen trade more and trading is costly An alternative cause ofunderperformance is inferior security selection Two investorswith similar initial portfolios and similar turnover will differ inperformance if one consistently makes poor security selectionsTo measure security selection ability we compare the returns ofstocks bought with those of stocks sold

In each month we construct a portfolio comprised of thosestocks purchased by men in the preceding twelve months Thereturns on this portfolio in month t are calculated as

R tpm 5 O

i= 1

npt

TitpmRit Y O

i= 1

npt

Titpm

where T itpm is the aggregate value of all purchases by men in

security i from month t 2 12 through t 2 1 Rit is the grossmonthly return of stock i in month t and npt is the number ofdifferent stocks purchased from month t 2 12 through t 2 1(Alternatively we weight by the number rather than the value oftrades) Four portfolios are constructed one for the purchases ofmen (Rt

pm) one for the purchases of women (Rtpw) one for the

sales of men (Rtsm) and one for the sales of women (Rt

sw)

12 Alternatively one can rst calculate the monthly time-series averageown-benchmark return for each household and then test the signicance of thecross-sectional average of these The t-statistics for the cross-sectional tests arelarger than those we report for the time-series tests


III RESULTS

IIIA Men versus Women

In Table II Panel A we present position values and turnoverrates for the portfolios held by men and women Women holdslightly but not dramatically smaller common stock portfolios($18371 versus $21975) Of greater interest is the difference inturnover between women and men Models of overcondencepredict that women who are generally less overcondent thanmen will trade less than men The empirical evidence is consis-tent with this prediction Women turn their portfolios over ap-proximately 53 percent annually (monthly turnover of 44 percenttimes twelve) while men turn their portfolios over approximately77 percent annually (monthly turnover of 64 percent timestwelve) We are able to comfortably reject the null hypothesis thatturnover rates are similar for men and women (at less than a 1percent level) Although the median turnover is substantially lessfor both men and women the differences in the median levels ofturnover are also reliably different between genders

In Table II Panel B we present the gross and net percentagemonthly own-benchmark abnormal returns for common stockportfolios held by women and men Women earn gross monthlyreturns that are 0041 percent lower than those earned by theportfolio they held at the beginning of the year while men earngross monthly returns that are 0069 percent lower than thoseearned by the portfolio they held at the beginning of the yearBoth shortfalls are statistically signicant at the 1 percent levelas is their 0028 difference (034 percent annually)

Turning to net own-benchmark returns we nd that womenearn net monthly returns that are 0143 percent lower than thoseearned by the portfolio they held at the beginning of the yearwhile men earn net monthly returns that are 0221 percent lowerthan those earned by the portfolio they held at the beginning ofthe year Again both shortfalls are statistically signicant at the1 percent level as is their difference of 0078 percent (094 percentannually)

Are the lower own-benchmark returns earned by men due tomore active trading or to poor security selection The calculationsdescribed in subsection IIE indicate that the stocks both men andwomen choose to sell earn reliably greater returns than the stocksthey choose to buy This is consistent with Odean [1999] whouses different data to show that the stocks individual investors


TA

BL

EII

PO

SIT

ION

VA

LU

ET

UR

NO

VE

RA

ND

RE

TU

RN

PE

RF

OR

MA

NC

EO

FC

OM

MO

NS

TO

CK

INV

ES

TM

EN

TS

OF

FE

MA

LE

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eho

useh

olds

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

ren

ce(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

P

osit

ion

Val

ue

and

Tur

nove

rM

ean

[med

ian]

183

7121

975

23

604

17

754

222

932

453

9

196

5420

161

250

7

begi

nnin

gpo

siti

onva

lue

($)

[73

87]

[82

18]

[283

1]

[7

410

][8

175

][2

765]

[74

91]

[80

97]

[260

6]

Mea

n[m

edia

n]4

406

412

201

441

611

21

70

4

227

052

283

mon

thly

turn

over

()

[17

4][2

94]

[21

20]

[1

79]

[28

1][1

02]

[15

5][3

32]

[21

77]

Pan

elB

P

erfo

rman

ceO

wn-

benc

hmar

km

onth

ly2

004

1

20

069

0

028

2

005

0

20

068

0

018

20

029

20

074

0

045

abno

rmal

gros

sre

turn

()

(22

84)

(23

66)

(24

3)(2

289

)(2

367

)(1

28)

(21

64)

(23

60)

(25

3)

Ow

n-be

nchm

ark

mon

thly

20

143

2

022

1

007

8

20

154

2

021

4

006

0

20

121

2

024

2

012

0

abno

rmal

net

retu

rn(

)(2

970

)(2

108

3)(6

35)

(29

10)

(210

48)

(39

5)(2

668

)(2

111

5)(6

68)

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

T

ests

for

diff

eren

ces

inm

edia

ns

are

base

don

aW

ilcox

onsi

gn-r

ank

test

stat

isti

cH

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Beg

inni

ngpo

siti

onva

lue

isth

em

arke

tva

lue

ofco

mm

onst

ocks

held

inth

e

rst

mon

thth

atth

eho

use

hold

appe

ars

duri

ngou

rsa

mpl

epe

riod

M

ean

mon

thly

turn

over

isth

eav

erag

eof

sale

san

dpu

rch

ase

turn

over

[M

edia

nva

lues

are

inbr

acke

ts]

Ow

n-be

nch

mar

kab

norm

alre

turn

sar

eth

eav

erag

eh

ouse

hol

dpe

rcen

tage

mon

thly

abn

orm

alre

turn

calc

ula

ted

asth

ere

aliz

edm

onth

lyre

turn

for

ah

ouse

hol

dle

ssth

ere

turn

that

wou

ldha

vebe

enea

rned

ifth

eho

useh

old

had

held

the

begi

nnin

g-of

-yea

rpo

rtfo

lio

for

the

enti

reye

ar(i

e

the

twel

vem

onth

sbe

ginn

ing

Feb

ruar

y1)

T-s

tati

stic

sfo

rab

norm

alre

turn

sar

ein

pare

nthe

ses

and

are

calc

ulat

edu

sing

tim

e-se

ries

stan

dard

erro

rsac

ross

mon

ths


sell earn reliably greater returns than the stocks they buy Wend that the stocks men choose to purchase underperform thosethat they choose to sell by twenty basis points per month (t =2 279)13 The stocks women choose to purchase underperformthose they choose to sell by seventeen basis points per month (t =2 202) The difference in the underperformances of men andwomen is not statistically signicant (When we weight eachtrade equally rather than by its value menrsquos purchases under-perform their sales by 23 basis points per month and womenrsquospurchases underperform their sales by 22 basis points permonth) Both men and women detract from their returns (grossand net) by trading men simply do so more often

While not pertinent to our hypothesesmdashwhich predict thatovercondence leads to excessive trading and that this tradinghurts performancemdash one might want to compare the raw returnsof men with those of women During our sample period menearned average monthly gross and net returns of 1501 and 1325percent women earned average monthly gross and net returns of1482 and 1361 percent Menrsquos gross and net average monthlymarket-adjusted returns (the raw monthly return minus themonthly return on the CRSP value-weighted index) were 0081and 2 0095 percent womenrsquos gross and net average monthlymarket-adjusted returns were 0062 and 2 0059 percent14 Fornone of these returns are the differences between men andwomen statistically signicant The gross raw and market-ad-justed returns earned by men and women differed in part be-cause as we document in subsection IIID men tended to holdsmaller higher beta stocks than did women such stocks per-formed well in our sample period

In summary our main ndings are consistent with the twopredictions of the overcondence models First men who aremore overcondent than women trade more than women (asmeasured by monthly portfolio turnover) Second men lowertheir returns more through excessive trading than do women

13 This t-statistic is calculated as

t 5(Rt

pm 2 Rtsm)

s (Rtpm 2 Rt

sm) Icirc 72

14 The gross (net) annualized geometric mean returns earned by men andwomen were 187 (163) and 186 (169) percent respectively


Men lower their returns more than women because they trademore not because their security selections are worse

IIIB Single Men versus Single Women

If gender serves as a reasonable proxy for overcondence wewould expect the differences in portfolio turnover and net returnperformance to be larger between the accounts of single men andsingle women than between the accounts of married men andmarried women This is because as discussed above one spousemay make or inuence decisions for an account opened by theother To test this ancillary prediction we partition our sampleinto four groups married women married men single womenand single men Because we do not have marital status for allheads of households in our data set the total number of house-holds that we analyze here is less than that previously analyzedby about 4400

Position values and turnover rates of the portfolios held bythe four groups are presented in the last six columns of Table IIPanel A Married women tend to hold smaller common stockportfolios than married men these differences are smaller be-tween single men and single women Differences in turnover arelarger between single women and men than between marriedwomen and men thus conrming our ancillary prediction

In the last six columns of Table II Panel B we present thegross and net percentage monthly own-benchmark abnormal re-turns for common stock portfolios of the four groups The grossmonthly own-benchmark abnormal returns of single women( 2 0029) and of single men (2 0074) are statistically signicantat the 1 percent level as is their difference (0045mdashannually 054percent) We again stress that it is not the superior timing of thesecurity selections of women that leads to these gross returndifferences Men (and particularly single men) are simply morelikely to act (ie trade) despite their inferior ability

The net monthly own-benchmark abnormal returns of mar-ried women (2 0154) and married men ( 2 0214) are statisticallysignicant at the 1 percent level as is their difference (0060)The net monthly own-benchmark abnormal returns of singlewomen (2 0121) and of single men ( 2 0242) are statisticallysignicant at the 1 percent level as is their difference (0120mdashannually 14 percent) Single men underperform single women bysignicantly more than married men underperform marriedwomen (0120 2 060 = 060 t = 280)


In summary if married couples inuence each otherrsquos invest-ment decisions and thereby reduce the effects of gender differ-ences in overcondence then the results of this section are con-sistent with the predictions of the overcondence models Firstmen trade more than women and this difference is greatestbetween single men and women Second men lower their returnsmore through excessive trading than do women and this differ-ence is greatest between single men and women

IIIC Cross-Sectional Analysis of Turnover and Performance

Perhaps turnover and performance differ between men andwomen because gender correlates with other attributes that pre-dict turnover and performance We therefore consider severaldemographic characteristics known to affect nancial decision-making age marital status the presence of children in a house-hold and income

To assess whether the differences in turnover can be attrib-uted to these demographic characteristics we estimate a cross-sectional regression where the dependent variable is the observedaverage monthly turnover for each household The independentvariables in the regression include three dummy variables mari-tal status (one indicating single) gender (one indicating woman)and the presence of children (one indicating a household withchildren) In addition we estimate the interaction between mari-tal status and gender Finally we include the age of the personwho opened the account and household income Since our incomemeasure is truncated at $125000 we also include a dummyvariable if household income was greater than $12500015

We present the results of this analysis in column 2 of TableIII they support our earlier ndings The estimated dummyvariable on gender is highly signicant (t = 2 1276) and indi-cates that (ceteris paribus) the monthly turnover in marriedwomenrsquos accounts is 146 basis points less than in married menrsquosThe differences in turnover are signicantly more pronouncedbetween single women and single men ceteris paribus single

15 Average monthly turnover for each household is calculated for themonths during which common stock holdings are reported for that householdMarital status gender the presence of children age and income are from Info-basersquos records as of June 8 1997 Thus the dependent variable is observed beforethe independent variables This is also true for the cross-sectional tests reportedbelow


women trade 219 basis points (146 plus 73) less than single menOf the control variables we consider only age is signicantmonthly turnover declines by 31 basis points per decade thatwe age

We next consider whether our performance results can beexplained by other demographic characteristics To do so weestimate a cross-sectional regression in which the dependentvariable is the monthly own-benchmark abnormal net returnearned by each household The independent variables for the

TABLE IIICROSS-SECTIONAL REGRESSIONS OF TURNOVER OWN-BENCHMARK ABNORMAL

RETURN BETA AND SIZE FEBRUARY 1991 TO JANUARY 1997

Dependentvariable

Meanmonthlyturnover

()

Own-benchmarkabnormalnet return

Portfoliovolatility

Individualvolatility Beta

Sizecoefcient

Intercept 6269 2 0321 11466 11658 1226+ 0776(2 1147) (5885) (7098) (1144) (2216)

Single 0483 0002 0320 0330 0020 0079(424) (014) (340) (417) (212) (465)

Woman 2 1461 0058 2 0689 2 0682 2 0037 2 0136(2 1276) (427) (2 727) (2 854) (2 391) (2 800)

Single 3 2 0733 0027 2 0439 2 0540 2 0029 2 0138woman (2 338) (108) (2 245) (2 357) (2 160) (2 430)

Age10 2 0311 0002 2 0536 2 0393 2 0027 2 0055(2 926) (423) (2 1931) (2 1678) (2 955) ( 2 1100)

Children 2 0037 0008 2 0014 2 0051 2 0002 2 0008(2 040) (076) (2 019) (2 079) (2 022) (2 061)

Income 2 0002 00002 00003 0001 0003 00011000 (2 130) (133) (022) (138) (249) (031)

Income 2 00003 0027 0011 0012 2 0008 2 0018dummy (2 024) (154) (010) (011) (2 068) (2 082)

Adj R2 () 153 020 211 195 059 119

indicate signicantly different from zero at the 1 5 and 10 percent level respectively+ indicates signicantly different from one at the 1 percent levelEach regression is estimated using data from 26618 households The dependent variables are the mean

monthly percentage turnover for each household the mean monthly own-benchmark abnormal net return foreach household the portfolio volatility for each household the average volatility of the individual commonstocks held by each household estimated beta exposure for each household and estimated size exposure foreach household Own-benchmark abnormal net returns are calculated as the realized monthly return for ahousehold less the return that would have been earned if the household had held the beginning-of-yearportfolio for the entire year Portfolio volatility is the standard deviation of each householdrsquos monthly portfolioreturns Individual volatility is the average standard deviation of monthly returns over the previous threeyears for each stock in a householdrsquos portfolio The average is weighted equally across months and by positionsize within months The estimated exposures are the coefcient estimates on the independent variables fromtime-series regressions of the gross household excess return on the market excess return (Rmt 2 Rft) and azero-investment size portfolio (SMB t) Single is a dummy variable that takes a value of one if the primaryaccount holder (PAH) is single Woman is a dummy variable that takes a value of one if the primary accountholder is a woman Age is the age of the PAH Children is a dummy variable that takes a value of one if thehousehold has children Income is the income of the household and has a maximum value of $125000 WhenIncome is at this maximum Income dummy takes on a value of one (t-statistics are in parentheses)


regression are the same as those previously employed16 Theresults of this analysis presented in column 3 of Table III con-rm our earlier nding that men deduct more from their returnperformance by trading than do women The estimated dummyvariable on gender is highly signicant (t = 427) and indicatesthat (ceteris paribus) the monthly own-benchmark abnormal netreturn for married men is 58 basis points less than for marriedwomen The difference in the performance of single men andwomen 85 basis points a month (58 plus 27) is even greaterthan that of their married counterparts although not reliably soOf the control variables that we consider only age appears asstatistically signicant own-benchmark abnormal net returnsimprove by 02 basis points per decade that we age (The last fourcolumns of Table III are discussed in the following subsection)

IIID Portfolio Risk

In this subsection we estimate risk characteristics of thecommon stock investments of men and women Although not thecentral focus of our inquiry we believe the results that we presenthere are the rst to document that women tend to hold less riskypositions than men within their common stock portfolios Ouranalysis also provides additional evidence that men decreasetheir portfolio returns through trading more so than do women

We estimate market risk (beta) and the risk associated withsmall rms by estimating the following two-factor monthly time-series regression

(RMtgr 2 Rft) 5 a i 1 b i(Rmt 2 Rft) 1 s iSMBt 1 e it

where

Rft = the monthly return on T-Bills17

16 The statistical signicance of the results reported in this section shouldbe interpreted with caution On one hand the standard errors of the coefcientestimates are likely to be inated since the dependent variable is estimated witherror (Although we observe several months of each householdrsquos own-benchmarkreturns a householdrsquos observed own-benchmark returns may differ from itslong-run average) On the other hand these standard errors may be deatedsince different households may choose to trade in or out of the same securityresulting in cross-sectional dependence in the abnormal performance measureacross households We suspect that the problem of cross-sectional dependence isnot severe since the average household invests in only four securities

To reduce the undue inuence of a few households with position statementsfor only a few months we delete those households with less than three years ofpositions The tenor of our results is similar if we include these households

17 The return on T-bills is from Stocks Bonds Bills and Ination 1997Yearbook Ibbotson Associates Chicago IL


Rmt = the monthly return on a value-weighted market indexSMBt = the return on a value-weighted portfolio of small stocks

minus the return on a value-weighted portfolio of bigstocks18

a i = the interceptb i = the market betas i = coefcient of size risk ande it = the regression error term

The subscript i denotes parameter estimates and error termsfrom regression i where we estimate twelve regressions one eachfor the gross and net performances of the average man theaverage married man and the average single man and one eachfor the gross and net performance of the average woman theaverage married woman and the average single woman

In each regression the estimate of b i measures portfolio riskdue to covariance with the market portfolio The estimate of s i

measures risk associated with the size of the rms held in aportfolio a larger value of si denotes increased exposure to smallstocks Fama and French [1993] and Berk [1995] argue that rmsize is a proxy for risk19 Finally the intercept a i is an estimateof risk-adjusted return and thus provides an alternative perfor-mance measure to our own-benchmark abnormal return

The results of this analysis are presented in Table IV Thetime-series regressions of the gross average monthly excess re-turn earned by women and men on the market excess return anda size based zero-investment portfolio reveal that women holdless risky positions than men While relative to the total marketboth women and men tilt their portfolios toward high beta smallrms women do so less These regressions also conrm our nd-ing that men decrease their portfolio returns through tradingmore so than do women Men and women earn similar gross andnet returns however men do so by investing in smaller stocks

18 The construction of this portfolio is discussed in detail in Fama andFrench [1993] We thank Kenneth French for providing us with these data

19 Berk [1995] points out that systematic effects in returns are likely toappear in price since price is the value of future cash ows discounted by expectedreturn Thus size and the book-to-market ratio are likely to correlate withcross-sectional differences in expected returns Fama and French [1993] alsoclaim that size and the book-to-market ratio proxy for risk Not all authors agreethat book-to-market ratios are risk proxies (eg Lakonishok Shleifer and Vishny[1994]) Our qualitative results are unaffected by the inclusion of a book-to-market factor


TA

BL

EIV

RIS

KE

XP

OS

UR

ES

AN

DR

ISK

-AD

JUS

TE

DR

ET

UR

NS

OF

CO

MM

ON

ST

OC

KIN

VE

ST

ME

NT

SO

FF

EM

AL

E

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eh

ouse

hold

s

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

G

ross

aver

age

hou

seho

ldpe

rcen

tage

mon

thly

retu

rns

Tw

o-fa

ctor

mod

elin

terc

ept

20

044

20

083

003

92

005

12

008

20

031

20

036

20

099

006

3T

wo-

fact

orm

odel

coef

cie

ntes

tim

ate

on(R

mt

2R

ft)

105

0

108

1

20

031

1

053

1

075

0

022

103

5

108

8

005

3

Tw

o-fa

ctor

mod

elco

efc

ient

esti

mat

eon

SM

Bt

036

0

051

9

20

159

0

380

0

490

0

109

0

307

0

582

0

275

A

djus

ted

R2

938

920

653

934

921

528

944

915

706

Pan

elB

N

etav

erag

eho

useh

old

perc

enta

gem

onth

lyre

turn

s

Tw

o-fa

ctor

mod

elin

terc

ept

20

162

20

253

0

091

2

017

1

20

245

0

074

2

014

2

20

285

0

143

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

H

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Hou

seho

lds

are

clas

si

edas

mar

ried

orsi

ngle

base

don

the

mar

ital

stat

usof

the

head

ofho

use

hold

Coe

fci

ent

and

inte

rcep

tes

tim

ates

for

the

two-

fact

orm

odel

are

thos

efr

oma

tim

e-se

ries

regr

essi

onof

the

gros

s(n

et)

aver

age

hou

seho

ldex

cess

retu

rnon

the

mar

ket

exce

ssre

turn

(Rm

t2

Rf

t)

and

aze

ro-i

nve

stm

ent

size

port

foli

o(S

MB

t)

(RM

tgr

2R

ft)

=a

i+

bi(

Rm

t2

Rf

t)

+s i

SM

Bt

+e

it


with higher market risk20 The intercepts from the two-factorregressions of net returns (Table IV Panel B) suggest that aftera reasonable accounting for the higher market and size risks ofmenrsquos portfolios women earn net returns that are reliably higher(by nine basis points per month or 11 percent annually) thanthose earned by men21

Beta and size may not be the only two risk factors thatconcern individual investors These investors hold on averageonly four common stocks in their portfolios Those without othercommon stock or mutual fund holdings bear a great deal ofidiosyncratic risk To measure differences in the idiosyncraticrisk exposures of men and women we estimate the volatility oftheir common stock portfolios as well as the average volatility ofthe stocks they hold We calculate portfolio volatility as the stan-dard deviation of each householdrsquos monthly portfolio returns forthe months in which the household held common stocks Wecalculate the average volatility of the individual stocks they holdas the average standard deviation of monthly returns during theprevious three calendar years for each stock in a householdrsquosportfolio This average is weighted by position size within monthsand equally across months

To test whether men and women differ in the volatility oftheir portfolios and of the stocks they hold and to conrm thatthey differ in the market risk and size risk of their portfolios weestimate four additional cross-sectional regressions As in thecross-sectional regressions of turnover and own-benchmark re-turns the independent variables in these regressions includedummy variables for marital status gender the presence of chil-dren in the household and the interaction between marital statusand gender as well as variables for age and income and a dummy

20 During our sample period the mean monthly return on SMBt was sev-enteen basis points

21 Fama and French [1993] argue that the risk of common stock investmentscan be parsimoniously summarized as risk related to the market rm size and armrsquos book-to-market ratio When the return on a value-weighted portfolio of highbook-to-market stocks minus the return on a value-weighted portfolio of lowbook-to-market stocks (HMLt) is added as an independent variable to the two-factor monthly time-series regressions we nd that both men and women tilttheir portfolios toward high book-to-market stocks although men do more so Theintercepts from these regressions indicate that after accounting for the marketsize and book-to-market tilts of their portfolios women outperformed men bytwelve basis points a month (14 percent annually) Lyon Barber and Tsai [1999]document that intercept tests using the three-factor model are well specied inrandom samples and samples of large or small rms Thus the Fama-Frenchintercept tests account well for the small stock tilt of individual investors Duringour sample period the mean monthly return on HMLt was twenty basis points


variable for income over $125000 The dependent variable isalternately the volatility of each householdrsquos portfolio the aver-age volatility of the individual stocks held by each household thecoefcient on the market risk premium (ie beta) and the coef-cient on the size zero-investment portfolio Both coefcients areestimated from the two-factor model described above22

We present the results of these regressions in the last fourcolumns of Table III For all four risk measures (portfolio volatil-ity individual stock volatility beta and size) men invest inriskier positions than women Of the control variables that weconsider marital status age and income appear to be correlatedwith the riskiness of the stocks in which a household invests Theyoung and single hold more volatile portfolios composed of morevolatile stocks They are more willing to accept market risk and toinvest in small stocks Those with higher incomes are also morewilling to accept market risk These results are completely inkeeping with the common-sense notion that the young andwealthy with no dependents are willing to accept more invest-ment risk

The risk differences in the common stock portfolios held bymen and women are not surprising There is considerable evi-dence that men and women have different attitudes toward riskFrom survey responses of 5200 men and 6400 women BarskyJuster Kimball and Shapiro [1997] conclude that women aremore risk-averse than men Analyzing off-track betting slips for2000 men and 2000 women Bruce and Johnson [1994] nd thatmen take larger risks than women although they nd no evidenceof differences in performance Jianakoplos and Bernasek [1998]report that roughly 60 percent of the female respondents to the1989 Survey of Consumer Finances but only 40 percent of themen said they were not willing to take any nancial risks Kara-benick and Addy [1979] Sorrentino Hewitt and Raso-Knott[1992] and Zinkhan and Karande [1991] observe that men haveriskier preferences than women Flynn Slovic and Mertz [1994]Finucane Slovic Mertz Flynn and Sattereld [2000] and Finu-cane and Slovic [1999] nd that white men perceive a widevariety of risks as lower than do women and nonwhite menBajtelsmit and Bernasek [1996] Bajtelsmit and VanDerhei

22 These regressions are estimated for households with at least three yearsof available data Statistical inference might also be affected by measurementerror in the dependent variable and cross-sectional dependence in the risk mea-sures (see footnotes 11 and 12)


[1997] Hinz McCarthy and Turner [1997] and Sunden andSurette [1998] nd that men hold more of their retirement sav-ings in risky assets Jianakoplos and Bernasek [1998] report thesame for overall wealth Papke [1998] however nds in a sampleof near retirement women and their spouses that women do notinvest their pensions more conservatively than men

IV COMPETING EXPLANATIONS FOR DIFFERENCES

IN TURNOVER AND PERFORMANCE

IVA Risk Aversion

Since men and women differ in both overcondence and riskaversion it is natural to ask whether differences in risk aversionalone explain our ndings They do not While rational informedinvestors will trade more if they are less risk averse they will alsoimprove their performance by trading Thus if rational and in-formed men (and women) should improve their performance bytrading But both groups hurt their performance by trading Andmen do so more than women This outcome can be explained bydifferences in the overcondence of men and women and by dif-ferences in the risk aversion of overcondent men and women Itcannot be explained by differences in risk aversion alone

IVB Gambling

To what extent may gender differences in the propensity togamble explain the differences in turnover and returns that weobserve There are two aspects of gambling that we considerrisk-seeking and entertainment

Risk-seeking is when one demonstrates a preference for out-comes with greater variance but equal or lower expected returnIn equity markets the simplest way to increase variance withoutincreasing expected return is to underdiversify Excessive tradinghas a related but decidedly different effect it decreases expectedreturns without decreasing variance Thus risk-seeking may ac-count for underdiversication (although lack of diversicationcould also result from simple ignorance of its benets or fromovercondence) but it does not explain excessive trading

It may be that some men and to a lesser extent women tradefor entertainment They may enjoy placing trades that they ex-pect on average will lose money It is more likely that even thosewho enjoy trading believe overcondently that they have trading


ability This would be consistent with the Gallup Poll nding(reported in subsection IB) that both men and women expecttheir own portfolios to outperform the market but that menanticipate a greater outperformance

Some investors may set aside a small portion of their wealthto trade for entertainment while investing the majority moreprudently If ldquoentertainment accountsrdquo are driving our ndingswe would expect turnover and underperformance to decline as thecommon stocks in the accounts we observe represent a largerproportion of each householdrsquos total wealth We nd howeverthat this is not the case

Approximately one-third of our households reported their networth at the time they opened their accounts We calculate theproportion of net worth invested in the common stock portfolioswe observe as the beginning value of a householdrsquos common stockinvestments scaled by its self-reported net worth23 We then ana-lyze the turnover and investment performance of 2333 house-holds with at least 50 percent of their net worth invested incommon stock at this brokerage These households have similarturnover (625 percent per month24 75 percent annually) to ourfull sample (Table II) Furthermore these households earn grossand net returns that are very similar to the full sample

If households trade actively solely for entertainment then itis likely that the households that trade most actively nd tradingmost entertaining How much does active trading cost thesehouseholds compared with their other household expenses Toestimate the cost of trading for the 20 percent of households in oursample that trade most actively we calculate the monthly dollarloss as the own-benchmark abnormal return times the beginningof month position value This monthly dollar loss is averagedacross months for each household and multiplied by twelve toobtain an average annual cost of trading The average annual costof trading for the quintile of most active traders is $2849 TheDepartment of Labor Consumer Expenditure Survey reports onvarious categories of household expenditures The Survey parti-tions households into quintiles based on household income Theaverage annual income ($74000)25 of the quintile of most actively

23 This estimate is upwardly biased because the account opening dategenerally precedes our rst portfolio position observation and net worth is likelyto have increased in the interim

24 The standard error of the mean monthly turnover is 0225 This average annual income is somewhat understated since in calculating


trading households in our sample corresponds most closely to thatof the mean income ($92523) of the top quintile of the 1996Survey The active traders in our sample spent on average 39percent of their annual income on trading costs while comparablehouseholds in the Survey spent 33 percent of their income onutilities 26 percent on health care and 40 percent on all enter-tainment expenses including fees admissions television radioand sound equipment pets toys and playground equipment Ifthe active traders in our sample are trading solely for entertain-ment they must nd it very entertaining indeed

For mutual funds as for individuals turnover has a negativeimpact on returns [Carhart 1997]26 Some mutual fund managersmay actively trade and thereby knowingly reduce their fundsrsquoexpected returns simply to create the illusion that they areproviding a valuable service [Dow and Gorton 1994] If the ma-jority of active managers believe that they offer only disservice totheir clients this is a cynical industry indeed We propose thatmost mutual fund managers while aware that active manage-ment on average detracts value believe that their personal abil-ity to manage is above average Thus they are motivated to tradeby overcondence not cynicism

Individuals may trade for entertainment Mutual fund man-agers may trade to appear busy It is unlikely that most individ-uals churn their accounts to appear busy or that most fundmanagers trade for fun Overcondence offers a simple explana-tion for the high trading activity of both groups

V CONCLUSION

Modern nancial economics assumes that we behave withextreme rationality but we do not Furthermore our deviationsfrom rationality are often systematic Behavioral nance relaxesthe traditional assumptions of nancial economics by incorporat-ing these observable systematic and very human departuresfrom rationality into standard models of nancial markets Over-condence is one such departure Models that assume market

average household income we treat households in the over $125000 category asif their incomes were $125000 These account for less than 12 percent of thehouseholds for which we have income data

26 Lakonishok Shleifer and Vishny [1992] report a positive relation be-tween turnover and performance for 769 all-equity pension funds although thisnding puzzles the authors


participants are overcondent yield one central prediction over-condent investors will trade too much

We test this prediction by partitioning investors on the basisof a variable that provides a natural proxy for overcondencemdashgender Psychological research has established that men aremore prone to overcondence than women particularly so inmale-dominated realms such as nance Rational investors tradeonly if the expected gains exceed transactions costs Overcon-dent investors overestimate the precision of their information andthereby the expected gains of trading They may even trade whenthe true expected net gains are negative Models of investorovercondence predict that since men are more overcondentthan women men will trade more and perform worse thanwomen

Our empirical tests provide strong support for the behavioralnance model Men trade more than women and thereby reducetheir returns more so than do women Furthermore these differ-ences are most pronounced between single men and singlewomen

Individuals turn over their common stock investments about70 percent annually [Barber and Odean 2000] Mutual fundshave similar turnover rates [Carhart 1997] Yet those individu-als and mutual funds that trade most earn the lowest returns Webelieve that there is a simple and powerful explanation for thehigh levels of counterproductive trading in nancial marketsovercondence

UNIVERSITY OF CALIFORNIA DAVIS

REFERENCES

Alpert Marc and Howard Raiffa ldquoA Progress Report on the Training of Proba-bility Assessorsrdquo in Judgment Under Uncertainty Heuristics and BiasesDaniel Kahneman Paul Slovic and Amos Tversky eds (Cambridge and NewYork Cambridge University Press 1982) pp 294ndash305

Bajtelsmit Vickie L and Alexandra Bernasek ldquoWhy Do Women Invest Differ-ently than Menrdquo Financial Counseling and Planning VII (1996) 1ndash10

Bajtelsmit Vickie L and Jack L VanDerhie ldquoRisk Aversion and Pension Invest-ment Choicesrdquo in Positioning Pensions for the Twenty-rst Century MichaelS Gordon Olivia S Mitchell and Marc M Twinney eds (PhiladelphiaUniversity of Pennsylvania Press 1997) pp 45ndash65

Barber Brad M and Terrance Odean ldquoTrading Is Hazardous to Your WealthThe Common Stock Investment Performance of Individual Investorsrdquo Jour-nal of Finance LV (2000) 773ndash806

Barsky Robert B F Thomas Juster Miles S Kimball and Matthew D ShapiroldquoPreference Parameters and Behavioral Heterogeneity An Experimental Ap-proach in the Health and Retirement Studyrdquo Quarterly Journal of Econom-ics CXII (1997) 537ndash579


Baumann Andrea O Raisa B Deber and Gail G Thompson ldquoOvercondenceamong Physicians and Nurses The lsquoMicro-Certainty Macro-UncertaintyrsquoPhenomenonrdquo Social Science and Medicine XXXII (1991) 167ndash174

Benos Alexandros V ldquoOvercondent Speculators in Call Markets Trade Pat-terns and Survivalrdquo Journal of Financial Markets I (1998) 353ndash383

Berk Jonathan ldquoA Critique of Size Related Anomaliesrdquo Review of FinancialStudies VIII (1995) 275ndash286

Beyer Sylvia ldquoGender Differences in the Accuracy of Self-Evaluations of Perfor-mancerdquo Journal of Personality and Social Psychology LIX (1990) 960ndash970

Beyer Sylvia and Edward M Bowden ldquoGender Differences in Self-PerceptionsConvergent Evidence from Three Measures of Accuracy and Biasrdquo Personal-ity and Social Psychology Bulletin XXIII (1997) 157ndash172

Bruce Alistair C and Johnnie E Johnson ldquoMale and Female Betting BehaviourNew Perspectivesrdquo Journal of Gambling Studies X (1994) 183ndash198

Caballe Jordi and Jozsef Sakovics ldquoOvercondent Speculation with ImperfectCompetitionrdquo working paper Universitat Autonoma de Barcelona Spain1998

Carhart Mark M ldquoOn Persistence in Mutual Fund Performancerdquo Journal ofFinance LII (1997) 57ndash82

Christensen-Szalanski Jay J and James B Bushyhead ldquoPhysiciansrsquo Use ofProbabilistic Information in a Real Clinical Settingrdquo Journal of ExperimentalPsychology Human Perception and Performance VII (1981) 928ndash935

Cooper Arnold C Carolyn Y Woo and William C Dunkelberg ldquoEntrepreneursrsquoPerceived Chances for Successrdquo Journal of Business Venturing III (1988)97ndash108

Daniel Kent David Hirshleifer and Avanidar Subrahmanyam ldquoInvestor Psy-chology and Security Market Under- and Overreactionsrdquo Journal of FinanceLIII (1998) 1839ndash1885

Deaux Kay and Tim Emswiller ldquoExplanations of Successful Performance onSex-Linked Tasks What Is Skill for the Male is Luck for the Femalerdquo Journalof Personality and Social Psychology XXIX (1974) 80ndash85

Deaux Kay and Elizabeth Farris ldquoAttributing Causes for Onersquos Own Perfor-mance The Effects of Sex Norms and Outcomerdquo Journal of Research inPersonality XI (1977) 59ndash72

De Long J Bradford Andrei Shleifer Lawrence H Summers and Robert JWaldmann ldquoThe Survival of Noise Traders in Financial Marketsrdquo Journal ofBusiness LXIV (1991) 1ndash19

Dow James and Gary Gorton ldquoNoise Trading Delegated Portfolio Managementand Economic Welfarerdquo Journal of Political Economy CV (1994) 1024ndash1050

Fama Eugene F and Kenneth R French ldquoCommon Risk Factors in Returns onStocks and Bondsrdquo Journal of Financial Economics XXXIII (1993) 3ndash56

Finucane Melissa and Paul Slovic ldquoRisk and the White Male A Perspective onPerspectivesrdquo Framtider XVIII (1999) 24ndash29

Finucane Melissa Paul Slovic C K Mertz James Flynn and Theresa Satter-eld ldquoGender Race and Perceived Risk The lsquoWhite Malersquo Effectrdquo HealthRisk and Society II (2000) 159ndash179

Fischhoff Baruch Paul Slovic and Sarah Lichtenstein ldquoKnowing with CertaintyThe Appropriateness of Extreme Condencerdquo Journal of Experimental Psy-chology III (1977) 552ndash564

Flynn James Paul Slovic and C K Mertz ldquoGender Race and Perception ofEnvironmental Health Risksrdquo Risk Analysis XIV (1994) 1101ndash1108

Frank Jerome D ldquoSome Psychological Determinants of the Level of AspirationrdquoAmerican Journal of Psychology XLVII (1935) 285ndash293

Gervais Simon and Terrance Odean ldquoLearning to Be Overcondentrdquo workingpaper Wharton School University of Pennsylvania 1998

Grifn Dale and Amos Tversky ldquoThe Weighing of Evidence and the Determi-nants of Condencerdquo Cognitive Psychology XXIV (1992) 411ndash435

Grinblatt Mark and Sheridan Titman ldquoPerformance Measurement withoutBenchmarks An Examination of Mutual Fund Returnsrdquo Journal of BusinessLXVI (1993) 47ndash68

Grossman Sanford J and Joseph E Stiglitz ldquoOn the Impossibility of Informa-


tionally Efcient Marketsrdquo American Economic Review LXX (1980)393ndash408

Harris Milton and Artur Raviv ldquoDifferences of Opinion Make a Horse RacerdquoReview of Financial Studies VI (1993) 473ndash506

Hinz Richard P David D McCarthy and John A Turner ldquoAre Women Conser-vative Investors Gender Differences in Participant-Directed Pension Invest-mentsrdquo in Positioning Pensions for the Twenty-rst Century Michael SGordon Olivia S Mitchell and Marc M Twinney eds (Philadelphia Uni-versity of Pennsylvania Press 1997) pp 91ndash103

Jianakoplos Nancy A and Alexandra Bernasek ldquoAre Women More Risk AverserdquoEconomic Inquiry XXXVI (1998) 620ndash630

Karabenick Stuart A and Milton M Addy ldquoLocus of Control and Sex Differencesin Skill and Chance Risk-Taking Conditionsrdquo Journal of General PsychologyC (1979) 215ndash228

Kidd John B ldquoThe Utilization of Subjective Probabilities in Production Plan-ningrdquo Acta Psychologica XXXIV (1970) 338ndash347

Kyle Albert S and F Albert Wang ldquoSpeculation Duopoly with Agreement toDisagree Can Overcondence Survive the Market Testrdquo Journal of FinanceLII (1997) 2073ndash2090

Lakonishok Josef Andrei Shleifer and Robert W Vishny ldquoThe Structure andPerformance of the Money Management Industryrdquo in Martin Neil Baily andClifford Winston eds Brookings Papers on Economic Activity Microeconom-ics (Washington DC Brookings Institution 1992)

Lakonishok Josef Andrei Shleifer and Robert W Vishny ldquoContrarian Invest-ment Extrapolation and Riskrdquo Journal of Finance LIX (1994) 1541ndash1578

Lenney Ellen ldquoWomenrsquos Self-Condence in Achievement Settingsrdquo PsychologicalBulletin LXXXIV (1977) 1ndash13

Lewellen Wilber G Ronald C Lease Gary G Schlarbaum ldquoPatterns of Invest-ment Strategy and Behavior among Individual Investorsrdquo Journal of Busi-ness L (1977) 296ndash333

Lichtenstein Sarah and Baruch Fischhoff ldquoThe Effects of Gender and Instruc-tions on Calibrationrdquo Decision Research Report 81-5 Eugene OR DecisionResearch 1981

Lichtenstein Sarah Baruch Fischhoff and Lawrence Phillips ldquoCalibration ofProbabilities The State of the Art to 1980rdquo in Judgment Under UncertaintyHeuristics and Biases Daniel Kahneman Paul Slovic and Amos Tverskyeds (Cambridge and New York Cambridge University Press 1982)

Lundeberg Mary A Paul W Fox Judith Punccohar ldquoHighly Condent butWrong Gender Differences and Similarities in Condence Judgmentsrdquo Jour-nal of Educational Psychology LXXXVI (1994) 114ndash121

Lyon John D Brad M Barber and Chih-Ling Tsai ldquoImproved Methods for Testsof Long-Run Abnormal Stock Returnsrdquo Journal of Finance LIV (1999)165ndash201

Meehan Anita M and Willis F Overton ldquoGender Differences in Expectancies forSuccess and Performance on Piagetian Spatial Tasksrdquo Merrill-Palmer Quar-terly XXXII (1986) 427ndash441

Neale Margaret A and Max H Bazerman Cognition and Rationality in Nego-tiation (New York The Free Press 1990)

Odean Terrance ldquoVolume Volatility Price and Prot When All Traders Areabove Averagerdquo Journal of Finance LIII (1998) 1887ndash1934

mdashmdash ldquoDo Investors Trade Too Muchrdquo American Economic Review LXXXIX(1999) 1279ndash1298

Oskamp Stuart ldquoOvercondence in Case-Study Judgmentsrdquo Journal of Consult-ing Psychology XXIX (1965) 261ndash265

Papke Leslie E ldquoIndividual Financial Decisions in Retirement Savings PlansThe Role of Participant-Directionrdquo working paper Michigan State Univer-sity 1998

Prince Melvin ldquoWomen Men and Money Stylesrdquo Journal of Economic Psychol-ogy XIV (1993) 175ndash182

Russo J Edward and Paul J H Schoemaker ldquoManaging Overcondencerdquo SloanManagement Review XXXIII (1992) 7ndash17

Sorrentino Richard M Erin C Hewitt and Patricia A Raso-Knott ldquoRisk-Taking


in Games of Chance and Skill Information and Affective Inuences on ChoiceBehaviorrdquo Journal of Personality and Social Psychology LXII (1992)522ndash533

Stael von Holstein Carl-Axel S ldquoProbabilistic Forecasting An Experiment Re-lated to the Stock Marketrdquo Organizational Behavior and Human Perfor-mance VIII (1972) 139ndash158

Sunden Annika E and Brian J Surette ldquoGender Differences in the Allocation ofAssets in Retirement Savings Plansrdquo working paper Board of Governors ofthe Federal Reserve System 1998

Varian Hal R ldquoDifferences of Opinion in Financial Marketsrdquo Financial RiskTheory Evidence and Implications Courtenay C Stone ed (Proceedings ofthe Eleventh Annual Economic Policy conference of the Federal Reserve Bankof St Louis Boston 1989)

Wagenaar Willem and Gideon B Keren ldquoDoes the Expert Know The Reliabilityof Predictions and Condence Ratings of Expertsrdquo in Intelligent DecisionSupport in Process Environments Erik Hollnagel Giuseppe Mancini andDavid D Woods eds (Berlin Springer 1986)

Yates J Frank Judgment and Decision Making (Englewood Cliffs NJ PrenticeHall 1990)

Zinkhan George M and Kiran W Karande ldquoCultural and Gender Differences inRisk-Taking Behavior among American and Spanish Decision Makersrdquo Jour-nal of Social Psychology CXXXI (1991) 741ndash742


ismdashtrade more than rational investors and that doing so lowerstheir expected utilities Greater overcondence leads to greatertrading and to lower expected utility

A direct test of whether overcondence contributes to exces-sive market trading is to separate investors into those more andthose less prone to overcondence One can then test whethermore overcondence leads to more trading and to lower returnsSuch a test is the primary contribution of this paper

Psychologists nd that in areas such as nance men are moreovercondent than women This difference in overcondenceyields two predictions men will trade more than women and theperformance of men will be hurt more by excessive trading thanthe performance of women To test these hypotheses we partitiona data set of position and trading records for over 35000 house-holds at a large discount brokerage rm into accounts opened bymen and accounts opened by women Consistent with the predic-tions of the overcondence models we nd that the averageturnover rate of common stocks for men is nearly one and a halftimes that for women While both men and women reduce theirnet returns through trading men do so by 094 percentage pointsmore a year than do women

The differences in turnover and return performance are evenmore pronounced between single men and single women Singlemen trade 67 percent more than single women thereby reducingtheir returns by 144 percentage points per year more than dosingle women

The remainder of this paper is organized as follows Wemotivate our test of overcondence in Section I We discuss ourdata and empirical methods in Section II Our main results arepresented in Section III We discuss competing explanationsfor our results in Section IV and make concluding remarks inSection V

I A TEST OF OVERCONFIDENCE

IA Overcondence and Trading on Financial Markets

Studies of the calibration of subjective probabilities nd thatpeople tend to overestimate the precision of their knowledge[Alpert and Raiffa 1982 Fischhoff Slovic and Lichtenstein1977] see Lichtenstein Fischhoff and Phillips [1982] for a re-view of the calibration literature Such overcondence has been


























II DATA AND METHODS












TA

BL

EI

DE

SC

RIP

TIV

ES

TA

TIS

TIC

SF

OR

DE

MO

GR

AP

HIC

SO

FF

EM

AL

EA

ND

MA

LE

HO

US

EH

OL

DS

Var

iabl

e

All

hous

ehol

dsM

arri

edho

use

hold

sS

ingl

eho

useh

olds

Wom

enM

en

Dif

fere

nce

(wom

enndash

men

)W

omen

Men

Dif

fere

nce

(wom

enndash

men

)W

omen

Men

Dif

fere

nce

(wom

enmdash

men

)

Pan

elA

In

foba

seda

ta

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Per

cen

tage

mar

ried

680

757

27

7P

erce

nta

gew

ith

chil

dren

252

322

27

033

640

42

68

106

105

01

Mea

nag

e50

950

30

649

951

12

12

530

482

48

Med

ian

age

480

480

00

480

480

00

500

460

40

Mea

nin

com

e($

000)

730

756

22

681

279

61

656

762

82

61

w

ith

inco

me

gt$1

250

0011

211

72

05

142

130

12

59

74

21

5

Pan

elB

Sel

f-re

port

edda

ta

Nu

mbe

rof

hou

seho

lds

263

711

226

170

77

700

652

218

4N

etw

orth

($00

0)90

thP

erce

ntil

e50

00

500

00

050

00

500

00

035

00

450

02

100

075

thP

erce

ntil

e20

00

250

02

500

250

025

00

00

175

020

00

225

0M

edia

n10

00

100

00

010

00

100

00

010

00

100

00

025

thP

erce

ntil

e60

074

52

145

625

745

212

040

062

02

220

10th

Per

cent

ile

270

370

210

035

037

02

20

200

350

215

0E

quit

yto

net

wor

th(

)M

ean

133

132

01

129

129

00

144

143

01

Med

ian

67

67

00

63

66

20

37

97

40

5In

vest

men

tex

peri

ence

()

Non

e5

43

42

04

73

41

37

43

04

4L

imit

ed46

834

112

744

934

210

752

633

319

3G

ood

391

485

29

440

848

52

77

333

488

215

5E

xten

sive

87

140

25

39

613

92

43

67

149

28

2

The

sam

ple

con

sist

sof

hous

ehol

dsw

ith

com

mon

stoc

kin

vest

men

tat

ala

rge

disc

oun

tbr

oker

age

rm

for

whi

chw

ear

eab

leto

iden

tify

the

gend

erof

the

pers

onw

hoop

ened

the

hous

ehol

drsquos

rs

tac

coun

tD

ata

onm

arit

alst

atus

ch

ildr

en

age

and

inco

me

are

from

Info

base

Inc

asof

June

1997

S

elf-

repo

rted

data

are

info

rmat

ion

supp

lied

toth

edi

scou

ntbr

oker

age

rm

atth

eti

me

the

acco

unt

isop

ened

byth

epe

rson

onop

enin

gth

eac

coun

tIn

com

eis

repo

rted

wit

hin

eigh

tra

nges

whe

reth

eto

pra

nge

isgr

eate

rth

an$1

250

00W

eca

lcul

ate

mea

nsus

ing

the

mid

poin

tof

each

ran

gean

d$1

250

00fo

rth

eto

pra

nge

Equ

ity

toN

etW

orth

()i

sth

epr

opor

tion

ofth

em

arke

tva

lue

ofco

mm

onst

ock

inve

stm

ent

atth

isdi

scou

ntbr

oker

age

rm

asof

Janu

ary

1991

toto

tals

elf-

repo

rted

net

wor

thw

hen

the

hou

seho

ldop

ened

its

rst

acco

unt

atth

isbr

oker

age

Tho

seh

ouse

hold

sw

ith

apr

opor

tion

equ

ity

tone

tw

orth

grea

ter

than

100

perc

ent

are

dele

ted

wh

enca

lcul

atin

gm

ean

san

dm

edia

ns

Nu

mbe

rof

obse

rvat

ion

sfo

rea

chva

riab

leis

slig

htl

yle

ssth

anth

en

umbe

rof

repo

rted

hous

ehol

ds












sprds 5 S Pds

cl

Pds


cl

Pdb

b 2 1 D Pds

cl and Pdb


s and Pdbb are the











gr)is calculated as

Rhtgr 5 O

i= 1

sht

pitRitgr




net) as

(1 1 Ritnet) 5 (1 1 Rit

gr) (1 2 cits )(1 1 c it 2 1

b )





s




Rhtnet 5 O

i= 1

sht

pitRitnet



RMtgr 5

1nmt

Oh= 1

nmt

Rhtgr and RMnet 5

1nmt

Oh= 1

nmt

Rhtnet


IIC Turnover










gr = Rhtgr 2 Rht

b or ARhtnet = Rht




gr[ s ( ARt

gr) Ouml 72] where

ARtgr 5

1nmt

Ot= 1

nmt

(Rhtgr 2 Rht

b )









R tpm 5 O

i= 1

npt

TitpmRit Y O

i= 1

npt

Titpm





sw)



III RESULTS







TA

BL

EII

PO

SIT

ION

VA

LU

ET

UR

NO

VE

RA

ND

RE

TU

RN

PE

RF

OR

MA

NC

EO

FC

OM

MO

NS

TO

CK

INV

ES

TM

EN

TS

OF

FE

MA

LE

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eho

useh

olds

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

ren

ce(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

P

osit

ion

Val

ue

and

Tur

nove

rM

ean

[med

ian]

183

7121

975

23

604

17

754

222

932

453

9

196

5420

161

250

7

begi

nnin

gpo

siti

onva

lue

($)

[73

87]

[82

18]

[283

1]

[7

410

][8

175

][2

765]

[74

91]

[80

97]

[260

6]

Mea

n[m

edia

n]4

406

412

201

441

611

21

70

4

227

052

283

mon

thly

turn

over

()

[17

4][2

94]

[21

20]

[1

79]

[28

1][1

02]

[15

5][3

32]

[21

77]

Pan

elB

P

erfo

rman

ceO

wn-

benc

hmar

km

onth

ly2

004

1

20

069

0

028

2

005

0

20

068

0

018

20

029

20

074

0

045

abno

rmal

gros

sre

turn

()

(22

84)

(23

66)

(24

3)(2

289

)(2

367

)(1

28)

(21

64)

(23

60)

(25

3)

Ow

n-be

nchm

ark

mon

thly

20

143

2

022

1

007

8

20

154

2

021

4

006

0

20

121

2

024

2

012

0

abno

rmal

net

retu

rn(

)(2

970

)(2

108

3)(6

35)

(29

10)

(210

48)

(39

5)(2

668

)(2

111

5)(6

68)

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

T

ests

for

diff

eren

ces

inm

edia

ns

are

base

don

aW

ilcox

onsi

gn-r

ank

test

stat

isti

cH

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Beg

inni

ngpo

siti

onva

lue

isth

em

arke

tva

lue

ofco

mm

onst

ocks

held

inth

e

rst

mon

thth

atth

eho

use

hold

appe

ars

duri

ngou

rsa

mpl

epe

riod

M

ean

mon

thly

turn

over

isth

eav

erag

eof

sale

san

dpu

rch

ase

turn

over

[M

edia

nva

lues

are

inbr

acke

ts]

Ow

n-be

nch

mar

kab

norm

alre

turn

sar

eth

eav

erag

eh

ouse

hol

dpe

rcen

tage

mon

thly

abn

orm

alre

turn

calc

ula

ted

asth

ere

aliz

edm

onth

lyre

turn

for

ah

ouse

hol

dle

ssth

ere

turn

that

wou

ldha

vebe

enea

rned

ifth

eho

useh

old

had

held

the

begi

nnin

g-of

-yea

rpo

rtfo

lio

for

the

enti

reye

ar(i

e

the

twel

vem

onth

sbe

ginn

ing

Feb

ruar

y1)

T-s

tati

stic

sfo

rab

norm

alre

turn

sar

ein

pare

nthe

ses

and

are

calc

ulat

edu

sing

tim

e-se

ries

stan

dard

erro

rsac

ross

mon

ths






t 5(Rt

pm 2 Rtsm)

s (Rtpm 2 Rt

sm) Icirc 72





















Dependentvariable

Meanmonthlyturnover

()


Portfoliovolatility


Sizecoefcient

Intercept 6269 2 0321 11466 11658 1226+ 0776(2 1147) (5885) (7098) (1144) (2216)

Single 0483 0002 0320 0330 0020 0079(424) (014) (340) (417) (212) (465)

Woman 2 1461 0058 2 0689 2 0682 2 0037 2 0136(2 1276) (427) (2 727) (2 854) (2 391) (2 800)

Single 3 2 0733 0027 2 0439 2 0540 2 0029 2 0138woman (2 338) (108) (2 245) (2 357) (2 160) (2 430)

Age10 2 0311 0002 2 0536 2 0393 2 0027 2 0055(2 926) (423) (2 1931) (2 1678) (2 955) ( 2 1100)

Children 2 0037 0008 2 0014 2 0051 2 0002 2 0008(2 040) (076) (2 019) (2 079) (2 022) (2 061)

Income 2 0002 00002 00003 0001 0003 00011000 (2 130) (133) (022) (138) (249) (031)

Income 2 00003 0027 0011 0012 2 0008 2 0018dummy (2 024) (154) (010) (011) (2 068) (2 082)

Adj R2 () 153 020 211 195 059 119





IIID Portfolio Risk




where
















TA

BL

EIV

RIS

KE

XP

OS

UR

ES

AN

DR

ISK

-AD

JUS

TE

DR

ET

UR

NS

OF

CO

MM

ON

ST

OC

KIN

VE

ST

ME

NT

SO

FF

EM

AL

E

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eh

ouse

hold

s

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

G

ross

aver

age

hou

seho

ldpe

rcen

tage

mon

thly

retu

rns

Tw

o-fa

ctor

mod

elin

terc

ept

20

044

20

083

003

92

005

12

008

20

031

20

036

20

099

006

3T

wo-

fact

orm

odel

coef

cie

ntes

tim

ate

on(R

mt

2R

ft)

105

0

108

1

20

031

1

053

1

075

0

022

103

5

108

8

005

3

Tw

o-fa

ctor

mod

elco

efc

ient

esti

mat

eon

SM

Bt

036

0

051

9

20

159

0

380

0

490

0

109

0

307

0

582

0

275

A

djus

ted

R2

938

920

653

934

921

528

944

915

706

Pan

elB

N

etav

erag

eho

useh

old

perc

enta

gem

onth

lyre

turn

s

Tw

o-fa

ctor

mod

elin

terc

ept

20

162

20

253

0

091

2

017

1

20

245

0

074

2

014

2

20

285

0

143

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

H

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Hou

seho

lds

are

clas

si

edas

mar

ried

orsi

ngle

base

don

the

mar

ital

stat

usof

the

head

ofho

use

hold

Coe

fci

ent

and

inte

rcep

tes

tim

ates

for

the

two-

fact

orm

odel

are

thos

efr

oma

tim

e-se

ries

regr

essi

onof

the

gros

s(n

et)

aver

age

hou

seho

ldex

cess

retu

rnon

the

mar

ket

exce

ssre

turn

(Rm

t2

Rf

t)

and

aze

ro-i

nve

stm

ent

size

port

foli

o(S

MB

t)

(RM

tgr

2R

ft)

=a

i+

bi(

Rm

t2

Rf

t)

+s i

SM

Bt

+e

it
















IVA Risk Aversion


IVB Gambling















V CONCLUSION










REFERENCES


























































































II DATA AND METHODS












TA

BL

EI

DE

SC

RIP

TIV

ES

TA

TIS

TIC

SF

OR

DE

MO

GR

AP

HIC

SO

FF

EM

AL

EA

ND

MA

LE

HO

US

EH

OL

DS

Var

iabl

e

All

hous

ehol

dsM

arri

edho

use

hold

sS

ingl

eho

useh

olds

Wom

enM

en

Dif

fere

nce

(wom

enndash

men

)W

omen

Men

Dif

fere

nce

(wom

enndash

men

)W

omen

Men

Dif

fere

nce

(wom

enmdash

men

)

Pan

elA

In

foba

seda

ta

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Per

cen

tage

mar

ried

680

757

27

7P

erce

nta

gew

ith

chil

dren

252

322

27

033

640

42

68

106

105

01

Mea

nag

e50

950

30

649

951

12

12

530

482

48

Med

ian

age

480

480

00

480

480

00

500

460

40

Mea

nin

com

e($

000)

730

756

22

681

279

61

656

762

82

61

w

ith

inco

me

gt$1

250

0011

211

72

05

142

130

12

59

74

21

5

Pan

elB

Sel

f-re

port

edda

ta

Nu

mbe

rof

hou

seho

lds

263

711

226

170

77

700

652

218

4N

etw

orth

($00

0)90

thP

erce

ntil

e50

00

500

00

050

00

500

00

035

00

450

02

100

075

thP

erce

ntil

e20

00

250

02

500

250

025

00

00

175

020

00

225

0M

edia

n10

00

100

00

010

00

100

00

010

00

100

00

025

thP

erce

ntil

e60

074

52

145

625

745

212

040

062

02

220

10th

Per

cent

ile

270

370

210

035

037

02

20

200

350

215

0E

quit

yto

net

wor

th(

)M

ean

133

132

01

129

129

00

144

143

01

Med

ian

67

67

00

63

66

20

37

97

40

5In

vest

men

tex

peri

ence

()

Non

e5

43

42

04

73

41

37

43

04

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imit

ed46

834

112

744

934

210

752

633

319

3G

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391

485

29

440

848

52

77

333

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215

5E

xten

sive

87

140

25

39

613

92

43

67

149

28

2

The

sam

ple

con

sist

sof

hous

ehol

dsw

ith

com

mon

stoc

kin

vest

men

tat

ala

rge

disc

oun

tbr

oker

age

rm

for

whi

chw

ear

eab

leto

iden

tify

the

gend

erof

the

pers

onw

hoop

ened

the

hous

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drsquos

rs

tac

coun

tD

ata

onm

arit

alst

atus

ch

ildr

en

age

and

inco

me

are

from

Info

base

Inc

asof

June

1997

S

elf-

repo

rted

data

are

info

rmat

ion

supp

lied

toth

edi

scou

ntbr

oker

age

rm

atth

eti

me

the

acco

unt

isop

ened

byth

epe

rson

onop

enin

gth

eac

coun

tIn

com

eis

repo

rted

wit

hin

eigh

tra

nges

whe

reth

eto

pra

nge

isgr

eate

rth

an$1

250

00W

eca

lcul

ate

mea

nsus

ing

the

mid

poin

tof

each

ran

gean

d$1

250

00fo

rth

eto

pra

nge

Equ

ity

toN

etW

orth

()i

sth

epr

opor

tion

ofth

em

arke

tva

lue

ofco

mm

onst

ock

inve

stm

ent

atth

isdi

scou

ntbr

oker

age

rm

asof

Janu

ary

1991

toto

tals

elf-

repo

rted

net

wor

thw

hen

the

hou

seho

ldop

ened

its

rst

acco

unt

atth

isbr

oker

age

Tho

seh

ouse

hold

sw

ith

apr

opor

tion

equ

ity

tone

tw

orth

grea

ter

than

100

perc

ent

are

dele

ted

wh

enca

lcul

atin

gm

ean

san

dm

edia

ns

Nu

mbe

rof

obse

rvat

ion

sfo

rea

chva

riab

leis

slig

htl

yle

ssth

anth

en

umbe

rof

repo

rted

hous

ehol

ds












sprds 5 S Pds

cl

Pds


cl

Pdb

b 2 1 D Pds

cl and Pdb


s and Pdbb are the











gr)is calculated as

Rhtgr 5 O

i= 1

sht

pitRitgr




net) as

(1 1 Ritnet) 5 (1 1 Rit

gr) (1 2 cits )(1 1 c it 2 1

b )





s




Rhtnet 5 O

i= 1

sht

pitRitnet



RMtgr 5

1nmt

Oh= 1

nmt

Rhtgr and RMnet 5

1nmt

Oh= 1

nmt

Rhtnet


IIC Turnover










gr = Rhtgr 2 Rht

b or ARhtnet = Rht




gr[ s ( ARt

gr) Ouml 72] where

ARtgr 5

1nmt

Ot= 1

nmt

(Rhtgr 2 Rht

b )









R tpm 5 O

i= 1

npt

TitpmRit Y O

i= 1

npt

Titpm





sw)



III RESULTS







TA

BL

EII

PO

SIT

ION

VA

LU

ET

UR

NO

VE

RA

ND

RE

TU

RN

PE

RF

OR

MA

NC

EO

FC

OM

MO

NS

TO

CK

INV

ES

TM

EN

TS

OF

FE

MA

LE

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eho

useh

olds

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

ren

ce(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

P

osit

ion

Val

ue

and

Tur

nove

rM

ean

[med

ian]

183

7121

975

23

604

17

754

222

932

453

9

196

5420

161

250

7

begi

nnin

gpo

siti

onva

lue

($)

[73

87]

[82

18]

[283

1]

[7

410

][8

175

][2

765]

[74

91]

[80

97]

[260

6]

Mea

n[m

edia

n]4

406

412

201

441

611

21

70

4

227

052

283

mon

thly

turn

over

()

[17

4][2

94]

[21

20]

[1

79]

[28

1][1

02]

[15

5][3

32]

[21

77]

Pan

elB

P

erfo

rman

ceO

wn-

benc

hmar

km

onth

ly2

004

1

20

069

0

028

2

005

0

20

068

0

018

20

029

20

074

0

045

abno

rmal

gros

sre

turn

()

(22

84)

(23

66)

(24

3)(2

289

)(2

367

)(1

28)

(21

64)

(23

60)

(25

3)

Ow

n-be

nchm

ark

mon

thly

20

143

2

022

1

007

8

20

154

2

021

4

006

0

20

121

2

024

2

012

0

abno

rmal

net

retu

rn(

)(2

970

)(2

108

3)(6

35)

(29

10)

(210

48)

(39

5)(2

668

)(2

111

5)(6

68)

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

T

ests

for

diff

eren

ces

inm

edia

ns

are

base

don

aW

ilcox

onsi

gn-r

ank

test

stat

isti

cH

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Beg

inni

ngpo

siti

onva

lue

isth

em

arke

tva

lue

ofco

mm

onst

ocks

held

inth

e

rst

mon

thth

atth

eho

use

hold

appe

ars

duri

ngou

rsa

mpl

epe

riod

M

ean

mon

thly

turn

over

isth

eav

erag

eof

sale

san

dpu

rch

ase

turn

over

[M

edia

nva

lues

are

inbr

acke

ts]

Ow

n-be

nch

mar

kab

norm

alre

turn

sar

eth

eav

erag

eh

ouse

hol

dpe

rcen

tage

mon

thly

abn

orm

alre

turn

calc

ula

ted

asth

ere

aliz

edm

onth

lyre

turn

for

ah

ouse

hol

dle

ssth

ere

turn

that

wou

ldha

vebe

enea

rned

ifth

eho

useh

old

had

held

the

begi

nnin

g-of

-yea

rpo

rtfo

lio

for

the

enti

reye

ar(i

e

the

twel

vem

onth

sbe

ginn

ing

Feb

ruar

y1)

T-s

tati

stic

sfo

rab

norm

alre

turn

sar

ein

pare

nthe

ses

and

are

calc

ulat

edu

sing

tim

e-se

ries

stan

dard

erro

rsac

ross

mon

ths






t 5(Rt

pm 2 Rtsm)

s (Rtpm 2 Rt

sm) Icirc 72





















Dependentvariable

Meanmonthlyturnover

()


Portfoliovolatility


Sizecoefcient

Intercept 6269 2 0321 11466 11658 1226+ 0776(2 1147) (5885) (7098) (1144) (2216)

Single 0483 0002 0320 0330 0020 0079(424) (014) (340) (417) (212) (465)

Woman 2 1461 0058 2 0689 2 0682 2 0037 2 0136(2 1276) (427) (2 727) (2 854) (2 391) (2 800)

Single 3 2 0733 0027 2 0439 2 0540 2 0029 2 0138woman (2 338) (108) (2 245) (2 357) (2 160) (2 430)

Age10 2 0311 0002 2 0536 2 0393 2 0027 2 0055(2 926) (423) (2 1931) (2 1678) (2 955) ( 2 1100)

Children 2 0037 0008 2 0014 2 0051 2 0002 2 0008(2 040) (076) (2 019) (2 079) (2 022) (2 061)

Income 2 0002 00002 00003 0001 0003 00011000 (2 130) (133) (022) (138) (249) (031)

Income 2 00003 0027 0011 0012 2 0008 2 0018dummy (2 024) (154) (010) (011) (2 068) (2 082)

Adj R2 () 153 020 211 195 059 119





IIID Portfolio Risk




where
















TA

BL

EIV

RIS

KE

XP

OS

UR

ES

AN

DR

ISK

-AD

JUS

TE

DR

ET

UR

NS

OF

CO

MM

ON

ST

OC

KIN

VE

ST

ME

NT

SO

FF

EM

AL

E

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eh

ouse

hold

s

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

G

ross

aver

age

hou

seho

ldpe

rcen

tage

mon

thly

retu

rns

Tw

o-fa

ctor

mod

elin

terc

ept

20

044

20

083

003

92

005

12

008

20

031

20

036

20

099

006

3T

wo-

fact

orm

odel

coef

cie

ntes

tim

ate

on(R

mt

2R

ft)

105

0

108

1

20

031

1

053

1

075

0

022

103

5

108

8

005

3

Tw

o-fa

ctor

mod

elco

efc

ient

esti

mat

eon

SM

Bt

036

0

051

9

20

159

0

380

0

490

0

109

0

307

0

582

0

275

A

djus

ted

R2

938

920

653

934

921

528

944

915

706

Pan

elB

N

etav

erag

eho

useh

old

perc

enta

gem

onth

lyre

turn

s

Tw

o-fa

ctor

mod

elin

terc

ept

20

162

20

253

0

091

2

017

1

20

245

0

074

2

014

2

20

285

0

143

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

H

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Hou

seho

lds

are

clas

si

edas

mar

ried

orsi

ngle

base

don

the

mar

ital

stat

usof

the

head

ofho

use

hold

Coe

fci

ent

and

inte

rcep

tes

tim

ates

for

the

two-

fact

orm

odel

are

thos

efr

oma

tim

e-se

ries

regr

essi

onof

the

gros

s(n

et)

aver

age

hou

seho

ldex

cess

retu

rnon

the

mar

ket

exce

ssre

turn

(Rm

t2

Rf

t)

and

aze

ro-i

nve

stm

ent

size

port

foli

o(S

MB

t)

(RM

tgr

2R

ft)

=a

i+

bi(

Rm

t2

Rf

t)

+s i

SM

Bt

+e

it
















IVA Risk Aversion


IVB Gambling















V CONCLUSION










REFERENCES







































































TA

BL

EI

DE

SC

RIP

TIV

ES

TA

TIS

TIC

SF

OR

DE

MO

GR

AP

HIC

SO

FF

EM

AL

EA

ND

MA

LE

HO

US

EH

OL

DS

Var

iabl

e

All

hous

ehol

dsM

arri

edho

use

hold

sS

ingl

eho

useh

olds

Wom

enM

en

Dif

fere

nce

(wom

enndash

men

)W

omen

Men

Dif

fere

nce

(wom

enndash

men

)W

omen

Men

Dif

fere

nce

(wom

enmdash

men

)

Pan

elA

In

foba

seda

ta

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Per

cen

tage

mar

ried

680

757

27

7P

erce

nta

gew

ith

chil

dren

252

322

27

033

640

42

68

106

105

01

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nag

e50

950

30

649

951

12

12

530

482

48

Med

ian

age

480

480

00

480

480

00

500

460

40

Mea

nin

com

e($

000)

730

756

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681

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61

656

762

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61

w

ith

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me

gt$1

250

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211

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5

Pan

elB

Sel

f-re

port

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ta

Nu

mbe

rof

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263

711

226

170

77

700

652

218

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orth

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500

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450

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500

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0M

edia

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010

00

100

00

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thP

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625

745

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062

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220

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Per

cent

ile

270

370

210

035

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20

200

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215

0E

quit

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wor

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ean

133

132

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Med

ian

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Non

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00fo

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Equ

ity

toN

etW

orth

()i

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epr

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ofth

em

arke

tva

lue

ofco

mm

onst

ock

inve

stm

ent

atth

isdi

scou

ntbr

oker

age

rm

asof

Janu

ary

1991

toto

tals

elf-

repo

rted

net

wor

thw

hen

the

hou

seho

ldop

ened

its

rst

acco

unt

atth

isbr

oker

age

Tho

seh

ouse

hold

sw

ith

apr

opor

tion

equ

ity

tone

tw

orth

grea

ter

than

100

perc

ent

are

dele

ted

wh

enca

lcul

atin

gm

ean

san

dm

edia

ns

Nu

mbe

rof

obse

rvat

ion

sfo

rea

chva

riab

leis

slig

htl

yle

ssth

anth

en

umbe

rof

repo

rted

hous

ehol

ds












sprds 5 S Pds

cl

Pds


cl

Pdb

b 2 1 D Pds

cl and Pdb


s and Pdbb are the











gr)is calculated as

Rhtgr 5 O

i= 1

sht

pitRitgr




net) as

(1 1 Ritnet) 5 (1 1 Rit

gr) (1 2 cits )(1 1 c it 2 1

b )





s




Rhtnet 5 O

i= 1

sht

pitRitnet



RMtgr 5

1nmt

Oh= 1

nmt

Rhtgr and RMnet 5

1nmt

Oh= 1

nmt

Rhtnet


IIC Turnover










gr = Rhtgr 2 Rht

b or ARhtnet = Rht




gr[ s ( ARt

gr) Ouml 72] where

ARtgr 5

1nmt

Ot= 1

nmt

(Rhtgr 2 Rht

b )









R tpm 5 O

i= 1

npt

TitpmRit Y O

i= 1

npt

Titpm





sw)



III RESULTS







TA

BL

EII

PO

SIT

ION

VA

LU

ET

UR

NO

VE

RA

ND

RE

TU

RN

PE

RF

OR

MA

NC

EO

FC

OM

MO

NS

TO

CK

INV

ES

TM

EN

TS

OF

FE

MA

LE

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eho

useh

olds

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

ren

ce(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

P

osit

ion

Val

ue

and

Tur

nove

rM

ean

[med

ian]

183

7121

975

23

604

17

754

222

932

453

9

196

5420

161

250

7

begi

nnin

gpo

siti

onva

lue

($)

[73

87]

[82

18]

[283

1]

[7

410

][8

175

][2

765]

[74

91]

[80

97]

[260

6]

Mea

n[m

edia

n]4

406

412

201

441

611

21

70

4

227

052

283

mon

thly

turn

over

()

[17

4][2

94]

[21

20]

[1

79]

[28

1][1

02]

[15

5][3

32]

[21

77]

Pan

elB

P

erfo

rman

ceO

wn-

benc

hmar

km

onth

ly2

004

1

20

069

0

028

2

005

0

20

068

0

018

20

029

20

074

0

045

abno

rmal

gros

sre

turn

()

(22

84)

(23

66)

(24

3)(2

289

)(2

367

)(1

28)

(21

64)

(23

60)

(25

3)

Ow

n-be

nchm

ark

mon

thly

20

143

2

022

1

007

8

20

154

2

021

4

006

0

20

121

2

024

2

012

0

abno

rmal

net

retu

rn(

)(2

970

)(2

108

3)(6

35)

(29

10)

(210

48)

(39

5)(2

668

)(2

111

5)(6

68)

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

T

ests

for

diff

eren

ces

inm

edia

ns

are

base

don

aW

ilcox

onsi

gn-r

ank

test

stat

isti

cH

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Beg

inni

ngpo

siti

onva

lue

isth

em

arke

tva

lue

ofco

mm

onst

ocks

held

inth

e

rst

mon

thth

atth

eho

use

hold

appe

ars

duri

ngou

rsa

mpl

epe

riod

M

ean

mon

thly

turn

over

isth

eav

erag

eof

sale

san

dpu

rch

ase

turn

over

[M

edia

nva

lues

are

inbr

acke

ts]

Ow

n-be

nch

mar

kab

norm

alre

turn

sar

eth

eav

erag

eh

ouse

hol

dpe

rcen

tage

mon

thly

abn

orm

alre

turn

calc

ula

ted

asth

ere

aliz

edm

onth

lyre

turn

for

ah

ouse

hol

dle

ssth

ere

turn

that

wou

ldha

vebe

enea

rned

ifth

eho

useh

old

had

held

the

begi

nnin

g-of

-yea

rpo

rtfo

lio

for

the

enti

reye

ar(i

e

the

twel

vem

onth

sbe

ginn

ing

Feb

ruar

y1)

T-s

tati

stic

sfo

rab

norm

alre

turn

sar

ein

pare

nthe

ses

and

are

calc

ulat

edu

sing

tim

e-se

ries

stan

dard

erro

rsac

ross

mon

ths






t 5(Rt

pm 2 Rtsm)

s (Rtpm 2 Rt

sm) Icirc 72





















Dependentvariable

Meanmonthlyturnover

()


Portfoliovolatility


Sizecoefcient

Intercept 6269 2 0321 11466 11658 1226+ 0776(2 1147) (5885) (7098) (1144) (2216)

Single 0483 0002 0320 0330 0020 0079(424) (014) (340) (417) (212) (465)

Woman 2 1461 0058 2 0689 2 0682 2 0037 2 0136(2 1276) (427) (2 727) (2 854) (2 391) (2 800)

Single 3 2 0733 0027 2 0439 2 0540 2 0029 2 0138woman (2 338) (108) (2 245) (2 357) (2 160) (2 430)

Age10 2 0311 0002 2 0536 2 0393 2 0027 2 0055(2 926) (423) (2 1931) (2 1678) (2 955) ( 2 1100)

Children 2 0037 0008 2 0014 2 0051 2 0002 2 0008(2 040) (076) (2 019) (2 079) (2 022) (2 061)

Income 2 0002 00002 00003 0001 0003 00011000 (2 130) (133) (022) (138) (249) (031)

Income 2 00003 0027 0011 0012 2 0008 2 0018dummy (2 024) (154) (010) (011) (2 068) (2 082)

Adj R2 () 153 020 211 195 059 119





IIID Portfolio Risk




where
















TA

BL

EIV

RIS

KE

XP

OS

UR

ES

AN

DR

ISK

-AD

JUS

TE

DR

ET

UR

NS

OF

CO

MM

ON

ST

OC

KIN

VE

ST

ME

NT

SO

FF

EM

AL

E

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eh

ouse

hold

s

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

G

ross

aver

age

hou

seho

ldpe

rcen

tage

mon

thly

retu

rns

Tw

o-fa

ctor

mod

elin

terc

ept

20

044

20

083

003

92

005

12

008

20

031

20

036

20

099

006

3T

wo-

fact

orm

odel

coef

cie

ntes

tim

ate

on(R

mt

2R

ft)

105

0

108

1

20

031

1

053

1

075

0

022

103

5

108

8

005

3

Tw

o-fa

ctor

mod

elco

efc

ient

esti

mat

eon

SM

Bt

036

0

051

9

20

159

0

380

0

490

0

109

0

307

0

582

0

275

A

djus

ted

R2

938

920

653

934

921

528

944

915

706

Pan

elB

N

etav

erag

eho

useh

old

perc

enta

gem

onth

lyre

turn

s

Tw

o-fa

ctor

mod

elin

terc

ept

20

162

20

253

0

091

2

017

1

20

245

0

074

2

014

2

20

285

0

143

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

H

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Hou

seho

lds

are

clas

si

edas

mar

ried

orsi

ngle

base

don

the

mar

ital

stat

usof

the

head

ofho

use

hold

Coe

fci

ent

and

inte

rcep

tes

tim

ates

for

the

two-

fact

orm

odel

are

thos

efr

oma

tim

e-se

ries

regr

essi

onof

the

gros

s(n

et)

aver

age

hou

seho

ldex

cess

retu

rnon

the

mar

ket

exce

ssre

turn

(Rm

t2

Rf

t)

and

aze

ro-i

nve

stm

ent

size

port

foli

o(S

MB

t)

(RM

tgr

2R

ft)

=a

i+

bi(

Rm

t2

Rf

t)

+s i

SM

Bt

+e

it
















IVA Risk Aversion


IVB Gambling















V CONCLUSION










REFERENCES












































































sprds 5 S Pds

cl

Pds


cl

Pdb

b 2 1 D Pds

cl and Pdb


s and Pdbb are the











gr)is calculated as

Rhtgr 5 O

i= 1

sht

pitRitgr




net) as

(1 1 Ritnet) 5 (1 1 Rit

gr) (1 2 cits )(1 1 c it 2 1

b )





s




Rhtnet 5 O

i= 1

sht

pitRitnet



RMtgr 5

1nmt

Oh= 1

nmt

Rhtgr and RMnet 5

1nmt

Oh= 1

nmt

Rhtnet


IIC Turnover










gr = Rhtgr 2 Rht

b or ARhtnet = Rht




gr[ s ( ARt

gr) Ouml 72] where

ARtgr 5

1nmt

Ot= 1

nmt

(Rhtgr 2 Rht

b )









R tpm 5 O

i= 1

npt

TitpmRit Y O

i= 1

npt

Titpm





sw)



III RESULTS







TA

BL

EII

PO

SIT

ION

VA

LU

ET

UR

NO

VE

RA

ND

RE

TU

RN

PE

RF

OR

MA

NC

EO

FC

OM

MO

NS

TO

CK

INV

ES

TM

EN

TS

OF

FE

MA

LE

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eho

useh

olds

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

ren

ce(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

P

osit

ion

Val

ue

and

Tur

nove

rM

ean

[med

ian]

183

7121

975

23

604

17

754

222

932

453

9

196

5420

161

250

7

begi

nnin

gpo

siti

onva

lue

($)

[73

87]

[82

18]

[283

1]

[7

410

][8

175

][2

765]

[74

91]

[80

97]

[260

6]

Mea

n[m

edia

n]4

406

412

201

441

611

21

70

4

227

052

283

mon

thly

turn

over

()

[17

4][2

94]

[21

20]

[1

79]

[28

1][1

02]

[15

5][3

32]

[21

77]

Pan

elB

P

erfo

rman

ceO

wn-

benc

hmar

km

onth

ly2

004

1

20

069

0

028

2

005

0

20

068

0

018

20

029

20

074

0

045

abno

rmal

gros

sre

turn

()

(22

84)

(23

66)

(24

3)(2

289

)(2

367

)(1

28)

(21

64)

(23

60)

(25

3)

Ow

n-be

nchm

ark

mon

thly

20

143

2

022

1

007

8

20

154

2

021

4

006

0

20

121

2

024

2

012

0

abno

rmal

net

retu

rn(

)(2

970

)(2

108

3)(6

35)

(29

10)

(210

48)

(39

5)(2

668

)(2

111

5)(6

68)

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

T

ests

for

diff

eren

ces

inm

edia

ns

are

base

don

aW

ilcox

onsi

gn-r

ank

test

stat

isti

cH

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Beg

inni

ngpo

siti

onva

lue

isth

em

arke

tva

lue

ofco

mm

onst

ocks

held

inth

e

rst

mon

thth

atth

eho

use

hold

appe

ars

duri

ngou

rsa

mpl

epe

riod

M

ean

mon

thly

turn

over

isth

eav

erag

eof

sale

san

dpu

rch

ase

turn

over

[M

edia

nva

lues

are

inbr

acke

ts]

Ow

n-be

nch

mar

kab

norm

alre

turn

sar

eth

eav

erag

eh

ouse

hol

dpe

rcen

tage

mon

thly

abn

orm

alre

turn

calc

ula

ted

asth

ere

aliz

edm

onth

lyre

turn

for

ah

ouse

hol

dle

ssth

ere

turn

that

wou

ldha

vebe

enea

rned

ifth

eho

useh

old

had

held

the

begi

nnin

g-of

-yea

rpo

rtfo

lio

for

the

enti

reye

ar(i

e

the

twel

vem

onth

sbe

ginn

ing

Feb

ruar

y1)

T-s

tati

stic

sfo

rab

norm

alre

turn

sar

ein

pare

nthe

ses

and

are

calc

ulat

edu

sing

tim

e-se

ries

stan

dard

erro

rsac

ross

mon

ths






t 5(Rt

pm 2 Rtsm)

s (Rtpm 2 Rt

sm) Icirc 72





















Dependentvariable

Meanmonthlyturnover

()


Portfoliovolatility


Sizecoefcient

Intercept 6269 2 0321 11466 11658 1226+ 0776(2 1147) (5885) (7098) (1144) (2216)

Single 0483 0002 0320 0330 0020 0079(424) (014) (340) (417) (212) (465)

Woman 2 1461 0058 2 0689 2 0682 2 0037 2 0136(2 1276) (427) (2 727) (2 854) (2 391) (2 800)

Single 3 2 0733 0027 2 0439 2 0540 2 0029 2 0138woman (2 338) (108) (2 245) (2 357) (2 160) (2 430)

Age10 2 0311 0002 2 0536 2 0393 2 0027 2 0055(2 926) (423) (2 1931) (2 1678) (2 955) ( 2 1100)

Children 2 0037 0008 2 0014 2 0051 2 0002 2 0008(2 040) (076) (2 019) (2 079) (2 022) (2 061)

Income 2 0002 00002 00003 0001 0003 00011000 (2 130) (133) (022) (138) (249) (031)

Income 2 00003 0027 0011 0012 2 0008 2 0018dummy (2 024) (154) (010) (011) (2 068) (2 082)

Adj R2 () 153 020 211 195 059 119





IIID Portfolio Risk




where
















TA

BL

EIV

RIS

KE

XP

OS

UR

ES

AN

DR

ISK

-AD

JUS

TE

DR

ET

UR

NS

OF

CO

MM

ON

ST

OC

KIN

VE

ST

ME

NT

SO

FF

EM

AL

E

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eh

ouse

hold

s

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

G

ross

aver

age

hou

seho

ldpe

rcen

tage

mon

thly

retu

rns

Tw

o-fa

ctor

mod

elin

terc

ept

20

044

20

083

003

92

005

12

008

20

031

20

036

20

099

006

3T

wo-

fact

orm

odel

coef

cie

ntes

tim

ate

on(R

mt

2R

ft)

105

0

108

1

20

031

1

053

1

075

0

022

103

5

108

8

005

3

Tw

o-fa

ctor

mod

elco

efc

ient

esti

mat

eon

SM

Bt

036

0

051

9

20

159

0

380

0

490

0

109

0

307

0

582

0

275

A

djus

ted

R2

938

920

653

934

921

528

944

915

706

Pan

elB

N

etav

erag

eho

useh

old

perc

enta

gem

onth

lyre

turn

s

Tw

o-fa

ctor

mod

elin

terc

ept

20

162

20

253

0

091

2

017

1

20

245

0

074

2

014

2

20

285

0

143

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

H

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Hou

seho

lds

are

clas

si

edas

mar

ried

orsi

ngle

base

don

the

mar

ital

stat

usof

the

head

ofho

use

hold

Coe

fci

ent

and

inte

rcep

tes

tim

ates

for

the

two-

fact

orm

odel

are

thos

efr

oma

tim

e-se

ries

regr

essi

onof

the

gros

s(n

et)

aver

age

hou

seho

ldex

cess

retu

rnon

the

mar

ket

exce

ssre

turn

(Rm

t2

Rf

t)

and

aze

ro-i

nve

stm

ent

size

port

foli

o(S

MB

t)

(RM

tgr

2R

ft)

=a

i+

bi(

Rm

t2

Rf

t)

+s i

SM

Bt

+e

it
















IVA Risk Aversion


IVB Gambling















V CONCLUSION










REFERENCES






































































gr)is calculated as

Rhtgr 5 O

i= 1

sht

pitRitgr




net) as

(1 1 Ritnet) 5 (1 1 Rit

gr) (1 2 cits )(1 1 c it 2 1

b )





s




Rhtnet 5 O

i= 1

sht

pitRitnet



RMtgr 5

1nmt

Oh= 1

nmt

Rhtgr and RMnet 5

1nmt

Oh= 1

nmt

Rhtnet


IIC Turnover










gr = Rhtgr 2 Rht

b or ARhtnet = Rht




gr[ s ( ARt

gr) Ouml 72] where

ARtgr 5

1nmt

Ot= 1

nmt

(Rhtgr 2 Rht

b )









R tpm 5 O

i= 1

npt

TitpmRit Y O

i= 1

npt

Titpm





sw)



III RESULTS







TA

BL

EII

PO

SIT

ION

VA

LU

ET

UR

NO

VE

RA

ND

RE

TU

RN

PE

RF

OR

MA

NC

EO

FC

OM

MO

NS

TO

CK

INV

ES

TM

EN

TS

OF

FE

MA

LE

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eho

useh

olds

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

ren

ce(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

P

osit

ion

Val

ue

and

Tur

nove

rM

ean

[med

ian]

183

7121

975

23

604

17

754

222

932

453

9

196

5420

161

250

7

begi

nnin

gpo

siti

onva

lue

($)

[73

87]

[82

18]

[283

1]

[7

410

][8

175

][2

765]

[74

91]

[80

97]

[260

6]

Mea

n[m

edia

n]4

406

412

201

441

611

21

70

4

227

052

283

mon

thly

turn

over

()

[17

4][2

94]

[21

20]

[1

79]

[28

1][1

02]

[15

5][3

32]

[21

77]

Pan

elB

P

erfo

rman

ceO

wn-

benc

hmar

km

onth

ly2

004

1

20

069

0

028

2

005

0

20

068

0

018

20

029

20

074

0

045

abno

rmal

gros

sre

turn

()

(22

84)

(23

66)

(24

3)(2

289

)(2

367

)(1

28)

(21

64)

(23

60)

(25

3)

Ow

n-be

nchm

ark

mon

thly

20

143

2

022

1

007

8

20

154

2

021

4

006

0

20

121

2

024

2

012

0

abno

rmal

net

retu

rn(

)(2

970

)(2

108

3)(6

35)

(29

10)

(210

48)

(39

5)(2

668

)(2

111

5)(6

68)

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

T

ests

for

diff

eren

ces

inm

edia

ns

are

base

don

aW

ilcox

onsi

gn-r

ank

test

stat

isti

cH

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Beg

inni

ngpo

siti

onva

lue

isth

em

arke

tva

lue

ofco

mm

onst

ocks

held

inth

e

rst

mon

thth

atth

eho

use

hold

appe

ars

duri

ngou

rsa

mpl

epe

riod

M

ean

mon

thly

turn

over

isth

eav

erag

eof

sale

san

dpu

rch

ase

turn

over

[M

edia

nva

lues

are

inbr

acke

ts]

Ow

n-be

nch

mar

kab

norm

alre

turn

sar

eth

eav

erag

eh

ouse

hol

dpe

rcen

tage

mon

thly

abn

orm

alre

turn

calc

ula

ted

asth

ere

aliz

edm

onth

lyre

turn

for

ah

ouse

hol

dle

ssth

ere

turn

that

wou

ldha

vebe

enea

rned

ifth

eho

useh

old

had

held

the

begi

nnin

g-of

-yea

rpo

rtfo

lio

for

the

enti

reye

ar(i

e

the

twel

vem

onth

sbe

ginn

ing

Feb

ruar

y1)

T-s

tati

stic

sfo

rab

norm

alre

turn

sar

ein

pare

nthe

ses

and

are

calc

ulat

edu

sing

tim

e-se

ries

stan

dard

erro

rsac

ross

mon

ths






t 5(Rt

pm 2 Rtsm)

s (Rtpm 2 Rt

sm) Icirc 72





















Dependentvariable

Meanmonthlyturnover

()


Portfoliovolatility


Sizecoefcient

Intercept 6269 2 0321 11466 11658 1226+ 0776(2 1147) (5885) (7098) (1144) (2216)

Single 0483 0002 0320 0330 0020 0079(424) (014) (340) (417) (212) (465)

Woman 2 1461 0058 2 0689 2 0682 2 0037 2 0136(2 1276) (427) (2 727) (2 854) (2 391) (2 800)

Single 3 2 0733 0027 2 0439 2 0540 2 0029 2 0138woman (2 338) (108) (2 245) (2 357) (2 160) (2 430)

Age10 2 0311 0002 2 0536 2 0393 2 0027 2 0055(2 926) (423) (2 1931) (2 1678) (2 955) ( 2 1100)

Children 2 0037 0008 2 0014 2 0051 2 0002 2 0008(2 040) (076) (2 019) (2 079) (2 022) (2 061)

Income 2 0002 00002 00003 0001 0003 00011000 (2 130) (133) (022) (138) (249) (031)

Income 2 00003 0027 0011 0012 2 0008 2 0018dummy (2 024) (154) (010) (011) (2 068) (2 082)

Adj R2 () 153 020 211 195 059 119





IIID Portfolio Risk




where
















TA

BL

EIV

RIS

KE

XP

OS

UR

ES

AN

DR

ISK

-AD

JUS

TE

DR

ET

UR

NS

OF

CO

MM

ON

ST

OC

KIN

VE

ST

ME

NT

SO

FF

EM

AL

E

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eh

ouse

hold

s

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

G

ross

aver

age

hou

seho

ldpe

rcen

tage

mon

thly

retu

rns

Tw

o-fa

ctor

mod

elin

terc

ept

20

044

20

083

003

92

005

12

008

20

031

20

036

20

099

006

3T

wo-

fact

orm

odel

coef

cie

ntes

tim

ate

on(R

mt

2R

ft)

105

0

108

1

20

031

1

053

1

075

0

022

103

5

108

8

005

3

Tw

o-fa

ctor

mod

elco

efc

ient

esti

mat

eon

SM

Bt

036

0

051

9

20

159

0

380

0

490

0

109

0

307

0

582

0

275

A

djus

ted

R2

938

920

653

934

921

528

944

915

706

Pan

elB

N

etav

erag

eho

useh

old

perc

enta

gem

onth

lyre

turn

s

Tw

o-fa

ctor

mod

elin

terc

ept

20

162

20

253

0

091

2

017

1

20

245

0

074

2

014

2

20

285

0

143

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

H

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Hou

seho

lds

are

clas

si

edas

mar

ried

orsi

ngle

base

don

the

mar

ital

stat

usof

the

head

ofho

use

hold

Coe

fci

ent

and

inte

rcep

tes

tim

ates

for

the

two-

fact

orm

odel

are

thos

efr

oma

tim

e-se

ries

regr

essi

onof

the

gros

s(n

et)

aver

age

hou

seho

ldex

cess

retu

rnon

the

mar

ket

exce

ssre

turn

(Rm

t2

Rf

t)

and

aze

ro-i

nve

stm

ent

size

port

foli

o(S

MB

t)

(RM

tgr

2R

ft)

=a

i+

bi(

Rm

t2

Rf

t)

+s i

SM

Bt

+e

it
















IVA Risk Aversion


IVB Gambling















V CONCLUSION










REFERENCES





































































s




Rhtnet 5 O

i= 1

sht

pitRitnet



RMtgr 5

1nmt

Oh= 1

nmt

Rhtgr and RMnet 5

1nmt

Oh= 1

nmt

Rhtnet


IIC Turnover










gr = Rhtgr 2 Rht

b or ARhtnet = Rht




gr[ s ( ARt

gr) Ouml 72] where

ARtgr 5

1nmt

Ot= 1

nmt

(Rhtgr 2 Rht

b )









R tpm 5 O

i= 1

npt

TitpmRit Y O

i= 1

npt

Titpm





sw)



III RESULTS







TA

BL

EII

PO

SIT

ION

VA

LU

ET

UR

NO

VE

RA

ND

RE

TU

RN

PE

RF

OR

MA

NC

EO

FC

OM

MO

NS

TO

CK

INV

ES

TM

EN

TS

OF

FE

MA

LE

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eho

useh

olds

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

ren

ce(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

P

osit

ion

Val

ue

and

Tur

nove

rM

ean

[med

ian]

183

7121

975

23

604

17

754

222

932

453

9

196

5420

161

250

7

begi

nnin

gpo

siti

onva

lue

($)

[73

87]

[82

18]

[283

1]

[7

410

][8

175

][2

765]

[74

91]

[80

97]

[260

6]

Mea

n[m

edia

n]4

406

412

201

441

611

21

70

4

227

052

283

mon

thly

turn

over

()

[17

4][2

94]

[21

20]

[1

79]

[28

1][1

02]

[15

5][3

32]

[21

77]

Pan

elB

P

erfo

rman

ceO

wn-

benc

hmar

km

onth

ly2

004

1

20

069

0

028

2

005

0

20

068

0

018

20

029

20

074

0

045

abno

rmal

gros

sre

turn

()

(22

84)

(23

66)

(24

3)(2

289

)(2

367

)(1

28)

(21

64)

(23

60)

(25

3)

Ow

n-be

nchm

ark

mon

thly

20

143

2

022

1

007

8

20

154

2

021

4

006

0

20

121

2

024

2

012

0

abno

rmal

net

retu

rn(

)(2

970

)(2

108

3)(6

35)

(29

10)

(210

48)

(39

5)(2

668

)(2

111

5)(6

68)

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

T

ests

for

diff

eren

ces

inm

edia

ns

are

base

don

aW

ilcox

onsi

gn-r

ank

test

stat

isti

cH

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Beg

inni

ngpo

siti

onva

lue

isth

em

arke

tva

lue

ofco

mm

onst

ocks

held

inth

e

rst

mon

thth

atth

eho

use

hold

appe

ars

duri

ngou

rsa

mpl

epe

riod

M

ean

mon

thly

turn

over

isth

eav

erag

eof

sale

san

dpu

rch

ase

turn

over

[M

edia

nva

lues

are

inbr

acke

ts]

Ow

n-be

nch

mar

kab

norm

alre

turn

sar

eth

eav

erag

eh

ouse

hol

dpe

rcen

tage

mon

thly

abn

orm

alre

turn

calc

ula

ted

asth

ere

aliz

edm

onth

lyre

turn

for

ah

ouse

hol

dle

ssth

ere

turn

that

wou

ldha

vebe

enea

rned

ifth

eho

useh

old

had

held

the

begi

nnin

g-of

-yea

rpo

rtfo

lio

for

the

enti

reye

ar(i

e

the

twel

vem

onth

sbe

ginn

ing

Feb

ruar

y1)

T-s

tati

stic

sfo

rab

norm

alre

turn

sar

ein

pare

nthe

ses

and

are

calc

ulat

edu

sing

tim

e-se

ries

stan

dard

erro

rsac

ross

mon

ths






t 5(Rt

pm 2 Rtsm)

s (Rtpm 2 Rt

sm) Icirc 72





















Dependentvariable

Meanmonthlyturnover

()


Portfoliovolatility


Sizecoefcient

Intercept 6269 2 0321 11466 11658 1226+ 0776(2 1147) (5885) (7098) (1144) (2216)

Single 0483 0002 0320 0330 0020 0079(424) (014) (340) (417) (212) (465)

Woman 2 1461 0058 2 0689 2 0682 2 0037 2 0136(2 1276) (427) (2 727) (2 854) (2 391) (2 800)

Single 3 2 0733 0027 2 0439 2 0540 2 0029 2 0138woman (2 338) (108) (2 245) (2 357) (2 160) (2 430)

Age10 2 0311 0002 2 0536 2 0393 2 0027 2 0055(2 926) (423) (2 1931) (2 1678) (2 955) ( 2 1100)

Children 2 0037 0008 2 0014 2 0051 2 0002 2 0008(2 040) (076) (2 019) (2 079) (2 022) (2 061)

Income 2 0002 00002 00003 0001 0003 00011000 (2 130) (133) (022) (138) (249) (031)

Income 2 00003 0027 0011 0012 2 0008 2 0018dummy (2 024) (154) (010) (011) (2 068) (2 082)

Adj R2 () 153 020 211 195 059 119





IIID Portfolio Risk




where
















TA

BL

EIV

RIS

KE

XP

OS

UR

ES

AN

DR

ISK

-AD

JUS

TE

DR

ET

UR

NS

OF

CO

MM

ON

ST

OC

KIN

VE

ST

ME

NT

SO

FF

EM

AL

E

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eh

ouse

hold

s

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

G

ross

aver

age

hou

seho

ldpe

rcen

tage

mon

thly

retu

rns

Tw

o-fa

ctor

mod

elin

terc

ept

20

044

20

083

003

92

005

12

008

20

031

20

036

20

099

006

3T

wo-

fact

orm

odel

coef

cie

ntes

tim

ate

on(R

mt

2R

ft)

105

0

108

1

20

031

1

053

1

075

0

022

103

5

108

8

005

3

Tw

o-fa

ctor

mod

elco

efc

ient

esti

mat

eon

SM

Bt

036

0

051

9

20

159

0

380

0

490

0

109

0

307

0

582

0

275

A

djus

ted

R2

938

920

653

934

921

528

944

915

706

Pan

elB

N

etav

erag

eho

useh

old

perc

enta

gem

onth

lyre

turn

s

Tw

o-fa

ctor

mod

elin

terc

ept

20

162

20

253

0

091

2

017

1

20

245

0

074

2

014

2

20

285

0

143

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

H

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Hou

seho

lds

are

clas

si

edas

mar

ried

orsi

ngle

base

don

the

mar

ital

stat

usof

the

head

ofho

use

hold

Coe

fci

ent

and

inte

rcep

tes

tim

ates

for

the

two-

fact

orm

odel

are

thos

efr

oma

tim

e-se

ries

regr

essi

onof

the

gros

s(n

et)

aver

age

hou

seho

ldex

cess

retu

rnon

the

mar

ket

exce

ssre

turn

(Rm

t2

Rf

t)

and

aze

ro-i

nve

stm

ent

size

port

foli

o(S

MB

t)

(RM

tgr

2R

ft)

=a

i+

bi(

Rm

t2

Rf

t)

+s i

SM

Bt

+e

it
















IVA Risk Aversion


IVB Gambling















V CONCLUSION










REFERENCES






































































gr = Rhtgr 2 Rht

b or ARhtnet = Rht




gr[ s ( ARt

gr) Ouml 72] where

ARtgr 5

1nmt

Ot= 1

nmt

(Rhtgr 2 Rht

b )









R tpm 5 O

i= 1

npt

TitpmRit Y O

i= 1

npt

Titpm





sw)



III RESULTS







TA

BL

EII

PO

SIT

ION

VA

LU

ET

UR

NO

VE

RA

ND

RE

TU

RN

PE

RF

OR

MA

NC

EO

FC

OM

MO

NS

TO

CK

INV

ES

TM

EN

TS

OF

FE

MA

LE

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eho

useh

olds

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

ren

ce(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

P

osit

ion

Val

ue

and

Tur

nove

rM

ean

[med

ian]

183

7121

975

23

604

17

754

222

932

453

9

196

5420

161

250

7

begi

nnin

gpo

siti

onva

lue

($)

[73

87]

[82

18]

[283

1]

[7

410

][8

175

][2

765]

[74

91]

[80

97]

[260

6]

Mea

n[m

edia

n]4

406

412

201

441

611

21

70

4

227

052

283

mon

thly

turn

over

()

[17

4][2

94]

[21

20]

[1

79]

[28

1][1

02]

[15

5][3

32]

[21

77]

Pan

elB

P

erfo

rman

ceO

wn-

benc

hmar

km

onth

ly2

004

1

20

069

0

028

2

005

0

20

068

0

018

20

029

20

074

0

045

abno

rmal

gros

sre

turn

()

(22

84)

(23

66)

(24

3)(2

289

)(2

367

)(1

28)

(21

64)

(23

60)

(25

3)

Ow

n-be

nchm

ark

mon

thly

20

143

2

022

1

007

8

20

154

2

021

4

006

0

20

121

2

024

2

012

0

abno

rmal

net

retu

rn(

)(2

970

)(2

108

3)(6

35)

(29

10)

(210

48)

(39

5)(2

668

)(2

111

5)(6

68)

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

T

ests

for

diff

eren

ces

inm

edia

ns

are

base

don

aW

ilcox

onsi

gn-r

ank

test

stat

isti

cH

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Beg

inni

ngpo

siti

onva

lue

isth

em

arke

tva

lue

ofco

mm

onst

ocks

held

inth

e

rst

mon

thth

atth

eho

use

hold

appe

ars

duri

ngou

rsa

mpl

epe

riod

M

ean

mon

thly

turn

over

isth

eav

erag

eof

sale

san

dpu

rch

ase

turn

over

[M

edia

nva

lues

are

inbr

acke

ts]

Ow

n-be

nch

mar

kab

norm

alre

turn

sar

eth

eav

erag

eh

ouse

hol

dpe

rcen

tage

mon

thly

abn

orm

alre

turn

calc

ula

ted

asth

ere

aliz

edm

onth

lyre

turn

for

ah

ouse

hol

dle

ssth

ere

turn

that

wou

ldha

vebe

enea

rned

ifth

eho

useh

old

had

held

the

begi

nnin

g-of

-yea

rpo

rtfo

lio

for

the

enti

reye

ar(i

e

the

twel

vem

onth

sbe

ginn

ing

Feb

ruar

y1)

T-s

tati

stic

sfo

rab

norm

alre

turn

sar

ein

pare

nthe

ses

and

are

calc

ulat

edu

sing

tim

e-se

ries

stan

dard

erro

rsac

ross

mon

ths






t 5(Rt

pm 2 Rtsm)

s (Rtpm 2 Rt

sm) Icirc 72





















Dependentvariable

Meanmonthlyturnover

()


Portfoliovolatility


Sizecoefcient

Intercept 6269 2 0321 11466 11658 1226+ 0776(2 1147) (5885) (7098) (1144) (2216)

Single 0483 0002 0320 0330 0020 0079(424) (014) (340) (417) (212) (465)

Woman 2 1461 0058 2 0689 2 0682 2 0037 2 0136(2 1276) (427) (2 727) (2 854) (2 391) (2 800)

Single 3 2 0733 0027 2 0439 2 0540 2 0029 2 0138woman (2 338) (108) (2 245) (2 357) (2 160) (2 430)

Age10 2 0311 0002 2 0536 2 0393 2 0027 2 0055(2 926) (423) (2 1931) (2 1678) (2 955) ( 2 1100)

Children 2 0037 0008 2 0014 2 0051 2 0002 2 0008(2 040) (076) (2 019) (2 079) (2 022) (2 061)

Income 2 0002 00002 00003 0001 0003 00011000 (2 130) (133) (022) (138) (249) (031)

Income 2 00003 0027 0011 0012 2 0008 2 0018dummy (2 024) (154) (010) (011) (2 068) (2 082)

Adj R2 () 153 020 211 195 059 119





IIID Portfolio Risk




where
















TA

BL

EIV

RIS

KE

XP

OS

UR

ES

AN

DR

ISK

-AD

JUS

TE

DR

ET

UR

NS

OF

CO

MM

ON

ST

OC

KIN

VE

ST

ME

NT

SO

FF

EM

AL

E

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eh

ouse

hold

s

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

G

ross

aver

age

hou

seho

ldpe

rcen

tage

mon

thly

retu

rns

Tw

o-fa

ctor

mod

elin

terc

ept

20

044

20

083

003

92

005

12

008

20

031

20

036

20

099

006

3T

wo-

fact

orm

odel

coef

cie

ntes

tim

ate

on(R

mt

2R

ft)

105

0

108

1

20

031

1

053

1

075

0

022

103

5

108

8

005

3

Tw

o-fa

ctor

mod

elco

efc

ient

esti

mat

eon

SM

Bt

036

0

051

9

20

159

0

380

0

490

0

109

0

307

0

582

0

275

A

djus

ted

R2

938

920

653

934

921

528

944

915

706

Pan

elB

N

etav

erag

eho

useh

old

perc

enta

gem

onth

lyre

turn

s

Tw

o-fa

ctor

mod

elin

terc

ept

20

162

20

253

0

091

2

017

1

20

245

0

074

2

014

2

20

285

0

143

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

H

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Hou

seho

lds

are

clas

si

edas

mar

ried

orsi

ngle

base

don

the

mar

ital

stat

usof

the

head

ofho

use

hold

Coe

fci

ent

and

inte

rcep

tes

tim

ates

for

the

two-

fact

orm

odel

are

thos

efr

oma

tim

e-se

ries

regr

essi

onof

the

gros

s(n

et)

aver

age

hou

seho

ldex

cess

retu

rnon

the

mar

ket

exce

ssre

turn

(Rm

t2

Rf

t)

and

aze

ro-i

nve

stm

ent

size

port

foli

o(S

MB

t)

(RM

tgr

2R

ft)

=a

i+

bi(

Rm

t2

Rf

t)

+s i

SM

Bt

+e

it
















IVA Risk Aversion


IVB Gambling















V CONCLUSION










REFERENCES







































































R tpm 5 O

i= 1

npt

TitpmRit Y O

i= 1

npt

Titpm





sw)



III RESULTS







TA

BL

EII

PO

SIT

ION

VA

LU

ET

UR

NO

VE

RA

ND

RE

TU

RN

PE

RF

OR

MA

NC

EO

FC

OM

MO

NS

TO

CK

INV

ES

TM

EN

TS

OF

FE

MA

LE

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eho

useh

olds

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

ren

ce(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

P

osit

ion

Val

ue

and

Tur

nove

rM

ean

[med

ian]

183

7121

975

23

604

17

754

222

932

453

9

196

5420

161

250

7

begi

nnin

gpo

siti

onva

lue

($)

[73

87]

[82

18]

[283

1]

[7

410

][8

175

][2

765]

[74

91]

[80

97]

[260

6]

Mea

n[m

edia

n]4

406

412

201

441

611

21

70

4

227

052

283

mon

thly

turn

over

()

[17

4][2

94]

[21

20]

[1

79]

[28

1][1

02]

[15

5][3

32]

[21

77]

Pan

elB

P

erfo

rman

ceO

wn-

benc

hmar

km

onth

ly2

004

1

20

069

0

028

2

005

0

20

068

0

018

20

029

20

074

0

045

abno

rmal

gros

sre

turn

()

(22

84)

(23

66)

(24

3)(2

289

)(2

367

)(1

28)

(21

64)

(23

60)

(25

3)

Ow

n-be

nchm

ark

mon

thly

20

143

2

022

1

007

8

20

154

2

021

4

006

0

20

121

2

024

2

012

0

abno

rmal

net

retu

rn(

)(2

970

)(2

108

3)(6

35)

(29

10)

(210

48)

(39

5)(2

668

)(2

111

5)(6

68)

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

T

ests

for

diff

eren

ces

inm

edia

ns

are

base

don

aW

ilcox

onsi

gn-r

ank

test

stat

isti

cH

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Beg

inni

ngpo

siti

onva

lue

isth

em

arke

tva

lue

ofco

mm

onst

ocks

held

inth

e

rst

mon

thth

atth

eho

use

hold

appe

ars

duri

ngou

rsa

mpl

epe

riod

M

ean

mon

thly

turn

over

isth

eav

erag

eof

sale

san

dpu

rch

ase

turn

over

[M

edia

nva

lues

are

inbr

acke

ts]

Ow

n-be

nch

mar

kab

norm

alre

turn

sar

eth

eav

erag

eh

ouse

hol

dpe

rcen

tage

mon

thly

abn

orm

alre

turn

calc

ula

ted

asth

ere

aliz

edm

onth

lyre

turn

for

ah

ouse

hol

dle

ssth

ere

turn

that

wou

ldha

vebe

enea

rned

ifth

eho

useh

old

had

held

the

begi

nnin

g-of

-yea

rpo

rtfo

lio

for

the

enti

reye

ar(i

e

the

twel

vem

onth

sbe

ginn

ing

Feb

ruar

y1)

T-s

tati

stic

sfo

rab

norm

alre

turn

sar

ein

pare

nthe

ses

and

are

calc

ulat

edu

sing

tim

e-se

ries

stan

dard

erro

rsac

ross

mon

ths






t 5(Rt

pm 2 Rtsm)

s (Rtpm 2 Rt

sm) Icirc 72





















Dependentvariable

Meanmonthlyturnover

()


Portfoliovolatility


Sizecoefcient

Intercept 6269 2 0321 11466 11658 1226+ 0776(2 1147) (5885) (7098) (1144) (2216)

Single 0483 0002 0320 0330 0020 0079(424) (014) (340) (417) (212) (465)

Woman 2 1461 0058 2 0689 2 0682 2 0037 2 0136(2 1276) (427) (2 727) (2 854) (2 391) (2 800)

Single 3 2 0733 0027 2 0439 2 0540 2 0029 2 0138woman (2 338) (108) (2 245) (2 357) (2 160) (2 430)

Age10 2 0311 0002 2 0536 2 0393 2 0027 2 0055(2 926) (423) (2 1931) (2 1678) (2 955) ( 2 1100)

Children 2 0037 0008 2 0014 2 0051 2 0002 2 0008(2 040) (076) (2 019) (2 079) (2 022) (2 061)

Income 2 0002 00002 00003 0001 0003 00011000 (2 130) (133) (022) (138) (249) (031)

Income 2 00003 0027 0011 0012 2 0008 2 0018dummy (2 024) (154) (010) (011) (2 068) (2 082)

Adj R2 () 153 020 211 195 059 119





IIID Portfolio Risk




where
















TA

BL

EIV

RIS

KE

XP

OS

UR

ES

AN

DR

ISK

-AD

JUS

TE

DR

ET

UR

NS

OF

CO

MM

ON

ST

OC

KIN

VE

ST

ME

NT

SO

FF

EM

AL

E

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eh

ouse

hold

s

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

G

ross

aver

age

hou

seho

ldpe

rcen

tage

mon

thly

retu

rns

Tw

o-fa

ctor

mod

elin

terc

ept

20

044

20

083

003

92

005

12

008

20

031

20

036

20

099

006

3T

wo-

fact

orm

odel

coef

cie

ntes

tim

ate

on(R

mt

2R

ft)

105

0

108

1

20

031

1

053

1

075

0

022

103

5

108

8

005

3

Tw

o-fa

ctor

mod

elco

efc

ient

esti

mat

eon

SM

Bt

036

0

051

9

20

159

0

380

0

490

0

109

0

307

0

582

0

275

A

djus

ted

R2

938

920

653

934

921

528

944

915

706

Pan

elB

N

etav

erag

eho

useh

old

perc

enta

gem

onth

lyre

turn

s

Tw

o-fa

ctor

mod

elin

terc

ept

20

162

20

253

0

091

2

017

1

20

245

0

074

2

014

2

20

285

0

143

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

H

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Hou

seho

lds

are

clas

si

edas

mar

ried

orsi

ngle

base

don

the

mar

ital

stat

usof

the

head

ofho

use

hold

Coe

fci

ent

and

inte

rcep

tes

tim

ates

for

the

two-

fact

orm

odel

are

thos

efr

oma

tim

e-se

ries

regr

essi

onof

the

gros

s(n

et)

aver

age

hou

seho

ldex

cess

retu

rnon

the

mar

ket

exce

ssre

turn

(Rm

t2

Rf

t)

and

aze

ro-i

nve

stm

ent

size

port

foli

o(S

MB

t)

(RM

tgr

2R

ft)

=a

i+

bi(

Rm

t2

Rf

t)

+s i

SM

Bt

+e

it
















IVA Risk Aversion


IVB Gambling















V CONCLUSION










REFERENCES


































































III RESULTS







TA

BL

EII

PO

SIT

ION

VA

LU

ET

UR

NO

VE

RA

ND

RE

TU

RN

PE

RF

OR

MA

NC

EO

FC

OM

MO

NS

TO

CK

INV

ES

TM

EN

TS

OF

FE

MA

LE

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eho

useh

olds

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

ren

ce(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

P

osit

ion

Val

ue

and

Tur

nove

rM

ean

[med

ian]

183

7121

975

23

604

17

754

222

932

453

9

196

5420

161

250

7

begi

nnin

gpo

siti

onva

lue

($)

[73

87]

[82

18]

[283

1]

[7

410

][8

175

][2

765]

[74

91]

[80

97]

[260

6]

Mea

n[m

edia

n]4

406

412

201

441

611

21

70

4

227

052

283

mon

thly

turn

over

()

[17

4][2

94]

[21

20]

[1

79]

[28

1][1

02]

[15

5][3

32]

[21

77]

Pan

elB

P

erfo

rman

ceO

wn-

benc

hmar

km

onth

ly2

004

1

20

069

0

028

2

005

0

20

068

0

018

20

029

20

074

0

045

abno

rmal

gros

sre

turn

()

(22

84)

(23

66)

(24

3)(2

289

)(2

367

)(1

28)

(21

64)

(23

60)

(25

3)

Ow

n-be

nchm

ark

mon

thly

20

143

2

022

1

007

8

20

154

2

021

4

006

0

20

121

2

024

2

012

0

abno

rmal

net

retu

rn(

)(2

970

)(2

108

3)(6

35)

(29

10)

(210

48)

(39

5)(2

668

)(2

111

5)(6

68)

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

T

ests

for

diff

eren

ces

inm

edia

ns

are

base

don

aW

ilcox

onsi

gn-r

ank

test

stat

isti

cH

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Beg

inni

ngpo

siti

onva

lue

isth

em

arke

tva

lue

ofco

mm

onst

ocks

held

inth

e

rst

mon

thth

atth

eho

use

hold

appe

ars

duri

ngou

rsa

mpl

epe

riod

M

ean

mon

thly

turn

over

isth

eav

erag

eof

sale

san

dpu

rch

ase

turn

over

[M

edia

nva

lues

are

inbr

acke

ts]

Ow

n-be

nch

mar

kab

norm

alre

turn

sar

eth

eav

erag

eh

ouse

hol

dpe

rcen

tage

mon

thly

abn

orm

alre

turn

calc

ula

ted

asth

ere

aliz

edm

onth

lyre

turn

for

ah

ouse

hol

dle

ssth

ere

turn

that

wou

ldha

vebe

enea

rned

ifth

eho

useh

old

had

held

the

begi

nnin

g-of

-yea

rpo

rtfo

lio

for

the

enti

reye

ar(i

e

the

twel

vem

onth

sbe

ginn

ing

Feb

ruar

y1)

T-s

tati

stic

sfo

rab

norm

alre

turn

sar

ein

pare

nthe

ses

and

are

calc

ulat

edu

sing

tim

e-se

ries

stan

dard

erro

rsac

ross

mon

ths






t 5(Rt

pm 2 Rtsm)

s (Rtpm 2 Rt

sm) Icirc 72





















Dependentvariable

Meanmonthlyturnover

()


Portfoliovolatility


Sizecoefcient

Intercept 6269 2 0321 11466 11658 1226+ 0776(2 1147) (5885) (7098) (1144) (2216)

Single 0483 0002 0320 0330 0020 0079(424) (014) (340) (417) (212) (465)

Woman 2 1461 0058 2 0689 2 0682 2 0037 2 0136(2 1276) (427) (2 727) (2 854) (2 391) (2 800)

Single 3 2 0733 0027 2 0439 2 0540 2 0029 2 0138woman (2 338) (108) (2 245) (2 357) (2 160) (2 430)

Age10 2 0311 0002 2 0536 2 0393 2 0027 2 0055(2 926) (423) (2 1931) (2 1678) (2 955) ( 2 1100)

Children 2 0037 0008 2 0014 2 0051 2 0002 2 0008(2 040) (076) (2 019) (2 079) (2 022) (2 061)

Income 2 0002 00002 00003 0001 0003 00011000 (2 130) (133) (022) (138) (249) (031)

Income 2 00003 0027 0011 0012 2 0008 2 0018dummy (2 024) (154) (010) (011) (2 068) (2 082)

Adj R2 () 153 020 211 195 059 119





IIID Portfolio Risk




where
















TA

BL

EIV

RIS

KE

XP

OS

UR

ES

AN

DR

ISK

-AD

JUS

TE

DR

ET

UR

NS

OF

CO

MM

ON

ST

OC

KIN

VE

ST

ME

NT

SO

FF

EM

AL

E

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eh

ouse

hold

s

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

G

ross

aver

age

hou

seho

ldpe

rcen

tage

mon

thly

retu

rns

Tw

o-fa

ctor

mod

elin

terc

ept

20

044

20

083

003

92

005

12

008

20

031

20

036

20

099

006

3T

wo-

fact

orm

odel

coef

cie

ntes

tim

ate

on(R

mt

2R

ft)

105

0

108

1

20

031

1

053

1

075

0

022

103

5

108

8

005

3

Tw

o-fa

ctor

mod

elco

efc

ient

esti

mat

eon

SM

Bt

036

0

051

9

20

159

0

380

0

490

0

109

0

307

0

582

0

275

A

djus

ted

R2

938

920

653

934

921

528

944

915

706

Pan

elB

N

etav

erag

eho

useh

old

perc

enta

gem

onth

lyre

turn

s

Tw

o-fa

ctor

mod

elin

terc

ept

20

162

20

253

0

091

2

017

1

20

245

0

074

2

014

2

20

285

0

143

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

H

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Hou

seho

lds

are

clas

si

edas

mar

ried

orsi

ngle

base

don

the

mar

ital

stat

usof

the

head

ofho

use

hold

Coe

fci

ent

and

inte

rcep

tes

tim

ates

for

the

two-

fact

orm

odel

are

thos

efr

oma

tim

e-se

ries

regr

essi

onof

the

gros

s(n

et)

aver

age

hou

seho

ldex

cess

retu

rnon

the

mar

ket

exce

ssre

turn

(Rm

t2

Rf

t)

and

aze

ro-i

nve

stm

ent

size

port

foli

o(S

MB

t)

(RM

tgr

2R

ft)

=a

i+

bi(

Rm

t2

Rf

t)

+s i

SM

Bt

+e

it
















IVA Risk Aversion


IVB Gambling















V CONCLUSION










REFERENCES


































































TA

BL

EII

PO

SIT

ION

VA

LU

ET

UR

NO

VE

RA

ND

RE

TU

RN

PE

RF

OR

MA

NC

EO

FC

OM

MO

NS

TO

CK

INV

ES

TM

EN

TS

OF

FE

MA

LE

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eho

useh

olds

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

ren

ce(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

P

osit

ion

Val

ue

and

Tur

nove

rM

ean

[med

ian]

183

7121

975

23

604

17

754

222

932

453

9

196

5420

161

250

7

begi

nnin

gpo

siti

onva

lue

($)

[73

87]

[82

18]

[283

1]

[7

410

][8

175

][2

765]

[74

91]

[80

97]

[260

6]

Mea

n[m

edia

n]4

406

412

201

441

611

21

70

4

227

052

283

mon

thly

turn

over

()

[17

4][2

94]

[21

20]

[1

79]

[28

1][1

02]

[15

5][3

32]

[21

77]

Pan

elB

P

erfo

rman

ceO

wn-

benc

hmar

km

onth

ly2

004

1

20

069

0

028

2

005

0

20

068

0

018

20

029

20

074

0

045

abno

rmal

gros

sre

turn

()

(22

84)

(23

66)

(24

3)(2

289

)(2

367

)(1

28)

(21

64)

(23

60)

(25

3)

Ow

n-be

nchm

ark

mon

thly

20

143

2

022

1

007

8

20

154

2

021

4

006

0

20

121

2

024

2

012

0

abno

rmal

net

retu

rn(

)(2

970

)(2

108

3)(6

35)

(29

10)

(210

48)

(39

5)(2

668

)(2

111

5)(6

68)

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

T

ests

for

diff

eren

ces

inm

edia

ns

are

base

don

aW

ilcox

onsi

gn-r

ank

test

stat

isti

cH

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Beg

inni

ngpo

siti

onva

lue

isth

em

arke

tva

lue

ofco

mm

onst

ocks

held

inth

e

rst

mon

thth

atth

eho

use

hold

appe

ars

duri

ngou

rsa

mpl

epe

riod

M

ean

mon

thly

turn

over

isth

eav

erag

eof

sale

san

dpu

rch

ase

turn

over

[M

edia

nva

lues

are

inbr

acke

ts]

Ow

n-be

nch

mar

kab

norm

alre

turn

sar

eth

eav

erag

eh

ouse

hol

dpe

rcen

tage

mon

thly

abn

orm

alre

turn

calc

ula

ted

asth

ere

aliz

edm

onth

lyre

turn

for

ah

ouse

hol

dle

ssth

ere

turn

that

wou

ldha

vebe

enea

rned

ifth

eho

useh

old

had

held

the

begi

nnin

g-of

-yea

rpo

rtfo

lio

for

the

enti

reye

ar(i

e

the

twel

vem

onth

sbe

ginn

ing

Feb

ruar

y1)

T-s

tati

stic

sfo

rab

norm

alre

turn

sar

ein

pare

nthe

ses

and

are

calc

ulat

edu

sing

tim

e-se

ries

stan

dard

erro

rsac

ross

mon

ths






t 5(Rt

pm 2 Rtsm)

s (Rtpm 2 Rt

sm) Icirc 72





















Dependentvariable

Meanmonthlyturnover

()


Portfoliovolatility


Sizecoefcient

Intercept 6269 2 0321 11466 11658 1226+ 0776(2 1147) (5885) (7098) (1144) (2216)

Single 0483 0002 0320 0330 0020 0079(424) (014) (340) (417) (212) (465)

Woman 2 1461 0058 2 0689 2 0682 2 0037 2 0136(2 1276) (427) (2 727) (2 854) (2 391) (2 800)

Single 3 2 0733 0027 2 0439 2 0540 2 0029 2 0138woman (2 338) (108) (2 245) (2 357) (2 160) (2 430)

Age10 2 0311 0002 2 0536 2 0393 2 0027 2 0055(2 926) (423) (2 1931) (2 1678) (2 955) ( 2 1100)

Children 2 0037 0008 2 0014 2 0051 2 0002 2 0008(2 040) (076) (2 019) (2 079) (2 022) (2 061)

Income 2 0002 00002 00003 0001 0003 00011000 (2 130) (133) (022) (138) (249) (031)

Income 2 00003 0027 0011 0012 2 0008 2 0018dummy (2 024) (154) (010) (011) (2 068) (2 082)

Adj R2 () 153 020 211 195 059 119





IIID Portfolio Risk




where
















TA

BL

EIV

RIS

KE

XP

OS

UR

ES

AN

DR

ISK

-AD

JUS

TE

DR

ET

UR

NS

OF

CO

MM

ON

ST

OC

KIN

VE

ST

ME

NT

SO

FF

EM

AL

E

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eh

ouse

hold

s

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

G

ross

aver

age

hou

seho

ldpe

rcen

tage

mon

thly

retu

rns

Tw

o-fa

ctor

mod

elin

terc

ept

20

044

20

083

003

92

005

12

008

20

031

20

036

20

099

006

3T

wo-

fact

orm

odel

coef

cie

ntes

tim

ate

on(R

mt

2R

ft)

105

0

108

1

20

031

1

053

1

075

0

022

103

5

108

8

005

3

Tw

o-fa

ctor

mod

elco

efc

ient

esti

mat

eon

SM

Bt

036

0

051

9

20

159

0

380

0

490

0

109

0

307

0

582

0

275

A

djus

ted

R2

938

920

653

934

921

528

944

915

706

Pan

elB

N

etav

erag

eho

useh

old

perc

enta

gem

onth

lyre

turn

s

Tw

o-fa

ctor

mod

elin

terc

ept

20

162

20

253

0

091

2

017

1

20

245

0

074

2

014

2

20

285

0

143

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

H

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Hou

seho

lds

are

clas

si

edas

mar

ried

orsi

ngle

base

don

the

mar

ital

stat

usof

the

head

ofho

use

hold

Coe

fci

ent

and

inte

rcep

tes

tim

ates

for

the

two-

fact

orm

odel

are

thos

efr

oma

tim

e-se

ries

regr

essi

onof

the

gros

s(n

et)

aver

age

hou

seho

ldex

cess

retu

rnon

the

mar

ket

exce

ssre

turn

(Rm

t2

Rf

t)

and

aze

ro-i

nve

stm

ent

size

port

foli

o(S

MB

t)

(RM

tgr

2R

ft)

=a

i+

bi(

Rm

t2

Rf

t)

+s i

SM

Bt

+e

it
















IVA Risk Aversion


IVB Gambling















V CONCLUSION










REFERENCES






































































t 5(Rt

pm 2 Rtsm)

s (Rtpm 2 Rt

sm) Icirc 72





















Dependentvariable

Meanmonthlyturnover

()


Portfoliovolatility


Sizecoefcient

Intercept 6269 2 0321 11466 11658 1226+ 0776(2 1147) (5885) (7098) (1144) (2216)

Single 0483 0002 0320 0330 0020 0079(424) (014) (340) (417) (212) (465)

Woman 2 1461 0058 2 0689 2 0682 2 0037 2 0136(2 1276) (427) (2 727) (2 854) (2 391) (2 800)

Single 3 2 0733 0027 2 0439 2 0540 2 0029 2 0138woman (2 338) (108) (2 245) (2 357) (2 160) (2 430)

Age10 2 0311 0002 2 0536 2 0393 2 0027 2 0055(2 926) (423) (2 1931) (2 1678) (2 955) ( 2 1100)

Children 2 0037 0008 2 0014 2 0051 2 0002 2 0008(2 040) (076) (2 019) (2 079) (2 022) (2 061)

Income 2 0002 00002 00003 0001 0003 00011000 (2 130) (133) (022) (138) (249) (031)

Income 2 00003 0027 0011 0012 2 0008 2 0018dummy (2 024) (154) (010) (011) (2 068) (2 082)

Adj R2 () 153 020 211 195 059 119





IIID Portfolio Risk




where
















TA

BL

EIV

RIS

KE

XP

OS

UR

ES

AN

DR

ISK

-AD

JUS

TE

DR

ET

UR

NS

OF

CO

MM

ON

ST

OC

KIN

VE

ST

ME

NT

SO

FF

EM

AL

E

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eh

ouse

hold

s

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

G

ross

aver

age

hou

seho

ldpe

rcen

tage

mon

thly

retu

rns

Tw

o-fa

ctor

mod

elin

terc

ept

20

044

20

083

003

92

005

12

008

20

031

20

036

20

099

006

3T

wo-

fact

orm

odel

coef

cie

ntes

tim

ate

on(R

mt

2R

ft)

105

0

108

1

20

031

1

053

1

075

0

022

103

5

108

8

005

3

Tw

o-fa

ctor

mod

elco

efc

ient

esti

mat

eon

SM

Bt

036

0

051

9

20

159

0

380

0

490

0

109

0

307

0

582

0

275

A

djus

ted

R2

938

920

653

934

921

528

944

915

706

Pan

elB

N

etav

erag

eho

useh

old

perc

enta

gem

onth

lyre

turn

s

Tw

o-fa

ctor

mod

elin

terc

ept

20

162

20

253

0

091

2

017

1

20

245

0

074

2

014

2

20

285

0

143

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

H

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Hou

seho

lds

are

clas

si

edas

mar

ried

orsi

ngle

base

don

the

mar

ital

stat

usof

the

head

ofho

use

hold

Coe

fci

ent

and

inte

rcep

tes

tim

ates

for

the

two-

fact

orm

odel

are

thos

efr

oma

tim

e-se

ries

regr

essi

onof

the

gros

s(n

et)

aver

age

hou

seho

ldex

cess

retu

rnon

the

mar

ket

exce

ssre

turn

(Rm

t2

Rf

t)

and

aze

ro-i

nve

stm

ent

size

port

foli

o(S

MB

t)

(RM

tgr

2R

ft)

=a

i+

bi(

Rm

t2

Rf

t)

+s i

SM

Bt

+e

it
















IVA Risk Aversion


IVB Gambling















V CONCLUSION










REFERENCES




















































































Dependentvariable

Meanmonthlyturnover

()


Portfoliovolatility


Sizecoefcient

Intercept 6269 2 0321 11466 11658 1226+ 0776(2 1147) (5885) (7098) (1144) (2216)

Single 0483 0002 0320 0330 0020 0079(424) (014) (340) (417) (212) (465)

Woman 2 1461 0058 2 0689 2 0682 2 0037 2 0136(2 1276) (427) (2 727) (2 854) (2 391) (2 800)

Single 3 2 0733 0027 2 0439 2 0540 2 0029 2 0138woman (2 338) (108) (2 245) (2 357) (2 160) (2 430)

Age10 2 0311 0002 2 0536 2 0393 2 0027 2 0055(2 926) (423) (2 1931) (2 1678) (2 955) ( 2 1100)

Children 2 0037 0008 2 0014 2 0051 2 0002 2 0008(2 040) (076) (2 019) (2 079) (2 022) (2 061)

Income 2 0002 00002 00003 0001 0003 00011000 (2 130) (133) (022) (138) (249) (031)

Income 2 00003 0027 0011 0012 2 0008 2 0018dummy (2 024) (154) (010) (011) (2 068) (2 082)

Adj R2 () 153 020 211 195 059 119





IIID Portfolio Risk




where
















TA

BL

EIV

RIS

KE

XP

OS

UR

ES

AN

DR

ISK

-AD

JUS

TE

DR

ET

UR

NS

OF

CO

MM

ON

ST

OC

KIN

VE

ST

ME

NT

SO

FF

EM

AL

E

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eh

ouse

hold

s

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

G

ross

aver

age

hou

seho

ldpe

rcen

tage

mon

thly

retu

rns

Tw

o-fa

ctor

mod

elin

terc

ept

20

044

20

083

003

92

005

12

008

20

031

20

036

20

099

006

3T

wo-

fact

orm

odel

coef

cie

ntes

tim

ate

on(R

mt

2R

ft)

105

0

108

1

20

031

1

053

1

075

0

022

103

5

108

8

005

3

Tw

o-fa

ctor

mod

elco

efc

ient

esti

mat

eon

SM

Bt

036

0

051

9

20

159

0

380

0

490

0

109

0

307

0

582

0

275

A

djus

ted

R2

938

920

653

934

921

528

944

915

706

Pan

elB

N

etav

erag

eho

useh

old

perc

enta

gem

onth

lyre

turn

s

Tw

o-fa

ctor

mod

elin

terc

ept

20

162

20

253

0

091

2

017

1

20

245

0

074

2

014

2

20

285

0

143

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

H

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Hou

seho

lds

are

clas

si

edas

mar

ried

orsi

ngle

base

don

the

mar

ital

stat

usof

the

head

ofho

use

hold

Coe

fci

ent

and

inte

rcep

tes

tim

ates

for

the

two-

fact

orm

odel

are

thos

efr

oma

tim

e-se

ries

regr

essi

onof

the

gros

s(n

et)

aver

age

hou

seho

ldex

cess

retu

rnon

the

mar

ket

exce

ssre

turn

(Rm

t2

Rf

t)

and

aze

ro-i

nve

stm

ent

size

port

foli

o(S

MB

t)

(RM

tgr

2R

ft)

=a

i+

bi(

Rm

t2

Rf

t)

+s i

SM

Bt

+e

it
















IVA Risk Aversion


IVB Gambling















V CONCLUSION










REFERENCES



































































IIID Portfolio Risk




where
















TA

BL

EIV

RIS

KE

XP

OS

UR

ES

AN

DR

ISK

-AD

JUS

TE

DR

ET

UR

NS

OF

CO

MM

ON

ST

OC

KIN

VE

ST

ME

NT

SO

FF

EM

AL

E

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eh

ouse

hold

s

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

G

ross

aver

age

hou

seho

ldpe

rcen

tage

mon

thly

retu

rns

Tw

o-fa

ctor

mod

elin

terc

ept

20

044

20

083

003

92

005

12

008

20

031

20

036

20

099

006

3T

wo-

fact

orm

odel

coef

cie

ntes

tim

ate

on(R

mt

2R

ft)

105

0

108

1

20

031

1

053

1

075

0

022

103

5

108

8

005

3

Tw

o-fa

ctor

mod

elco

efc

ient

esti

mat

eon

SM

Bt

036

0

051

9

20

159

0

380

0

490

0

109

0

307

0

582

0

275

A

djus

ted

R2

938

920

653

934

921

528

944

915

706

Pan

elB

N

etav

erag

eho

useh

old

perc

enta

gem

onth

lyre

turn

s

Tw

o-fa

ctor

mod

elin

terc

ept

20

162

20

253

0

091

2

017

1

20

245

0

074

2

014

2

20

285

0

143

indi

cate

sign

ica

ntat

the

15

and

10pe

rcen

tle

vel

resp

ecti

vely

H

ouse

hold

sar

ecl

assi

ed

asfe

mal

eor

mal

eba

sed

onth

ege

nder

ofth

epe

rson

wh

oop

ened

the

acco

unt

Hou

seho

lds

are

clas

si

edas

mar

ried

orsi

ngle

base

don

the

mar

ital

stat

usof

the

head

ofho

use

hold

Coe

fci

ent

and

inte

rcep

tes

tim

ates

for

the

two-

fact

orm

odel

are

thos

efr

oma

tim

e-se

ries

regr

essi

onof

the

gros

s(n

et)

aver

age

hou

seho

ldex

cess

retu

rnon

the

mar

ket

exce

ssre

turn

(Rm

t2

Rf

t)

and

aze

ro-i

nve

stm

ent

size

port

foli

o(S

MB

t)

(RM

tgr

2R

ft)

=a

i+

bi(

Rm

t2

Rf

t)

+s i

SM

Bt

+e

it
















IVA Risk Aversion


IVB Gambling















V CONCLUSION










REFERENCES












































































TA

BL

EIV

RIS

KE

XP

OS

UR

ES

AN

DR

ISK

-AD

JUS

TE

DR

ET

UR

NS

OF

CO

MM

ON

ST

OC

KIN

VE

ST

ME

NT

SO

FF

EM

AL

E

AN

DM

AL

EH

OU

SE

HO

LD

S F

EB

RU

AR

Y19

91T

OJA

NU

AR

Y19

97

All

hou

seho

lds

Mar

ried

hous

ehol

dsS

ingl

eh

ouse

hold

s

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)W

omen

Men

Dif

fere

nce

(wom

enndashm

en)

Wom

enM

enD

iffe

renc

e(w

omen

ndashmen

)

Nu

mbe

rof

hou

seho

lds

800

529

659

NA

489

419

741

NA

230

66

326

NA

Pan

elA

G

ross

aver

age

hou

seho

ldpe

rcen

tage

mon

thly

retu

rns

Tw

o-fa

ctor

mod

elin

terc

ept

20

044

20

083

003

92

005

12

008

20

031

20

036

20

099

006

3T

wo-

fact

orm

odel

coef

cie

ntes

tim

ate

on(R

mt

2R

ft)

105

0

108

1

20

031

1

053

1

075

0

022

103

5

108

8

005

3

Tw

o-fa

ctor

mod

elco

efc

ient

esti

mat

eon

SM

Bt

036

0

051

9

20

159

0

380

0

490

0

109

0

307

0

582

0

275

A

djus

ted

R2

938

920

653

934

921

528

944

915

706

Pan

elB

N

etav

erag

eho

useh

old

perc

enta

gem

onth

lyre

turn

s

Tw

o-fa

ctor

mod

elin

terc

ept

20

162

20

253

0

091

2

017

1

20

245

0

074

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IVA Risk Aversion


IVB Gambling















V CONCLUSION










REFERENCES
















































































IVA Risk Aversion


IVB Gambling















V CONCLUSION










REFERENCES












































































V CONCLUSION










REFERENCES







































































REFERENCES


































































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