Online Appendix for

“Market Maturity and Mispricing”

Heiko Jacobs∗

December 2015

Abstract

Table 1 shows computational details for the eleven anomalies un-

derlying the Stambaugh, Yu, and Yuan (2015) mispricing score.

The table additionally provides information about the construction

of 20 additional cross-sectional anomalies relied on in the paper.

For each country separately, Table 2 shows the sample period and monthly

Fama and French (1993) three-factor alphas obtained from the Stambaugh, Yu,

and Yuan (2015) mispricing measure. On a country-by-country basis, Tables

3 to 13 provide sample periods and alphas for each of the eleven anomalies

entering the aggregate Stambaugh, Yu, and Yuan (2015) mispricing score.

Separately for emerging markets and developed markets, Table 14 reports

∗Heiko Jacobs, Finance Department, University of Mannheim, L5,2, 68131 Mannheim, Germany. E-Mail:

jacobs@bank.bwl.uni-mannheim.de. Phone: +49 621 181 3453.

1

alphas obtained from an alternative measure of aggregate mispricing based

on 20 further anomalies (as described in Table 1 of this Online Appendix).

Table 15 reports alphas generated by a mispricing measure which takes both

the Stambaugh, Yu, and Yuan (2015) anomalies and the additional anomalies

of Table 14 of this Online Appendix into account. Separately for emerging

markets and developed markets, Table 16 reports alphas generated by three

aggregate mispricing measures computed from different groups of anomalies.

On a country-by-country basis, Figure 1 to 45 illustrate the fraction of

stocks with valid (individual or composite) anomaly rankings over time.

Table 17 compares the fraction of stocks with valid (individual or compos-

ite) anomaly rankings in developed markets relative to emerging markets.

On a country-by-country basis, Figures 46 to 49 compare the average

number of individual anomalies underlying the composite Stambaugh, Yu, and

Yuan (2015) mispricing score, conditional on non-missing values of the score

(i.e., conditional on the availability of at least five individual anomaly ranks).

Table 18 provides descriptive statistics of the data underlying Figures 46 to 49.

Separately for each year, Figure 50 compares the average number of in-

dividual anomalies underlying the composite Stambaugh, Yu, and Yuan (2015)

mispricing score between the average developed market and the average emerg-

ing market. Table 19 more formally tests for differences between developed

markets and emerging markets with respect to the average number of indi-

vidual anomalies underlying the composite Stambaugh, Yu, and Yuan (2015)

mispricing score. Separately for each year, Figure 51 compares the average

2

number of individual anomalies underlying the composite Stambaugh, Yu, and

Yuan (2015) mispricing score between the pooled stock-level observations in

developed markets and the pooled stock-level observations in emerging markets.

Figures 52 to 55 illustrate the distribution of the alpha difference be-

tween developed markets and emerging markets with respect to sim-

ulated composite mispricing based on five randomly selected indi-

vidual anomalies. In this context, Figure 52 and 53 concentrate on

the Stambaugh, Yu, and Yuan (2015) anomalies, whereas Figures 54

and 55 additionally take the 20 alternative anomalies into account.

References are provided on the last six pages.

3

Table

1:Com

putationofanomalies

inthecross-sectionofexpectedequityreturns

Anomaly

Description

Refere

nces

Portfolios

Holding

Data

base

sComputa

tionaldeta

ils

1.Sta

mbaugh,Yu,and

Yuan

(2015)anomalies

Failure

probabil-

ity

Firms

with

low

failure

profitability

outperform

firm

swith

high

failure

probability.

Campbell,

Hilscher,

and

Szilagyi

(2008),

Con-

rad,Kapadia,

and

Xing

(2014)

5failure

prob-

ability(1-5)

twelvemonths

CRSP,

Com-

pustat,

Datastream,

Worldscope

Thecomputation

ofthedistressmeasure

closely

follow

sthemethod

outlined

inTable

IVofCampbell,Hilscher,andSzilagyi(2008).

More

specifically,

distressrisk

iscomputed

as-9.164-20.264*NIM

TAAVG

+1.416*TLMTA

-7.129*EXRETAV

+1.411*SIG

MA

-0.045*RSIZE

-2.132*CASHMTA

+0.075*MB

-0.058*PRIC

E.As

common

in

theliterature

and

inorder

toav

oid

look-ahead

biasand

toassure

comparability

across

countries,

Imatch

accounting

data

for

the

fiscal-yearen

dofyeartwithstock

return

data

from

July

ofyeart+

1

untilJuneofyeart+

2.Market

equityis

updatedmonthly.NIM

TA

is

net

income(C

ompustatannualitem

NIforU.S.data,Worldcopeitem

WC01551forinternationaldata)divided

bythesum

ofmarket

equity

andtotalliabilities(LT,W

C03351).NIM

TAAVG

isequalto

(1-γ

3)/(1-

γ12)*[N

IM

TA

t−1+γ3*NIM

TA

t−4+γ6*NIM

TA

t−7+γ9*NIM

TA

t−10].

Inthis

context,

γis

equalto

2−1/3andtrefers

tomonth

t.TLMTA

istotalliabilities

divided

by

the

sum

ofmarket

equity

and

total

liabilities.

EXRET

isthemonthly

log

firm

excess

return

relativeto

theva

lue-weightedcountrymarket

index,andEXRETAV

is(1-γ

3)/(1-

γ12)*[(EXRETt−

1+...+

γ11*EXRETt−

12].

Inthis

context,

γis

equal

to2−1/3andtrefers

tomonth

t.SIG

MA

isthestandard

deviationof

thefirm

’sdailystock

return

over

thepast

3months.RSIZE

isupdated

monthly

andcomputedasthelogratioofthefirm

’sonemonth

lagged

market

capitalization

relative

tothe

country

market

capitalization.

CASHMTA

istheratio

ofcash

and

short-term

investm

ents

(CHE,

WC02001)divided

bythesum

ofmarket

equityand

totalliabilities.

MB

isthemarket-to-book

ratio.Tomitigate

theim

pact

ofoutliers,

Iadd

10%

ofthedifferen

cebetween

market

and

bookequityto

the

book

valueoftotalassets.

PRIC

Eis

thefirm

’slog

price

per

share

(in

USD,onemonth

lagged

),truncated

aboveat$15

and

below

at

$1.NIM

TAAVG,TLMTA,EXRETAV,SIG

MA,RSIZE,CASHMTA,

MB,andPRIC

Eare

winsorizedatthe5%

andthe95%

level.

[Continued

overleaf]

4

Anomaly

Description

Refere

nces

Portfolios

Typ.

Hold-

ing

Data

base

sComputa

tionaldeta

ils

Ohlson’s

O(dis-

tress)

Financially

healthy

firm

s

outperform

firm

sin

finan-

cialdistress.

Ohlson(1980)

5distress

(1-

5)

twelvemonths

Compustat,

Worldscope

Icompute

distress

(“O-score”)

as

-1.32

-0.407*log(T

A/CPI)

+

6.03*TLTA

-1.43*W

CTA

+0.076*CLCA

-1.72*OENEG

-2.37*NIT

A

-1.83*FUTL+

0.285*IN

TW

O-0.521*CHIN

.Imatchaccountingdata

forthefiscal-yearen

dofyeartwith

stock

return

data

from

July

of

yeart+

1untilJuneofyeart+

2.In

thiscontext,TA

istotalassets(A

T,

WC02999)andCPIis

thecountry-specificconsumer

price

index

(ob-

tained

from

theFed

eralReserveBankandDatastream,resp

ectively).

TLTA

istotalliabilities(D

LC+DLTT,W

C03351)divided

bytotalas-

sets.W

CTA

isworkingcapital(A

CT-LCT,W

C02201-W

C03101)di-

vided

bytotalassets.

CLCA

iscu

rren

tliabilities(L

CT,W

C03101)di-

vided

bycu

rren

tassets(item

ACT,W

C02201).

OENEG

isadummy

variable

whichis

oneif

totalliabilitiesexceed

stotalassets,

andzero

otherwise.

NIT

Ais

net

income(N

I,W

C01551)divided

bytotalassets.

FUTL

isthefundprovided

byoperations(P

I,W

C04201)divided

by

totalliabilities.IN

TW

Oisadummyva

riable

equalto

oneifnet

income

isnegativeforthelast

twoyears

andzero

otherwise.

CHIN

is(N

Iin

t

-NIin

t-1)/(|N

int|+|N

int-1|).

Net

stock

issues

Returns

are

negatively

related

toan-

nualnet

stock

issuance.

Ritter(1991),

Loughran

and

Ritter

(1995),

Fama

and

French

(2008),

Pon-

tiff

and

Woodgate

(2008)

5issuance

(1-

5)

onemonth

CRSP,Datas-

tream

Idefi

nenet

stock

issues

asthelogratioofsplit-adjusted

shares(based

oncfacshrandshroutfortheU.S.aswellasonNOSH

andAFforinter-

nationalmarkets)

outstandingin

month

t-1andsplit-adjusted

shares

outstandingin

month

t-13.

Composite

equity

Returns

are

negatively

related

to

composite

equity

is-

suance

(5

yearwindow

).

Ritter(1991),

Loughran

and

Ritter

(1995),Daniel

and

Titman

(2006)

5compos-

ite

equity

issuance

(1-5)

onemonth

CRSP,Datas-

tream

Idefi

necomposite

equityissuance

aslog(m

arket

capitalizationin

month

t-1/market

capitalizationin

month

t-61)minusthecu

mulativelogre-

turn

over

thesametimeperiod.

[Continued

overleaf]

5

Anomaly

Description

Refere

nces

Portfolios

Typ.

Hold-

ing

Data

base

sComputa

tionaldeta

ils

Accruals

Stocks

of

firm

swith

low

accruals

outperform

firm

swith

highaccruals.

Dechow

,

Sloan,

and

Sweeney

(1995),

Sloan

(1996)

5accruals

(1-

5)

twelvemonths

Compustat,

Worldscope

Inspired

by

Dechow

,Sloan,and

Sweeney

(1995),

Leu

z,Nanda,and

Wysock

(2004),

and

Sloan

(1996),

accruals

are

defi

ned

as(annual

changein

curren

tassets(A

CT,W

C02201)-annualchangein

total

cash

andshort-term

investm

ents

(CHE,W

C02001)-annualchangein

curren

tliabilities(L

CT,W

C03101)+

annualchangein

short

term

deb

t

(DLC,W

C03051)-annualdep

reciation,dep

letionandamortizationex-

pen

sein

yeart(D

P,W

C01151))/(0.5

*totalassetsin

yeart+

0.5

*

totalassetsin

yeart-1).Imatchaccountingdata

forthefiscal-yearen

d

ofyeartwithstock

return

data

from

July

ofyeart+

1untilJuneof

yeart+

2.

Net

op-

erating

assets

Net

operating

assets

scaled

by

total

assets

nega-

tively

predict

returns.

Hirshleifer,

Hou,

Teoh,

and

Zhang

(2004)

5net

operat-

ing

assets(1-

5)

twelvemonths

Compustat,

Worldscope

Inspired

byHirshleifer,Hou,Teoh,and

Zhang(2004),

net

operating

assetsare

defi

ned

as(operatingassets-operatingliabilities)/one

yearlagged

totalassets,

whereoperatingassets=

totalassets(A

T,

WC02999)-cash

and

short-term

Investm

ent(C

HE,W

C02001)and

whereoperatingliabilities=

totalassets-short

term

and

longterm

deb

t(D

LC/DLTT,W

C03255)-minority

interest

(MIB

,W

C03426)-

preferred

stock

andcommonequity(U

.S.market:PSTKRV

/PSTKLif

available+

CEQ,internationalmarkets:W

C03995).Imatchaccounting

data

forthefiscal-yearen

dofyeartwithstock

return

data

from

July

ofyeart+

1untilJuneofyeart+

2.

Momen

tum

Winners

of

the

recent

past

outper-

form

losers

ofthe

recent

past.

Asness,

Moskow

itz,

and

Ped

ersen

(2013),

Chui,

Titman,

and

Wei

(2010),

Jegadeesh

and

Tit-

man

(1993),

Jegadeesh

and

Tit-

man

(2001),

Rouwen

horst

(1998)

5past

returns

(5-1)

sixmonths

CRSP,Datas-

tream

Icompute

themomen

tum

form

ationperiodreturn

asthecu

mulative

return

over

themonthst-6to

t-1.In

unreported

robustnessch

ecks,

I

findthatusingaskipped

month

oralonger

form

ationperiod(upto

twelvemonths)

does

notch

angeinferences.

Ascommonin

themomen

-

tum

literature,Iconstruct

overlappingportfoliosin

thateligible

stocks

are

sorted

into

portfoliosatthebeginningofeach

month

andheldin

theseportfoliosforsixmonths.Themomen

tum

return

inagiven

month

istheequallyweightedav

erageoftheov

erlappingportfolioreturnsin

thatmonth.

[Continued

overleaf]

6

Anomaly

Description

Refere

nces

Portfolios

Typ.

Hold-

ing

Data

base

sComputa

tionaldeta

ils

Gross

profitabil-

ity

Profitability

positively

predicts

returns.

Nov

y-M

arx

(2013)

5gross

prof-

itability(5-1)

twelvemonths

Compustat,

Worldscope

Gross

profitabilityis

measuredas(reven

ues

minuscost

ofgoodssold

(REVT-C

OGS,W

C01100))/totalassets.

Asset

growth

Total

asset

growth

nega-

tivelypredicts

returns.

Cooper,

Gulen,

and

Schill

(2008),

Titman,Wei,

and

Xie

(2013)

5asset

growth

(1-5)

twelvemonths

Compustat,

Worldscope

Asset

growth

isestimatedasthepercentagech

angeoftotalassetsof

thefiscalyearen

dingin

calendaryeart-2to

thefiscalyearen

dingin

calendaryeart-1.

Return

on

assets

Return

on

assets

posi-

tivelypredicts

returns.

Chen

,Nov

y-

Marx,

and

Zhang(2011),

Fama

and

French

(2006)

5return

toas-

sets

(5-1)

twelvemonths

Compustat,

Worldscope

FortheU.S.stock

market,return

onassetsisdefi

ned

asyearlyearnings

(IB)/oneyearlagged

totalassets.

Forinternationalmarkets,

return

onassetsis

defi

ned

byWorldscopeitem

WC08326.Imatchaccounting

data

forthefiscal-yearen

dofyeartwithstock

return

data

from

July

ofyeart+

1untilJuneofyeart+

2.

Investm

ent-

to-assets

Scaled

capital

investm

ents

negatively

predict

re-

turns.

Titman,Wei,

and

Xie

(2004),

Xing

(2008)

5capital

in-

vestm

ents

(1-

5)

twelvemonths

Compustat,

Worldscope

Inspired

byChen

,Nov

y-M

arx,andZhang(2011)andLyandres,

Sun,

andZhang(2008),

investm

ent-to-assetsare

defi

ned

as(annualch

ange

ingross

property,plant,andequipmen

t(P

PEGT,W

C02301)+

annual

changein

inventories

(INVT,W

C02101))

/oneyearlagged

totalassets.

Imatch

accountingdata

forthefiscal-yearen

dofyeartwith

stock

return

data

from

July

ofyeart+

1untilJuneofyeart+

2.

Furtheranomalies

Low

volatility

anomaly

Firms

with

low

return

volatility

out-

perform

firm

s

with

high

volatility.

Baker,

Bradley,

and

Wurgler

(2011),

Black

(1972),

Hau-

gen

andHeins

(1975)

5volatility

(1-

5)

onemonth

CRSP,Datas-

tream

Imeasure

volatility

asthestandard

deviationofthemonthly

returns

over

thepreviousfiveyears.Irequireatleast

36va

lidmonthly

return

observations.

Portfolio1den

otesfirm

swiththelowestvolatility.

[Continued

overleaf]

7

Anomaly

Description

Refere

nces

Portfolios

Typ.

Hold-

ing

Data

base

sComputa

tionaldeta

ils

Low

beta

anomaly

Firms

with

low

ex-ante

beta

outper-

form

firm

s

with

high

ex-ante

beta.

Frazzini

and

Ped

ersen

(2014),

Hong

and

Sraer

(2015)

5beta(1-5)

onemonth

CRSP,Datas-

tream

Iestimate

betasfrom

rollingregressionsofdailyexcess

stock

returnson

excess

(country)market

returns.

Iuse

dailyreturnsov

ertheprevious

twelvemonthsandrequireatleast

200va

lidobservations.

Portfolio1

den

otesfirm

swiththelowestbeta.In

unreported

robustnessch

ecks,

I

havealsoestimatedlow-frequen

cybetasbasedonmonthly

data

over

thepreviousfiveyears.Inferencesdon’t

change.

Idiosyn-

cratic

risk

anomaly

Firms

with

low

idiosyn-

cratic

volatil-

ity

outper-

form

firm

s

with

high

idiosyncratic

volatility.

Ang,Hodrick,

Xing,

and

Zhang(2006),

Ang,Hodrick,

Xing,

and

Zhang(2009)

5idiosyn-

cratik

risk

(1-5)

onemonth

CRSP,Datas-

tream

Imeasure

idiosyncraticrisk

asthestandard

deviationoftheresidual

obtained

from

regressingastock’s

excess

return

onacountry-specific

Famaand

French

(1993)three-factormodel.Iuse

dailyreturnsov

er

theprevioustw

elvemonthsandrequireatleast

200va

lidobservations.

Portfolio1den

otesfirm

swith

thelowestidiosyncraticrisk.In

unre-

ported

robustnesschecks,

Ihav

ealsoestimatedlow-frequen

cyidiosyn-

craticvolatility

basedonthemarket

model

andonmonthly

data

over

thepreviousfiveyears.Inferencesdon’t

change.

Maxim

um

daily

return

anomaly

The

maxi-

mum

daily

return

over

the

previous

month

nega-

tivelypredicts

returns.

Bali,

Cakici,

andW

hitelaw

(2011)

5daily

max

return

(1-5)

onemonth

CRSP,Datas-

tream

Portfoliosortingsare

basedonthemaxim

um

dailyreturn

(measured

inlocalcu

rren

cy)ov

erthepreviousmonth.Portfolio1den

otesfirm

s

withthelowestmaxim

um

return.In

unreported

robustnessch

ecks,

I

havealsoestimatedthemaxim

um

abnorm

alreturn

basedonthemarket

model.Inferencesdon’t

change.

Lottery-

type

stocks

anomaly

Stocks

with

lottery

fea-

tures

un-

derperform

non-lottery

stocks.

Kumar(2009)

2(lottery

stocks

-

non-lottery

stocks)

onemonth

CRSP,Datas-

tream

Asin

Kumar(2009),

Idefi

nea

(non)-lottery-typestock

asa

stock

withabov

e(below

)med

ianidiosyncraticvolatility,abov

e(below

)me-

dianidiosyncraticskew

nessandbelow

(abov

e)med

iannominalstock

price.Imeasure

idiosyncraticvolatility

asthestandard

deviation

of

theresidualobtained

from

regressingastock’s

dailyexcess

return

on

acountry-specificFamaandFrench

(1993)three-factormodel

over

the

previoussixmonths.Idiosyncraticskew

nessisthethirdmomen

tofthe

residualobtained

by

regressingthedaily

stock

excess

return

on

the

excess

(country)market

return

andthesquaredexcess

market

return

over

theprevioussixmonths.

[Continued

overleaf]

8

Anomaly

Description

Refere

nces

Portfolios

Typ.

Hold-

ing

Data

base

sComputa

tionaldeta

ils

Short-

term

reversal

Firms

with

extrem

ere-

turns

inthe

previous

month

ex-

hibit

return

reversal.

Jegadeesh

(1990),

Leh

mann

(1990)

5past

returns

(1-5)

onemonth

CRSP,Datas-

tream

Irankstocksbasedontheirraw

return

inthepreviousmonth.

Long-term

reversal

Firms

with

extrem

ere-

turns

inthe

previousthree

tofive

years

exhibit

return

reversal.

DeB

ondt

and

Thaler

(1985),

DeB

ondt

and

Thaler

(1987)

5past

returns

(1-5)

sixmonths

CRSP,Datas-

tream

Irankstocksbasedontheircu

mulativereturn

over

monthst-60to

t-13.

Stocksare

sorted

into

portfoliosatthebeginningofeach

month

and

heldin

theseportfoliosforsixmonths.

Thelong-term

reversalreturn

inagiven

month

istheequallyweighted

averageoftheov

erlapping

portfolioreturnsin

thatmonth.

Turnover

anomaly

Firms

with

high

past

turnover

un-

derperform

stocks

with

low

past

turnover.

Datar,

Naik,

and

Radcliffe

(1998),

Lee

and

Swami-

nathan(2000)

5turnov

er(1-

5)

onemonth

CRSP,Datas-

tream

Irely

ontheaveragemonthly

turnov

erov

ertheprevioustw

elvemonths,

defi

ned

asthenumber

ofsharestraded

divided

bythenumber

ofshares

outstanding.FortheU.S.stock

market,Ionly

consider

stockstradingat

NYSEorAmex,asturnov

erforNasdaqstocksisconceptuallydifferen

t

(e.g.,AtkinsandDyl(1997)).

Seasonality

anomaly

Stocks

tend

tohav

ehigh

(low

)returns

every

year

inthe

same

calendar

month.

Heston

and

Sadka

(2008),

Heston

and

Sadka

(2010)

5past

returns

(5-1)

onemonth

CRSP,Datas-

tream

Form

ationperiodreturnsare

computedastheav

eragereturn

inmonths

t-12,t-24,t-36,t-48andt-60.Irequireatleast

threeva

lidreturn

esti-

mates.

[Continued

overleaf]

9

Anomaly

Description

Refere

nces

Portfolios

Typ.

Hold-

ing

Data

base

sComputa

tionaldeta

ils

Inter-

med

iate

momen

-

tum

Interm

ediate

returns

(i.e.,

inmonths

t-12

tot-7)

positively

predictfuture

returns.

Nov

y-M

arx

(2012)

5past

returns

(5-1)

sixmonths

CRSP,Datas-

tream

Form

ation

period

returnsare

computed

asthecu

mulativereturn

in

monthst-12to

t-7.Stocksare

sorted

into

portfoliosatthebeginningof

each

month

andheldin

theseportfoliosforsixmonths.

Theinterm

edi-

ate

momen

tum

return

inagiven

month

istheequallyweightedaverage

oftheov

erlappingportfolioreturnsin

thatmonth.

Continuous

inform

a-

tion

arrival

anomaly

Momentum

is

concentrated

infirm

sfor

which

in-

form

ation

arrives

con-

tinuously

insm

all

amounts.

Da,

Gurun,

andWarachka

(2014)

5past

returns

(5-1)

con-

ditional

on

inform

ation

discreten

ess

sixmonths

CRSP,Datas-

tream

Iclosely

follow

theapproach

inDa,Gurun,andWarachka

(2014).Infor-

mationdiscreten

essisdefi

ned

assign(cumulativereturnsov

ertheprevi-

oustw

elvemonths)*([%neg-%

pos])/[%

neg+%pos]),

wherethefraction

ofday

sduringtheform

ationperiodwithpositiveornegativereturns

(inlocalcu

rren

cy)are

referred

toas%posor%neg,resp

ectively.In

each

month,Ifirstform

fiveequallysizedform

ationperiodreturn

portfolios

basedonthecu

mulativereturn

over

theprevioustw

elvemonths.W

ithin

each

portfolio,Ithen

compute

threeequallysizedportfoliosbasedon

inform

ationdiscreten

ess,whereportfolio1(3)containsfirm

swithcon-

tinuousinform

ationarrival(discreteinform

ationarrival).Ithen

only

keepfirm

sthatare

ininform

ationdiscreten

essportfolio1.Iuse

over-

lappingportfoliosasin

thecase

of(traditional)

momen

tum.

Earnings

announce-

men

t

premium

Stocks

out-

perform

in

months

when

they

are

ex-

pected

to

announce

earnings.

Barb

er,

George,

Leh

avy,

and

Truem

an

(2013),

Beaver

(1968),

Co-

hen

,Dey,

Lys,

andSun-

der

(2007),

Frazzini

and

Lamont

(2007),

Sav

or

and

Wilson

(2015)

2(expected

announcers

-

other

firm

s)

onemonth

CRSP,

Com-

pustat,

Datastream,

Worldscope

Firmsare

expectedto

hav

eanearningsannouncemen

tin

agiven

month

ifthey

announcedearningstw

elvemonthsago.ForU.S.firm

s,Irely

on

Compustatitem

RDQ

toquantify

theannouncemen

tdate,forinterna-

tionalfirm

sonDatastream

Mnem

onic

Codes

WC05901to

WC05904.

[Continued

overleaf]

10

Anomaly

Description

Refere

nces

Portfolios

Typ.

Hold-

ing

Data

base

sComputa

tionaldeta

ils

Dividen

d

month

anomaly

Stocks

out-

perform

in

months

when

they

are

ex-

pected

topay

adividen

d.

Hartzm

ark

and

Solomon

(2013)

2(expected

announcers

-

other

firm

s)

onemonth

CRSP,Datas-

tream,World-

scope

Firmsare

expectedto

hav

eadividen

dannouncemen

tin

agiven

month

ifthey

announced

adividen

dpay

menttw

elvemonthsago.ForU.S.

firm

s,Irely

onCRSP

item

sDISTCD

andDCLRDT,forinternational

firm

sonDatastream

Mnem

onic

Codes

WC05910to

WC05913.

Announce-

men

t-

return

based

PEAD

Stocks

with

positive

earn-

ings

surprises

(as

measured

by

announce-

mentreturns)

outperform

stocks

with

negativeearn-

ingssurprises.

Brandt,

Kishore,

Santa-C

lara,

and

Ven

kat-

achalam

(2008),

Chan,

Jegadeesh,

and

Lakon-

ishok(1996)

5earnings

surprises

(5-1)

threemonths

CRSP,

Com-

pustat,

Datastream,

Worldscope

Earningssurprisesare

basedonthecu

mulativereturn

betweent-2and

t+1,wheretden

otestheday

oftheearningsannouncemen

t.Portfolio

sortingsare

basedonthemost

recentquarterly

earningssurprise

which

Irequireto

hav

etaken

place

inthepreviousmonth

orearlier,

butnot

more

than

100

day

sago.ForU.S.firm

s,Irely

on

Compustatitem

RDQ

toquantify

theannouncemen

tdate,forinternationalfirm

son

Datastream

Mnem

onic

Codes

WC05901to

WC05904.

Analyst

consensus-

based

PEAD

Stocks

with

positive

earn-

ings

surprises

(as

measured

by

deviations

from

analyst

consensus)

outperform

stocks

with

negativeearn-

ingssurprises.

Doy

le,

Lund-

holm

,and

Soliman

(2006),

Hir-

shleifer,

Lim

,

and

Teoh

(2009)

5earnings

surprises

(5-1)

twelvemonths

CRSP,IB

ES,

Datastream

Irely

onearningsannouncemen

tdatesandanalyst

fiscalyear1earn-

ingsestimates,

both

ofwhichIobtain

from

theIB

ESSummary

Tape.

Earningssurprisesare

computedasthedifferen

cebetweenactualearn-

ingsandthemed

iananalyst

forecast,scaledbythestandard

deviation

oftheforecasts.Portfoliosortingsare

basedonthemost

recentearnings

surprise

whichIrequireto

hav

etaken

place

less

thanoneyearago.

[Continued

overleaf]

11

Anomaly

Description

Refere

nces

Portfolios

Typ.

Hold-

ing

Data

base

sComputa

tionaldeta

ils

R&D

in-

tensity

anomaly

Firms

with

high

(low

)

scaled

R&D

outperform

(underper-

form

).

Chan,Lakon-

ishok,

and

Sougiannis

(2001)

5R&D

inten-

sity

(5-1)

twelvemonths

CRSP,

Com-

pustat,

Datastream,

Worldscope

MotivatedbyChan,Lakonishok,andSougiannis

(2001),

researchand

development(X

RD,W

C01201)intensity

isdefi

ned

asresearch

and

development/salesin

yeart+

0.8*researchanddevelopmen

t/salesin

yeart-1+

0.6*researchanddevelopmen

t/salesin

yeart-3+

0.4*re-

searchanddevelopment/salesin

yeart-4+

0.2*researchanddevelop-

men

t/salesin

yeart-5.Imatchaccountingdata

forthefiscal-yearen

d

ofyeartwithstock

return

data

from

July

ofyeart+

1untilJuneof

yeart+

2.

R&D

growth

anomaly

High

R&D

intensity

firm

sthat

unexpect-

edly

increase

their

R&D

outperform

.

Eberhardt,

Maxwell,

and

Siddique

(2004)

2(even

tfirm

s

-non-event

firm

s)

twelvemonths

CRSP,

Com-

pustat,

Datastream,

Worldscope

MotivatedbyEberhardt,

Maxwell,andSiddique(2004),

ahighR&D

intensity

firm

isconsidered

tounexpectedly

increase

itsR&D

(XRD,

WC01201)ifthefollow

ingcriteria

are

met.First,atthebeginningof

theR&D

increase

year,

theratiosofR&D

toassetsandR&D

tosales

are

atleast

5%.Second,thefirm

needsto

increase

both

itsdollarR&D

anditsratioofR&D

toassetsbyatleast

5%

duringtheeventyear.

ImatchIaccountingdata

forthefiscal-yearen

dofyeartwithstock

return

data

from

July

ofyeart+

1untilJuneofyeart+

2.

200

day

mov

ing

average

anomaly

The

ratio

of

the

curren

t

price

and

the

mov

ing

200

day

average

price

posi-

tivelypredicts

returns.

Brock,Lakon-

ishok,

and

LeB

aron

(1992),

Lo,

Mamay

sky,

and

Wang

(2000),

Sulli-

van,Tim

mer-

mann,

and

White(1999)

5 price/mov

ing

average(5-1)

onemonth

CRSP,Datas-

tream

Iform

theratiooftheprice

attheen

dofmonth

t-1andtheav

erage

price

over

theprevious200day

s.Pricesare

adjusted

fordividen

dsand

stock

splits.

[Continued

overleaf]

12

Anomaly

Description

Refere

nces

Portfolios

Typ.

Hold-

ing

Data

base

sComputa

tionaldeta

ils

52

week

high

anomaly

Nearness

to

the

52

week

high

posi-

tivelypredicts

returns.

Brock,Lakon-

ishok,

and

LeB

aron

(1992),

Hud-

dart,

Lang,

and

Yetman

(2009),Liand

Yu

(2012),

Sullivan,

Tim

mer-

mann,

and

White(1999)

5nearnessto

52

week

high

(5-1)

onemonth

CRSP,Datas-

tream

Iform

theratioofthestock

price

attheen

dofthemonth

t-1andthe

maxim

um

dailyprice

over

theprevious52weeks(endingin

month

t-1).

Pricesare

adjusted

fordividen

dsandstock

splits.

Analyst

forecast

dispersion

anomaly

Stocks

with

low

dispersion

inanalysts’

earningsfore-

casts

outper-

form

stocks

with

high

dispersion.

Diether,

Malloy,

and

Scherbina

(2002),

John-

son(2004)

5forecast

dis-

persion(1-5)

onemonth

CRSP,IB

ES,

Datastream

Inspired

byDiether,Malloy,andScherbina(2002),analyst

forecast

dis-

persion

isdefi

ned

asthestandard

deviation

ofanalyst

fiscalyear1

earningsestimates,

scaledbytheabsolute

valueofthemeanestimate.

Imeasure

analyst

forecast

dispersionin

thepreviousmonth

andcondi-

tiononfirm

swithatleast

twoanalystsontheIB

ESsummary

tape.

13

Table 2: Three-factor alphas, Stambaugh, Yu, and Yuan (2015) mispricing, country-level results

The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios of aggregated mispricing (computed as in Stam-baugh, Yu, and Yuan (2015) and explained in detail in Section 2.2. of the paper)on local Fama and French (1993) three-factor models (as explained in Section 2.3.of the paper). All returns are computed in local currency. T-statistics (in parenthe-ses) are based on the heteroskedasticity-consistent standard errors of White (1980).Two-tailed statistical significance at the 10%, 5%, and 1% level is indicated by *, **,and ***, respectively.

Country Sample period Return weightingStart End Equally weighted returns Value weighted returns

Argentina May-00 Dec-13 0.927* (1.75) 0.554 (0.73)Australia Jan-94 Dec-13 1.314*** (5.19) 0.834** (2.43)Austria Jan-94 Dec-13 1.488*** (4.92) 1.208*** (3.13)Belgium Jan-94 Dec-13 1.397*** (5.48) 1.314*** (3.42)Brazil Mar-98 Dec-13 1.413*** (2.71) 1.150* (1.70)Canada Jan-94 Dec-13 1.039*** (3.56) 1.037** (2.39)Chile Jan-94 Dec-13 0.689*** (3.32) -0.049 (-0.16)China Jul-95 Dec-13 0.608** (2.56) 0.594* (1.90)Colombia Jul-07 Dec-13 0.221 (0.32) -1.269** (-2.11)Denmark Jan-94 Dec-13 1.505*** (6.46) 1.499*** (3.57)Egypt Jul-04 Dec-13 0.845 (1.32) 0.640 (1.11)Finland Jan-94 Dec-13 1.235*** (4.37) 1.403** (2.55)France Jan-94 Dec-13 1.622*** (7.49) 1.345*** (4.33)Germany Jan-94 Dec-13 1.563*** (6.81) 0.806** (2.52)Greece Jan-94 Dec-13 1.251*** (3.35) 1.296** (2.32)Hongkong Jan-94 Dec-13 0.415 (1.08) 0.824* (1.71)India Jul-94 Dec-13 0.925*** (3.52) 0.680* (1.71)Indonesia Jan-94 Dec-13 1.565*** (3.47) 2.226*** (4.42)Ireland Feb-96 Dec-13 0.738 (1.26) 1.642** (2.11)Israel Aug-98 Dec-13 1.053*** (2.88) 1.587*** (2.87)Italy Jan-94 Dec-13 1.139*** (4.54) 0.818** (2.20)Japan Jan-94 Dec-13 0.455*** (2.68) 0.543** (2.14)Jordan Jul-07 Dec-13 0.344 (0.77) 0.860* (1.72)Korea Jan-94 Dec-13 1.289*** (3.97) 1.266*** (3.39)Malaysia Jan-94 Dec-13 1.235*** (6.13) 0.998*** (3.93)Mexico Jan-94 Dec-13 0.901*** (3.38) 0.280 (0.77)Morocco Jul-06 Dec-13 0.425 (1.03) -0.197 (-0.44)Netherlands Jan-94 Dec-13 1.528*** (6.22) 0.216 (0.55)New Zealand Jul-97 Dec-13 1.527*** (4.06) 0.931** (2.25)Norway Jan-94 Dec-13 1.859*** (5.70) 1.533*** (3.32)Pakistan Jul-94 Dec-13 1.101*** (2.93) 1.169** (2.39)Philippines Jul-94 Dec-13 0.233 (0.49) 0.004 (0.01)Poland Aug-98 Dec-13 1.043** (2.50) 0.253 (0.46)Portugal Jan-94 Dec-13 2.187*** (5.44) 1.762*** (3.55)Russia Jul-05 Dec-13 0.017 (0.02) 0.913 (1.24)Singapore Jan-94 Dec-13 0.442** (2.02) 0.142 (0.38)South Africa Jan-94 Dec-13 1.149*** (4.06) 0.494 (1.34)Spain Jan-94 Dec-13 0.726*** (3.16) 0.615** (2.09)Sri Lanka Oct-05 Dec-13 0.556 (1.22) 1.039* (1.68)Sweden Jan-94 Dec-13 1.957*** (6.22) 1.475*** (3.64)Switzerland Jan-94 Dec-13 1.543*** (8.11) 1.118*** (3.30)Taiwan Jul-94 Dec-13 0.717*** (2.95) 0.369 (1.14)Thailand Jan-94 Dec-13 1.610*** (5.75) 1.570*** (4.09)Turkey May-94 Dec-13 -0.013 (-0.03) -1.052 (-1.62)UK Jan-94 Dec-13 1.846*** (10.89) 0.866*** (2.89)USA Jan-94 Dec-13 1.362*** (6.66) 1.179*** (6.00)

14

Table 3: Three-factor alphas, failure probability, country-level results

The table reports monthly alphas (in %) obtained from regressing the quintile-based country-level long/short portfolios sorted on failure probability (as quanti-fied by the Campbell, Hilscher, and Szilagyi (2008) measure) on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). All re-turns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.

Country Sample period Return weightingStart End Equally weighted returns Value weighted returns

Argentina May-00 Dec-13 0.781 (1.53) 1.205* (1.68)Australia Jan-94 Dec-13 0.647*** (2.60) 0.045 (0.11)Austria Jan-94 Dec-13 1.164*** (3.57) 1.212** (2.59)Belgium Jan-94 Dec-13 1.422*** (5.66) 1.344*** (3.56)Brazil Mar-98 Dec-13 0.603 (0.78) -1.063 (-0.81)Canada Jan-94 Dec-13 0.388 (1.30) 0.345 (0.80)Chile Jan-94 Dec-13 1.059*** (4.17) 0.703** (2.12)China May-96 Dec-13 0.069 (0.21) 0.217 (0.64)Colombia Jul-10 Dec-13 1.281* (1.72) -0.420 (-0.50)Denmark Jan-94 Dec-13 0.719** (2.30) 0.080 (0.18)Egypt May-05 Dec-13 1.279* (1.91) 0.930 (1.10)Finland Jan-94 Dec-13 0.950*** (2.98) 1.145** (2.00)France Jan-94 Dec-13 1.117*** (4.89) 0.899* (1.92)Germany Jan-94 Dec-13 1.155*** (5.29) 1.302*** (3.51)Greece Jan-94 Dec-13 0.735* (1.73) 0.876* (1.82)Hongkong Jan-94 Dec-13 0.195 (0.48) 0.714 (1.31)India May-94 Dec-13 0.549** (2.01) 0.640 (1.27)Indonesia Jan-94 Dec-13 0.921** (2.00) 1.580** (2.27)Ireland May-98 Dec-13 0.162 (0.22) 0.652 (0.64)Israel May-99 Dec-13 0.748* (1.96) 0.527 (0.81)Italy Jan-94 Dec-13 0.955*** (3.47) 1.184*** (2.68)Japan Jan-94 Dec-13 0.400** (2.12) 0.679** (2.26)Jordan May-07 Dec-13 0.080 (0.16) -1.102 (-1.41)Korea Jan-94 Dec-13 1.009*** (2.63) 1.868*** (3.18)Malaysia Jan-94 Dec-13 1.208*** (4.96) 1.214*** (4.20)Mexico Jan-94 Dec-13 0.926*** (2.84) 0.553 (1.28)Morocco May-07 Dec-13 1.210*** (2.80) 1.176** (2.20)Netherlands Jan-94 Dec-13 1.406*** (4.20) 0.686 (1.25)New Zealand May-97 Dec-13 1.379*** (3.71) 1.121** (2.20)Norway Jan-94 Dec-13 1.185*** (3.51) 1.023* (1.91)Pakistan May-94 Dec-13 0.434 (0.98) 0.972* (1.67)Philippines Jul-94 Dec-13 0.771 (1.43) 0.763 (1.31)Poland May-99 Dec-13 0.937** (2.23) 0.848 (1.54)Portugal Jan-94 Dec-13 1.062** (2.31) 0.965* (1.83)Russia May-05 Dec-13 -0.527 (-0.51) -0.405 (-0.44)Singapore Jan-94 Dec-13 0.363 (1.15) 1.032*** (3.01)South Africa Jan-94 Dec-13 0.343 (1.08) 0.700* (1.65)Spain Jan-94 Dec-13 0.847*** (2.66) 1.020*** (2.71)Sri Lanka May-07 Dec-13 0.766 (1.37) 2.440*** (3.69)Sweden Jan-94 Dec-13 1.516*** (4.76) 1.144** (2.08)Switzerland Jan-94 Dec-13 1.213*** (5.78) 1.077** (2.52)Taiwan May-94 Dec-13 0.755*** (2.73) 1.322*** (4.09)Thailand Jan-94 Dec-13 0.817** (2.37) 1.288*** (2.65)Turkey May-94 Dec-13 -0.598 (-1.22) -1.590** (-2.10)UK Jan-94 Dec-13 1.047*** (5.62) 0.288 (0.87)USA Jan-94 Dec-13 0.645** (2.50) 1.186*** (3.91)

15

Table 4: Three-factor alphas, Ohlson (1980) score, country-level results

The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios sorted on financial distress (as quantified by theOhlson (1980) score) on local Fama and French (1993) three-factor models (as ex-plained in Section 2.3. of the paper). All returns are computed in local currency.T-statistics (in parentheses) are based on the heteroskedasticity-consistent standarderrors of White (1980). Two-tailed statistical significance at the 10%, 5%, and 1%level is indicated by *, **, and ***, respectively.

Country Sample period Return weightingStart End Equally weighted returns Value weighted returns

Argentina Jul-00 Dec-13 0.907* (1.68) 1.746** (2.26)Australia Jan-94 Dec-13 -0.183 (-0.83) -0.204 (-0.75)Austria Jan-94 Dec-13 0.568* (1.68) 0.824** (1.99)Belgium Jan-94 Dec-13 0.995*** (3.34) 0.783* (1.86)Brazil Mar-98 Dec-13 0.389 (0.50) 0.558 (0.67)Canada Jan-94 Dec-13 -0.099 (-0.31) 0.432 (1.07)Chile Jan-94 Dec-13 0.235 (1.09) 0.208 (0.69)China Jul-95 Dec-13 0.127 (0.41) 0.384 (1.33)Colombia Oct-09 Dec-13 -0.096 (-0.14) -1.571 (-1.50)Denmark Jan-94 Dec-13 0.193 (0.66) 0.062 (0.14)Egypt Jul-05 Dec-13 0.117 (0.21) 0.181 (0.19)Finland Jan-94 Dec-13 0.317 (1.15) 0.308 (0.78)France Jan-94 Dec-13 0.746*** (4.48) 0.414 (1.44)Germany Jan-94 Dec-13 0.271 (1.57) -0.274 (-0.76)Greece Jan-94 Dec-13 0.764** (2.19) 0.566 (1.24)Hongkong Jan-94 Dec-13 -0.463 (-1.09) 0.017 (0.03)India Jul-94 Dec-13 -0.093 (-0.41) 0.216 (0.57)Indonesia Jan-94 Dec-13 0.270 (0.70) 0.448 (0.79)Ireland Jul-98 Dec-13 0.497 (0.70) 0.385 (0.43)Israel Jul-99 Dec-13 0.817** (2.03) 0.614 (0.90)Italy Jan-94 Dec-13 1.180*** (4.61) 0.993*** (2.95)Japan Jan-94 Dec-13 0.418*** (3.35) 0.605*** (2.94)Jordan Jul-07 Dec-13 0.480 (1.16) -0.169 (-0.20)Korea Jan-94 Dec-13 0.720** (2.56) 1.528*** (2.95)Malaysia Jan-94 Dec-13 0.673*** (2.91) 0.774*** (2.68)Mexico Jan-94 Dec-13 0.945*** (3.05) 1.102*** (2.72)Morocco Jul-07 Dec-13 1.103*** (2.94) 0.236 (0.37)Netherlands Jan-94 Dec-13 0.629** (2.52) 0.142 (0.34)New Zealand Jul-97 Dec-13 0.346 (0.93) -0.103 (-0.19)Norway Jan-94 Dec-13 0.733** (2.29) 0.994* (1.94)Pakistan Jul-94 Dec-13 0.508 (1.34) 0.970** (2.04)Philippines Jul-94 Dec-13 1.031* (1.82) 0.177 (0.29)Poland Jul-98 Dec-13 0.316 (0.73) 0.037 (0.06)Portugal Jan-94 Dec-13 0.623 (1.31) 0.605 (1.34)Russia Jul-05 Dec-13 -0.816 (-0.96) 1.236* (1.75)Singapore Jan-94 Dec-13 0.504 (1.64) 0.669** (2.13)South Africa Jan-94 Dec-13 -0.359 (-0.90) -0.045 (-0.10)Spain Jan-94 Dec-13 0.272 (1.03) 0.502 (1.47)Sri Lanka Jul-07 Dec-13 0.626 (0.89) -0.270 (-0.32)Sweden Jan-94 Dec-13 0.815*** (2.90) 0.995*** (2.87)Switzerland Jan-94 Dec-13 0.760*** (3.68) 1.367*** (3.70)Taiwan Jul-94 Dec-13 0.352 (1.35) 0.955*** (3.63)Thailand Jan-94 Dec-13 0.141 (0.44) 0.452 (0.84)Turkey Jul-94 Dec-13 -0.205 (-0.42) -0.939 (-1.25)UK Jan-94 Dec-13 0.641*** (3.45) 0.375 (1.45)USA Jan-94 Dec-13 0.508** (2.15) 0.546** (2.15)

16

Table 5: Three-factor alphas, net stock issues, country-level results

The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios sorted on net stock issues on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). Allreturns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.

Country Sample period Return weightingStart End Equally weighted returns Value weighted returns

Argentina May-94 Dec-13 0.250 (0.69) -0.474 (-0.95)Australia Jan-94 Dec-13 0.817*** (3.73) 0.688** (2.38)Austria Jan-94 Dec-13 0.755*** (2.85) 0.291 (0.83)Belgium Jan-94 Dec-13 0.672*** (3.20) 0.136 (0.37)Brazil Sep-95 Dec-13 0.701 (1.28) 1.343 (1.53)Canada Jan-94 Dec-13 0.850*** (3.48) 0.549* (1.79)Chile Jan-94 Dec-13 0.514*** (2.97) -0.135 (-0.45)China Jan-94 Dec-13 -0.141 (-1.01) -0.196 (-0.90)Colombia Mar-94 Dec-13 0.605 (1.56) 0.295 (0.61)Denmark Jan-94 Dec-13 0.763*** (3.73) 0.953** (2.42)Egypt Jul-99 Dec-13 0.318 (0.68) -0.499 (-0.81)Finland Jan-94 Dec-13 0.745*** (3.16) -0.194 (-0.38)France Jan-94 Dec-13 0.829*** (5.20) 0.608*** (2.88)Germany Jan-94 Dec-13 0.710*** (5.19) 0.718*** (2.73)Greece Jan-94 Dec-13 0.800*** (3.09) 0.351 (1.01)Hongkong Jan-94 Dec-13 1.386*** (4.12) 0.444 (1.11)India Jan-94 Dec-13 0.509*** (3.84) 0.179 (0.56)Indonesia Jan-94 Dec-13 0.428 (1.41) 0.500 (1.32)Ireland Jan-94 Dec-13 0.102 (0.23) 0.614 (0.95)Israel Jul-94 Dec-13 0.656*** (3.24) 0.616 (1.45)Italy Jan-94 Dec-13 0.584*** (3.54) -0.020 (-0.08)Japan Jan-94 Dec-13 0.160* (1.74) -0.343** (-2.44)Jordan Dec-06 Dec-13 -0.453 (-1.27) -0.483 (-0.84)Korea Jan-94 Dec-13 2.177*** (7.79) 1.531*** (4.45)Malaysia Jan-94 Dec-13 0.563*** (3.01) 0.512*** (2.80)Mexico Jan-94 Dec-13 -0.010 (-0.04) 0.274 (0.91)Morocco Oct-00 Dec-13 -0.556* (-1.75) -0.548* (-1.88)Netherlands Jan-94 Dec-13 1.038*** (4.16) 0.346 (0.95)New Zealand Jan-94 Dec-13 0.648** (2.08) 0.235 (0.66)Norway Jan-94 Dec-13 1.173*** (4.39) 0.772** (1.99)Pakistan Jan-94 Dec-13 0.281 (1.21) 0.593* (1.67)Philippines Jan-94 Dec-13 0.252 (0.78) 0.201 (0.53)Poland Jan-97 Dec-13 0.046 (0.14) -0.171 (-0.44)Portugal Jan-94 Dec-13 0.925*** (2.64) 0.420 (1.03)Russia Dec-02 Dec-13 -0.388 (-0.80) -0.261 (-0.39)Singapore Jan-94 Dec-13 0.546** (2.54) -0.201 (-0.64)South Africa Jan-94 Dec-13 0.937*** (3.82) -0.092 (-0.27)Spain Jan-94 Dec-13 0.718*** (3.84) 0.822*** (2.73)Sri Lanka Jun-95 Dec-13 0.622* (1.82) 0.372 (0.77)Sweden Jan-94 Dec-13 1.211*** (5.23) 0.001 (0.00)Switzerland Jan-94 Dec-13 0.668*** (4.06) 0.179 (0.59)Taiwan Jan-94 Dec-13 0.393** (2.01) 0.127 (0.49)Thailand Jan-94 Dec-13 0.836*** (3.96) 0.867*** (3.00)Turkey Jan-94 Dec-13 0.517 (1.50) -0.633 (-0.93)UK Jan-94 Dec-13 1.180*** (8.26) 0.450* (1.77)USA Jan-94 Dec-13 1.195*** (7.44) 0.670*** (3.97)

17

Table 6: Three-factor alphas, composite equity issues, country-level results

The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios sorted on composite equity issues on local Famaand French (1993) three-factor models (as explained in Section 2.3. of the paper). Allreturns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.

Country Sample period Return weightingStart End Equally weighted returns Value weighted returns

Argentina Sep-98 Dec-13 0.313 (0.79) -0.022 (-0.03)Australia Jan-94 Dec-13 1.254*** (5.12) 0.410 (1.23)Austria Jan-94 Dec-13 1.034*** (3.46) 0.925*** (2.72)Belgium Jan-94 Dec-13 1.026*** (4.85) 0.145 (0.42)Brazil Sep-99 Dec-13 1.273 (1.34) -0.124 (-0.14)Canada Jan-94 Dec-13 1.401*** (4.33) 0.682* (1.91)Chile Sep-94 Dec-13 1.015*** (4.66) 0.099 (0.22)China Oct-97 Dec-13 0.352** (1.98) 0.246 (1.03)Colombia Apr-99 Dec-13 0.103 (0.17) 0.176 (0.29)Denmark Jan-94 Dec-13 0.869*** (3.95) 0.948** (2.40)Egypt Nov-01 Dec-13 1.351** (2.56) 1.541** (2.15)Finland Oct-94 Dec-13 1.154*** (4.57) 0.792 (1.52)France Jan-94 Dec-13 0.787*** (4.62) 0.457* (1.95)Germany Jan-94 Dec-13 0.754*** (4.93) 0.589* (1.92)Greece Jan-94 Dec-13 1.453*** (4.18) 1.870*** (3.47)Hongkong Jan-94 Dec-13 0.848** (2.13) 0.966 (1.61)India Mar-95 Dec-13 1.092*** (5.94) 0.679** (1.97)Indonesia Jun-95 Dec-13 1.393*** (2.93) 0.370 (0.63)Ireland Jan-94 Dec-13 0.047 (0.07) 0.091 (0.11)Israel Jul-94 Dec-13 0.743*** (3.25) 0.486 (1.18)Italy Jan-94 Dec-13 1.086*** (5.65) 0.738*** (2.72)Japan Jan-94 Dec-13 0.290** (2.40) 0.256 (1.33)Jordan Dec-10 Dec-13 1.025* (1.74) 0.871 (1.01)Korea Jan-94 Dec-13 1.796*** (6.60) 1.016*** (2.62)Malaysia Jan-94 Dec-13 1.232*** (6.46) 1.039*** (4.30)Mexico Jan-94 Dec-13 0.732** (2.26) 0.282 (0.77)Morocco Oct-00 Dec-13 -0.032 (-0.09) -0.382 (-0.99)Netherlands Jan-94 Dec-13 1.191*** (4.79) 0.457 (1.25)New Zealand Jan-94 Dec-13 0.683** (2.06) 0.236 (0.57)Norway Jan-94 Dec-13 0.991*** (3.15) 0.601 (1.42)Pakistan Sep-97 Dec-13 1.339*** (4.07) 1.821*** (3.82)Philippines Feb-95 Dec-13 1.454*** (2.75) 0.589 (1.13)Poland May-02 Dec-13 1.116*** (2.70) 1.143** (2.41)Portugal Jan-94 Dec-13 2.035*** (4.46) 1.164** (2.57)Russia Jul-04 Dec-13 0.314 (0.50) -2.017** (-2.07)Singapore Jan-94 Dec-13 0.951*** (3.80) 0.002 (0.00)South Africa Jan-94 Dec-13 0.964*** (4.20) 0.300 (0.98)Spain Jan-94 Dec-13 1.003*** (4.70) 0.677** (2.35)Sri Lanka Jun-95 Dec-13 0.893** (2.49) 1.326*** (2.99)Sweden Jan-94 Dec-13 0.771** (2.59) 0.688 (1.54)Switzerland Jan-94 Dec-13 0.699*** (4.44) 0.182 (0.60)Taiwan Jan-94 Dec-13 0.564** (2.57) -0.036 (-0.11)Thailand Jan-94 Dec-13 0.908*** (3.19) 1.081*** (2.84)Turkey Jan-94 Dec-13 1.111*** (3.07) 0.285 (0.42)UK Jan-94 Dec-13 1.083*** (7.00) 0.512 (1.57)USA Jan-94 Dec-13 0.742*** (4.53) 0.559*** (3.60)

18

Table 7: Three-factor alphas, accruals, country-level results

The table reports monthly alphas (in %) obtained from regressing the quintile-based country-level long/short portfolios sorted on accruals on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). Allreturns are computed in local currency. T-statistics (in parentheses) are based onthe heteroskedasticity-consistent standard errors of White (1980). Two-tailed sta-tistical significance at the 10%, 5%, and 1% level is indicated by *, **, and ***,respectively.

Country Sample period Return weightingStart End Equally weighted returns Value weighted returns

Argentina Jul-01 Dec-13 0.036 (0.07) -1.036 (-1.19)Australia Jan-94 Dec-13 0.360 (1.64) 0.617* (1.86)Austria Jan-94 Dec-13 0.190 (0.55) 0.376 (0.78)Belgium Jan-94 Dec-13 0.374 (1.41) 0.358 (0.99)Brazil Mar-98 Dec-13 0.673 (1.29) 1.736* (1.85)Canada Jan-94 Dec-13 0.288 (0.86) 0.518 (1.45)Chile Jan-94 Dec-13 -0.112 (-0.45) -0.101 (-0.32)China Jul-95 Dec-13 0.359* (1.69) 0.427 (1.54)Colombia Nov-09 Dec-13 -1.769* (-1.79) -4.573 (-1.21)Denmark Jan-94 Dec-13 0.623** (2.50) 1.988*** (3.69)Egypt Jul-05 Dec-13 -0.866 (-1.09) -1.345 (-1.29)Finland Jan-94 Dec-13 0.585** (2.45) 1.153* (1.92)France Jan-94 Dec-13 0.542*** (3.71) 0.391 (1.06)Germany Jan-94 Dec-13 0.339** (2.16) 0.553 (1.57)Greece Jan-94 Dec-13 0.233 (0.69) -0.092 (-0.21)Hongkong Jan-94 Dec-13 0.069 (0.17) 0.961* (1.82)India Jul-94 Dec-13 0.669*** (3.22) 0.888** (2.35)Indonesia Jan-94 Dec-13 0.628* (1.76) 0.499 (0.86)Ireland Jul-98 Dec-13 0.288 (0.46) 1.358* (1.84)Israel Jul-99 Dec-13 0.487 (1.30) 0.125 (0.21)Italy Jan-94 Dec-13 0.564*** (2.71) 0.551 (1.64)Japan Jan-94 Dec-13 0.074 (0.81) 0.081 (0.46)Jordan Jul-07 Dec-13 -0.110 (-0.21) 0.813 (1.30)Korea Jan-94 Dec-13 0.355 (1.59) 1.246** (2.42)Malaysia Jan-94 Dec-13 0.118 (0.65) 0.280 (0.92)Mexico Jan-94 Dec-13 0.339 (1.07) 0.356 (0.83)Morocco Jul-07 Dec-13 0.810* (1.67) -0.180 (-0.27)Netherlands Jan-94 Dec-13 -0.136 (-0.57) -0.843 (-1.52)New Zealand Jul-97 Dec-13 0.792** (2.10) 0.601 (1.33)Norway Jan-94 Dec-13 0.301 (0.98) 0.522 (1.02)Pakistan Jul-94 Dec-13 -0.025 (-0.07) 0.033 (0.07)Philippines Jul-95 Dec-13 0.143 (0.29) -0.401 (-0.81)Poland Jul-03 Dec-13 0.265 (0.72) 0.184 (0.37)Portugal Jan-94 Dec-13 0.812** (2.08) 1.330*** (3.05)Russia Feb-06 Dec-13 0.890 (1.11) 0.145 (0.20)Singapore Jan-94 Dec-13 0.111 (0.57) 0.416 (1.04)South Africa Jan-94 Dec-13 0.700** (2.47) 0.648* (1.67)Spain Jan-94 Dec-13 -0.013 (-0.05) 0.176 (0.49)Sri Lanka Jul-07 Dec-13 0.495 (0.79) -1.243** (-1.99)Sweden Jan-94 Dec-13 0.291 (1.20) 0.666 (1.48)Switzerland Jan-94 Dec-13 0.376** (2.04) 0.967** (2.55)Taiwan Jul-94 Dec-13 0.169 (0.75) 0.055 (0.16)Thailand Jan-94 Dec-13 1.131*** (3.85) 1.144** (2.57)Turkey Nov-96 Dec-13 -0.372 (-0.98) -0.394 (-0.67)UK Jan-94 Dec-13 0.379*** (3.05) 0.633* (1.71)USA Jan-94 Dec-13 0.263** (2.51) 0.168 (0.85)

19

Table 8: Three-factor alphas, net operating assets, country-level results

The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios sorted on net operating assets on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). Allreturns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.

Country Sample period Return weightingStart End Equally weighted returns Value weighted returns

Argentina Jul-00 Dec-13 0.218 (0.39) 0.057 (0.08)Australia Jan-94 Dec-13 0.736*** (3.42) 0.475 (1.38)Austria Jan-94 Dec-13 0.723* (1.95) 0.778* (1.87)Belgium Jan-94 Dec-13 0.263 (1.15) 0.226 (0.78)Brazil Mar-98 Dec-13 0.247 (0.34) 0.312 (0.53)Canada Jan-94 Dec-13 0.212 (0.68) 0.490 (1.45)Chile Jan-94 Dec-13 -0.357 (-1.55) -0.636* (-1.83)China Jul-95 Dec-13 0.480** (2.19) 0.597** (2.24)Colombia Jul-07 Dec-13 -1.463* (-1.96) -1.298* (-1.81)Denmark Jan-94 Dec-13 0.669*** (2.72) 1.072** (2.25)Egypt Jul-05 Dec-13 0.319 (0.51) 0.176 (0.26)Finland Jan-94 Dec-13 0.097 (0.45) 0.523 (0.95)France Jan-94 Dec-13 0.626*** (4.61) 0.030 (0.11)Germany Jan-94 Dec-13 0.706*** (4.19) 0.281 (0.84)Greece Jan-94 Dec-13 0.658** (2.11) 0.430 (1.09)Hongkong Jan-94 Dec-13 0.347 (0.98) 1.056** (2.07)India Jul-94 Dec-13 0.875*** (3.94) 0.764** (2.26)Indonesia Jan-94 Dec-13 1.431*** (3.89) 1.458*** (2.91)Ireland Jul-98 Dec-13 0.585 (0.81) 1.392* (1.77)Israel Jul-98 Dec-13 0.532 (1.59) 0.164 (0.27)Italy Jan-94 Dec-13 0.218 (0.99) -0.101 (-0.32)Japan Jan-94 Dec-13 0.178* (1.92) 0.003 (0.02)Jordan Jul-07 Dec-13 0.348 (0.74) 0.326 (0.49)Korea Jan-94 Dec-13 0.350 (1.50) 0.293 (0.71)Malaysia Jan-94 Dec-13 0.419** (2.04) 0.761*** (2.96)Mexico Jan-94 Dec-13 0.309 (1.09) 0.713* (1.70)Morocco Jul-07 Dec-13 0.640 (1.49) 1.434** (2.11)Netherlands Jan-94 Dec-13 0.237 (0.91) 0.472 (1.18)New Zealand Jul-97 Dec-13 0.137 (0.37) -0.267 (-0.63)Norway Jan-94 Dec-13 1.460*** (4.08) 0.953** (2.10)Pakistan Jul-94 Dec-13 0.977** (2.28) 1.350*** (2.97)Philippines Jul-94 Dec-13 -0.269 (-0.53) 0.426 (0.71)Poland Aug-98 Dec-13 0.059 (0.11) 0.259 (0.33)Portugal Jan-94 Dec-13 1.011** (2.16) -0.296 (-0.63)Russia Jul-05 Dec-13 0.430 (0.54) 1.212 (1.49)Singapore Jan-94 Dec-13 0.087 (0.44) -0.162 (-0.47)South Africa Jan-94 Dec-13 0.811*** (2.94) 0.295 (0.76)Spain Jan-94 Dec-13 0.590*** (2.87) -0.128 (-0.37)Sri Lanka Jul-07 Dec-13 1.323** (2.16) 2.009** (2.39)Sweden Jan-94 Dec-13 0.720*** (2.83) 0.258 (0.51)Switzerland Jan-94 Dec-13 0.510*** (2.85) 0.110 (0.28)Taiwan Jul-94 Dec-13 0.170 (0.73) -0.243 (-0.80)Thailand Jan-94 Dec-13 0.817*** (2.89) 0.568 (1.45)Turkey Jul-94 Dec-13 0.514 (1.21) 0.215 (0.33)UK Jan-94 Dec-13 0.669*** (4.00) 0.406 (1.39)USA Jan-94 Dec-13 0.802*** (4.68) 0.454** (2.53)

20

Table 9: Three-factor alphas, momentum, country-level results

The table reports monthly alphas (in %) obtained from regressing the quintile-based country-level long/short portfolios sorted on momentum on local Fama andFrench (1993) three-factor models (as explained in Section 2.3 of the paper). All re-turns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.

Country Sample period Return weightingStart End Equally weighted returns Value weighted returns

Argentina Jan-94 Dec-13 -0.030 (-0.07) -0.231 (-0.38)Australia Jan-94 Dec-13 1.880*** (7.37) 2.008*** (6.35)Austria Jan-94 Dec-13 1.169*** (4.12) 0.934*** (3.17)Belgium Jan-94 Dec-13 1.590*** (6.06) 1.477*** (4.06)Brazil Mar-95 Dec-13 0.496 (0.79) 0.833 (0.68)Canada Jan-94 Dec-13 1.079*** (3.21) 1.419*** (3.55)Chile Jan-94 Dec-13 1.155*** (6.37) 0.922*** (3.30)China Jan-94 Dec-13 0.381* (1.71) 0.610** (2.06)Colombia Jan-94 Dec-13 1.058*** (2.75) 1.077* (1.95)Denmark Jan-94 Dec-13 1.654*** (7.01) 1.434*** (3.85)Egypt Jul-99 Dec-13 0.415 (0.92) 0.882 (1.49)Finland Jan-94 Dec-13 1.272*** (4.14) 1.611*** (2.92)France Jan-94 Dec-13 1.513*** (5.71) 0.957*** (2.89)Germany Jan-94 Dec-13 1.445*** (5.16) 1.455*** (3.95)Greece Jan-94 Dec-13 0.972** (2.38) 1.517*** (2.81)Hongkong Jan-94 Dec-13 0.703* (1.77) 1.413*** (2.63)India Jan-94 Dec-13 1.333*** (3.94) 1.449*** (3.14)Indonesia Jan-94 Dec-13 0.378 (0.99) 0.991** (2.26)Ireland Jan-94 Dec-13 1.825*** (4.20) 1.625*** (2.89)Israel Jul-94 Dec-13 1.036*** (3.86) 1.679*** (3.86)Italy Jan-94 Dec-13 1.239*** (4.47) 1.119*** (3.02)Japan Jan-94 Dec-13 0.465** (2.02) 1.010*** (3.25)Jordan Jul-06 Dec-13 0.579 (1.28) 1.335* (1.90)Korea Jan-94 Dec-13 0.821* (1.97) 1.229** (2.59)Malaysia Jan-94 Dec-13 0.685*** (2.70) 0.721** (2.57)Mexico Jan-94 Dec-13 1.020*** (3.35) 0.883*** (2.87)Morocco Oct-00 Dec-13 0.815*** (2.79) 0.735** (2.21)Netherlands Jan-94 Dec-13 1.839*** (5.87) 0.594 (1.33)New Zealand Jan-94 Dec-13 1.784*** (5.96) 1.115*** (3.34)Norway Jan-94 Dec-13 1.488*** (4.86) 1.322*** (3.10)Pakistan Jan-94 Dec-13 0.754* (1.84) 0.780* (1.68)Philippines Jan-94 Dec-13 0.323 (0.77) 0.537 (1.27)Poland Jul-96 Dec-13 1.071*** (2.95) 0.702 (1.53)Portugal Jan-94 Dec-13 1.536*** (3.67) 1.256*** (2.71)Russia Dec-02 Dec-13 0.277 (0.61) 1.649** (2.49)Singapore Jan-94 Dec-13 0.631* (1.91) 0.507 (1.41)South Africa Jan-94 Dec-13 2.405*** (9.95) 2.097*** (5.59)Spain Jan-94 Dec-13 0.916*** (3.52) 1.074*** (3.01)Sri Lanka Jun-95 Dec-13 0.415 (1.24) 1.020*** (2.61)Sweden Jan-94 Dec-13 1.474*** (4.06) 0.756 (1.61)Switzerland Jan-94 Dec-13 1.611*** (5.96) 1.022** (2.39)Taiwan Jan-94 Dec-13 0.500* (1.87) 0.325 (0.89)Thailand Jan-94 Dec-13 1.124*** (3.06) 1.583*** (3.75)Turkey Jan-94 Dec-13 -0.988*** (-2.60) -0.784 (-1.53)UK Jan-94 Dec-13 1.622*** (7.62) 1.257*** (3.87)USA Jan-94 Dec-13 1.211*** (3.67) 1.268*** (3.93)

21

Table 10: Three-factor alphas, gross profitability, country-level results

The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios sorted on gross profitability on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). Allreturns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.

Country Sample period Return weightingStart End Equally weighted returns Value weighted returns

Argentina Jul-99 Dec-13 0.132 (0.27) 0.463 (0.76)Australia Jan-94 Dec-13 0.098 (0.34) 0.123 (0.38)Austria Jan-94 Dec-13 -0.164 (-0.52) 0.358 (0.91)Belgium Jan-94 Dec-13 -0.644** (-2.43) -0.369 (-0.84)Brazil Jul-97 Dec-13 0.767 (0.92) 1.932*** (2.75)Canada Jan-94 Dec-13 0.851** (2.37) 0.062 (0.11)Chile Jan-94 Dec-13 0.114 (0.55) -0.050 (-0.14)China Jul-94 Dec-13 0.242 (0.76) 0.077 (0.22)Colombia Jul-10 Dec-13 -1.079 (-0.96) -2.004 (-1.00)Denmark Jan-94 Dec-13 0.801*** (2.83) 1.287*** (2.64)Egypt Jul-04 Dec-13 1.119* (1.84) 1.586* (1.94)Finland Jan-94 Dec-13 0.403* (1.70) 0.223 (0.48)France Jan-94 Dec-13 0.668*** (4.49) 0.468** (2.14)Germany Jan-94 Dec-13 0.775*** (4.81) 0.795** (2.48)Greece Jan-94 Dec-13 0.931*** (3.15) 0.720* (1.90)Hongkong Jan-94 Dec-13 0.244 (0.61) -0.083 (-0.13)India Jan-94 Dec-13 0.272 (1.15) 0.780** (2.23)Indonesia Jan-94 Dec-13 1.209** (2.42) 1.352** (2.52)Ireland Jul-99 Dec-13 -0.894 (-1.07) 0.005 (0.01)Israel Feb-98 Dec-13 0.593 (1.61) 0.264 (0.57)Italy Jan-94 Dec-13 0.403* (1.79) 0.383 (0.98)Japan Jan-94 Dec-13 0.682*** (4.59) 0.660*** (3.23)JordanKorea Jan-94 Dec-13 1.391*** (4.79) 1.206** (2.29)Malaysia Jan-94 Dec-13 0.895*** (4.54) 0.810*** (2.86)Mexico Jan-94 Dec-13 0.331 (1.05) 0.092 (0.24)Morocco Jul-06 Dec-13 0.185 (0.53) 0.071 (0.16)Netherlands Jan-94 Dec-13 0.483** (2.11) 0.128 (0.34)New Zealand Jul-95 Dec-13 0.419 (1.12) -0.300 (-0.57)Norway Jan-94 Dec-13 1.132*** (3.31) 0.755 (1.63)Pakistan Jul-94 Dec-13 0.980** (2.24) 1.125** (2.28)Philippines Jan-94 Dec-13 0.938** (2.03) 1.374*** (2.77)Poland Jul-97 Dec-13 1.283*** (3.27) 1.369** (2.48)Portugal Jan-94 Dec-13 1.433*** (3.02) 2.075*** (3.96)Russia Jul-04 Dec-13 0.820 (0.86) 1.965* (1.98)Singapore Jan-94 Dec-13 0.523** (1.99) 0.712** (2.18)South Africa Jan-94 Dec-13 0.278 (0.99) 0.228 (0.59)Spain Jan-94 Dec-13 0.252 (1.06) 0.760* (1.95)Sri Lanka Oct-05 Dec-13 1.803** (2.45) 2.166*** (2.82)Sweden Jan-94 Dec-13 1.079*** (3.65) 1.061*** (2.92)Switzerland Jan-94 Dec-13 0.650*** (3.30) 0.662** (2.47)Taiwan Jan-94 Dec-13 0.691*** (2.80) 1.315*** (4.03)Thailand Jan-94 Dec-13 0.844*** (3.11) 0.531 (1.31)Turkey Jan-94 Dec-13 0.406 (0.88) 0.413 (0.52)UK Jan-94 Dec-13 0.957*** (4.86) 0.347 (1.02)USA Jan-94 Dec-13 0.616*** (3.71) 0.955*** (4.51)

22

Table 11: Three-factor alphas, asset growth, country-level results

The table reports monthly alphas (in %) obtained from regressing the quintile-based country-level long/short portfolios sorted on asset growth on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). All re-turns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.

Country Sample period Return weightingStart End Equally weighted returns Value weighted returns

Argentina Jul-00 Dec-13 0.024 (0.05) -0.309 (-0.42)Australia Jan-94 Dec-13 1.142*** (4.99) 0.955*** (2.83)Austria Jan-94 Dec-13 0.373 (1.14) 0.593 (1.57)Belgium Jan-94 Dec-13 0.123 (0.48) -0.116 (-0.33)Brazil Jul-97 Dec-13 0.549 (0.77) 1.211 (1.26)Canada Jan-94 Dec-13 0.338 (0.97) 0.632* (1.77)Chile Jan-94 Dec-13 -0.542** (-2.44) -0.290 (-0.98)China Jul-95 Dec-13 0.435 (1.34) 0.056 (0.14)Colombia Jul-04 Dec-13 0.158 (0.30) 1.304* (1.73)Denmark Jan-94 Dec-13 0.648*** (3.01) 0.856** (2.46)Egypt Jul-04 Dec-13 0.161 (0.30) -0.371 (-0.61)Finland Jan-94 Dec-13 0.207 (0.71) 0.556 (1.26)France Jan-94 Dec-13 0.326* (1.72) 0.581* (1.96)Germany Jan-94 Dec-13 0.311 (1.40) 0.588 (1.52)Greece Jan-94 Dec-13 0.066 (0.19) -1.108** (-2.13)Hongkong Jan-94 Dec-13 -0.020 (-0.05) -0.415 (-0.79)India Jul-94 Dec-13 0.770*** (3.19) 0.620 (1.55)Indonesia Jan-94 Dec-13 0.511 (1.24) 0.302 (0.58)Ireland Jan-94 Dec-13 0.067 (0.12) 0.122 (0.14)Israel Jul-96 Dec-13 0.315 (0.85) -0.489 (-0.75)Italy Jan-94 Dec-13 -0.166 (-0.76) -0.228 (-0.76)Japan Jan-94 Dec-13 -0.119 (-0.83) -0.227 (-1.10)Jordan Jul-06 Dec-13 0.020 (0.04) -0.732 (-0.86)Korea Jan-94 Dec-13 0.219 (0.86) -0.383 (-0.85)Malaysia Jan-94 Dec-13 -0.015 (-0.08) 0.372 (1.45)Mexico Jan-94 Dec-13 -0.283 (-1.05) 0.003 (0.01)Morocco Jul-05 Dec-13 0.028 (0.06) -0.174 (-0.29)Netherlands Jan-94 Dec-13 0.138 (0.50) 0.110 (0.28)New Zealand Jul-97 Dec-13 0.283 (0.74) -0.519 (-1.06)Norway Jan-94 Dec-13 0.308 (0.96) 0.534 (1.29)Pakistan Jul-94 Dec-13 0.347 (0.88) 0.481 (0.98)Philippines Jan-94 Dec-13 -0.331 (-0.80) -0.480 (-1.00)Poland Jul-97 Dec-13 0.117 (0.31) -0.482 (-1.05)Portugal Jan-94 Dec-13 -0.064 (-0.15) 0.118 (0.24)Russia Jul-05 Dec-13 -0.718 (-0.86) 0.978 (1.26)Singapore Jan-94 Dec-13 -0.250 (-1.13) 0.237 (0.57)South Africa Jan-94 Dec-13 0.952*** (3.64) 0.052 (0.15)Spain Jan-94 Dec-13 -0.165 (-0.62) -0.138 (-0.41)Sri Lanka Jul-05 Dec-13 -0.032 (-0.07) 0.402 (0.77)Sweden Jan-94 Dec-13 0.197 (0.74) 0.234 (0.60)Switzerland Jan-94 Dec-13 0.339* (1.76) -0.179 (-0.47)Taiwan Jul-94 Dec-13 -0.238 (-0.99) -0.871*** (-2.83)Thailand Jan-94 Dec-13 0.678** (2.58) 0.364 (0.94)Turkey Jan-94 Dec-13 -0.224 (-0.58) 0.484 (0.78)UK Jan-94 Dec-13 0.473*** (3.44) 0.200 (0.61)USA Jan-94 Dec-13 0.856*** (4.68) 0.288* (1.65)

23

Table 12: Three-factor alphas, return on assets, country-level results

The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios sorted on return on assets on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). Allreturns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.

Country Sample period Return weightingStart End Equally weighted returns Value weighted returns

Argentina Jul-00 Dec-13 0.413 (0.70) 1.138* (1.77)Australia Jan-94 Dec-13 0.281 (1.22) -0.083 (-0.26)Austria Jan-94 Dec-13 0.466 (1.52) 0.242 (0.60)Belgium Jan-94 Dec-13 0.960*** (3.59) 0.914** (2.16)Brazil Jul-97 Dec-13 1.039 (1.62) 1.165 (1.64)Canada Jan-94 Dec-13 0.219 (0.69) 0.650* (1.69)Chile Jan-94 Dec-13 0.522** (2.43) 0.120 (0.39)China Jul-95 Dec-13 -0.00 (-0.00) -0.086 (-0.22)Colombia Jul-04 Dec-13 0.581 (0.99) -0.064 (-0.08)Denmark Jan-94 Dec-13 0.314 (1.23) -0.057 (-0.14)Egypt Jul-04 Dec-13 0.061 (0.10) -0.175 (-0.26)Finland Jan-94 Dec-13 0.260 (0.90) 0.126 (0.27)France Jan-94 Dec-13 0.742*** (4.34) 0.537* (1.87)Germany Jan-94 Dec-13 0.860*** (4.39) 0.835** (2.34)Greece Jan-94 Dec-13 0.432 (1.14) 0.939* (1.79)Hongkong Jan-94 Dec-13 -0.003 (-0.01) 0.536 (0.93)India Jul-94 Dec-13 -0.230 (-1.01) 0.017 (0.05)Indonesia Jan-94 Dec-13 0.897** (2.16) 1.602*** (2.95)Ireland Jan-94 Dec-13 0.100 (0.16) 0.350 (0.51)Israel Jul-96 Dec-13 0.237 (0.58) 0.932 (1.34)Italy Jan-94 Dec-13 0.676*** (2.95) 0.540 (1.60)Japan Jan-94 Dec-13 0.361** (2.52) 0.681*** (2.60)Jordan Jul-06 Dec-13 0.285 (0.69) 0.719 (1.09)Korea Jan-94 Dec-13 0.520* (1.80) 1.058** (2.34)Malaysia Jan-94 Dec-13 0.829*** (3.99) 0.731*** (2.80)Mexico Jan-94 Dec-13 0.093 (0.30) -0.886** (-2.08)Morocco Jul-05 Dec-13 0.232 (0.58) -0.360 (-1.05)Netherlands Jan-94 Dec-13 0.698** (2.57) 0.035 (0.09)New Zealand Jul-97 Dec-13 0.606* (1.67) -0.168 (-0.30)Norway Jan-94 Dec-13 0.856*** (2.70) 1.078** (2.04)Pakistan Jul-94 Dec-13 0.478 (1.05) 0.975** (2.05)Philippines Jan-94 Dec-13 0.467 (0.89) 0.541 (1.02)Poland Jul-97 Dec-13 0.244 (0.56) -0.045 (-0.09)Portugal Jan-94 Dec-13 0.693* (1.70) 0.897* (1.95)Russia Jul-05 Dec-13 -0.430 (-0.46) -0.286 (-0.38)Singapore Jan-94 Dec-13 0.716** (2.56) 0.737* (1.92)South Africa Jan-94 Dec-13 -0.225 (-0.96) 0.074 (0.18)Spain Jan-94 Dec-13 0.472* (1.90) 0.640 (1.62)Sri Lanka Jul-05 Dec-13 0.276 (0.64) 1.105* (1.97)Sweden Jan-94 Dec-13 0.859*** (2.72) 1.078*** (2.83)Switzerland Jan-94 Dec-13 0.656*** (3.62) 1.075*** (3.21)Taiwan Jul-94 Dec-13 0.590** (1.98) 1.108*** (2.89)Thailand Jan-94 Dec-13 0.583** (2.15) 1.028*** (2.64)Turkey Jan-94 Dec-13 -0.377 (-0.90) -0.916 (-1.42)UK Jan-94 Dec-13 0.951*** (5.67) 0.638** (2.16)USA Jan-94 Dec-13 0.330 (1.42) 0.618*** (2.84)

24

Table 13: Three-factor alphas, investment-to-assets, country-level results

The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios sorted on investment-to-assets on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). Allreturns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.

Country Sample period Return weightingStart End Equally weighted returns Value weighted returns

Argentina Dec-03 Dec-13 0.588 (1.07) 0.252 (0.26)Australia Jan-94 Dec-13 0.538** (2.30) -0.092 (-0.27)Austria Jun-95 Dec-13 0.151 (0.37) 0.561 (1.14)Belgium Jan-94 Dec-13 0.307 (1.31) 0.356 (0.87)Brazil Jun-98 Dec-13 0.621 (0.73) 0.559 (0.77)Canada Jan-94 Dec-13 0.630** (2.10) 0.845** (2.07)Chile Jan-94 Dec-13 -0.357* (-1.68) -0.775** (-2.42)China Jul-95 Dec-13 0.442 (1.49) 0.469 (1.36)ColombiaDenmark Jan-94 Dec-13 0.835*** (3.15) 0.553 (1.20)Egypt Jul-05 Dec-13 -0.308 (-0.57) 0.238 (0.30)Finland Jul-96 Dec-13 0.259 (0.92) 0.783 (1.38)France Jan-94 Dec-13 0.409** (2.41) 0.065 (0.22)Germany Jan-94 Dec-13 0.493*** (2.60) 0.423 (1.40)Greece Jan-94 Dec-13 -0.117 (-0.35) 0.455 (1.15)Hongkong Jan-94 Dec-13 -0.388 (-1.16) -0.969* (-1.97)India Jul-94 Dec-13 0.569*** (2.60) -0.154 (-0.41)Indonesia Jan-94 Dec-13 0.492 (1.18) -0.153 (-0.34)Ireland Oct-99 Dec-13 -0.838 (-1.02) -1.592* (-1.81)Israel Jul-99 Dec-13 0.053 (0.16) -0.245 (-0.40)Italy Jan-94 Dec-13 -0.129 (-0.53) -0.084 (-0.22)Japan Jan-94 Dec-13 0.032 (0.26) 0.172 (0.84)Jordan Jul-07 Dec-13 0.463 (0.74) 0.498 (0.37)Korea Jan-94 Dec-13 0.311 (1.39) -0.220 (-0.47)Malaysia Jan-94 Dec-13 -0.067 (-0.38) -0.102 (-0.40)Mexico Jan-94 Dec-13 0.073 (0.25) 0.269 (0.80)Morocco Jul-07 Dec-13 0.116 (0.24) -0.196 (-0.31)Netherlands Jan-94 Dec-13 0.256 (1.11) -0.159 (-0.37)New Zealand Jul-97 Dec-13 0.223 (0.66) -0.396 (-0.90)Norway Jan-94 Dec-13 0.735** (2.36) 0.378 (0.91)Pakistan Jul-94 Dec-13 0.736* (1.74) -0.009 (-0.02)Philippines Jul-94 Dec-13 -0.616 (-1.34) -0.388 (-0.70)Poland Jul-03 Dec-13 1.364*** (3.87) -0.004 (-0.01)Portugal Jul-94 Dec-13 0.001 (0.00) -0.379 (-0.68)Russia Aug-05 Dec-13 -0.238 (-0.33) 0.836 (1.05)Singapore Jan-94 Dec-13 0.010 (0.05) -0.006 (-0.02)South Africa Jan-94 Dec-13 0.699** (2.52) -0.054 (-0.17)Spain Jan-94 Dec-13 -0.173 (-0.72) 0.288 (1.00)Sri Lanka Jul-07 Dec-13 0.759 (1.07) 1.814** (2.44)Sweden Jan-94 Dec-13 0.718*** (3.18) 0.915* (1.79)Switzerland Jan-94 Dec-13 0.537*** (2.60) 0.688* (1.85)Taiwan Jul-94 Dec-13 -0.007 (-0.03) -0.333 (-1.04)Thailand Jan-94 Dec-13 0.512 (1.60) 0.445 (1.01)Turkey Jul-95 Dec-13 0.548 (1.50) 0.442 (0.77)UK Jan-94 Dec-13 0.457*** (3.25) -0.047 (-0.17)USA Jan-94 Dec-13 0.623*** (4.41) 0.171 (1.02)

25

Tab

le14:Mispricingbased

onalternativesetof

anomalies:Long/short

returnsandthree-factoralphasin

develop

edvs.

emergingmarkets

Thetable

reportsmonthly

raw

returns(in

%)and

monthly

alphas(in

%)obtained

from

regressingquintile-based

long/short

portfoliosof

agg

regated

mispricingonaglobalFamaandFrench

(1993)three-factormodel

(asexplained

inSection

2.3.

ofthe

pap

er).

Agg

regatemispricingis

computedfrom

thefollow

ing20individualanomalies:low

volatility

anom

aly,low

betaan

omaly,

idiosyncratic

risk

anom

aly,maxim

um

daily

return

anomaly,lottery-typestock

anomaly,short-term

return

reversal,

long-term

return

reversal,turnover

anomaly,

return

seasonality

anomaly,interm

ediate

momentum,continuou

sinform

ationarrivalan

omaly,

earningsannou

ncementpremium

anom

aly,dividend

month

anomaly,PEAD

basedon

announcementreturns,

PEAD

based

on

analyst

consensus,

R&D

intensity

anom

aly,R&D

growth

anomaly,200day

mov

ingaverageanom

aly,52weekhighan

omaly,

and

analyst

forecast

dispersionan

omaly.

Com

putationaldetailsfortheseanomalies

are

provided

inTable

1ofthis

OnlineAppendix.

Themechan

ism

tocompute

agg

regatecross-sectionalmispricingfollow

sStambaugh,Yu,andYuan(2015

)andisexplained

indetail

inSection

2.2.of

thepaper.Moreprecisely,foreach

individualanomaly-m

onth-countrycombination,Ifirstrankstocksin

away

that

thepresumably

mostunderpriced(overpriced)stock

receives

thelowest(highest)

rank.Ranksarestan

dardized

tobeuniformly

distributedover

theinterval(0,1]in

aeach

countrymonth.A

few

anomalies

(e.g.,theearnings

annou

ncementpremium

anom

aly)

arebased

onindicator

variables(e.g.,expectedannouncementin

agiven

month

vs.

noexpectedevent).Tomak

etheranking

procedure

forthesereturn

phenom

enacomparable

totheapproach

fortheother

anomalies,Iim

plementthefollow

ingmethod.Let

x%

oftheeligible

firm

sin

agiven

countrymonth

haveanexpectedearningsannouncement(oranother

eventexpectedto

yield

positiveabnormalreturns).Theseeventfirm

sare

then

assigned

arelativerankof0.5*x,andthenon

-eventfirm

sareassign

eda

rankof

0.5+0.5*x.Asin

Stambau

gh,Yu,an

dYuan(2015),

astock’s

composite

mispricingrankis

computedas

thearithmetic

averageofits(upto

20)

individualan

omalyranks.

Toobtain

acomposite

rankin

agiven

month,thestock

under

consideration

has

tohaveatleasteightvalidrelativeranksonindividualanomalies.Ithen

form

long/short

portfoliosbased

onquintiles,

and

presenttheresultsseparately

fordeveloped

marketsandem

ergingmarkets.

InPanel

A,long/short

returnsin

agiven

mon

thare

computedas

thearithmetic

averag

eof

alleligible

country-level

return

estimates.

InPanel

B,alleligible

stocksfrom

alleligible

countriesare

pooled

beforeacountry-neutraltime-series

oflong/short

returnsis

constructed.Thesample

periodis

Jan

uary1994

toDecem

ber

2013

.T-statistics(inparentheses)are

basedontheheteroskedasticity-consistentstan

dard

errors

ofW

hite(1980).

Two-tailed

statisticalsignificance

atthe10%

,5%,and1%

level

isindicatedby*,**,and***,

respectively.

[Continued

overleaf]

26

Develop

edMarkets

EmergingMarkets

Difference

Developed

Markets

EmergingMarkets

Difference

Equally

weightedreturns

Valueweightedreturns

Panel

A:Countryaverage

Raw

returns

1.561*

**0.644**

0.917***

1.361***

0.687

**0.674***

(4.99)

(2.47)

(4.34)

(3.88)

(2.59)

(2.66)

Three-factoralphas

2.001*

**1.195***

0.806***

1.940***

1.229*

**0.710***

(10.60)

(8.37)

(4.05)

(8.50)

(7.30)

(3.06)

Panel

B:Countrycomposite

Raw

returns

1.418*

**1.191***

0.227

1.110***

0.528*

*0.582*

(3.79)

(4.60)

(0.88)

(3.02)

(2.22)

(1.77)

Three-factoralphas

1.696*

**1.487***

0.209

1.491***

0.799

***

0.693**

(8.08)

(8.08)

(0.98)

(6.10)

(4.08)

(2.41)

27

Tab

le15:Mispricingbasedon

extended

setofanomalies:Long/short

returnsandthree-factoralphas

indeveloped

vs.

emergingmarkets

Thetable

reportsmonthly

raw

returns(in

%)and

monthly

alphas(in

%)obtained

from

regressingquintile-based

long/short

portfoliosof

agg

regated

mispricingonaglobalFamaandFrench

(1993)three-factormodel

(asexplained

inSection

2.3.

ofthe

pap

er).Aggregate

mispricingiscomputedfrom

intotal31individualanomalies.More

precisely,Iconsider

the11Stambau

gh,Yu,

andYuan(2015

)an

omaliesas

inthepap

er:failure

probability,

financialdistress,net

stock

issues,compositeequityissues,accruals,

net

operatingassets,mom

entum,gross

profitability,

asset

growth,return

onassets,

andinvestment-to-assets.

Additionally,Itake

thefollow

ing20

individualan

omaliesinto

account:low

volatility

anomaly,low

betaanomaly,idiosyncraticrisk

anom

aly,

max

imum

daily

return

anom

aly,lottery-typestock

anom

aly,short-term

return

reversal,long-term

return

reversal,turnover

anom

aly,

return

season

ality

anomaly,

interm

ediate

mom

entum,continuousinform

ationarrivalanomaly,earningsan

nouncementpremium

anom

aly,

dividen

dmonth

anomaly,PEAD

based

onannouncementreturns,PEAD

basedonanalyst

consensus,R&D

intensity

anom

aly,R&D

grow

thanom

aly,20

0day

mov

ingaverage

anomaly,52weekhighanomaly,andanalyst

forecast

dispersion

anomaly.

Com

putation

al

detailsforalltheseanom

alies

are

provided

inTable1ofthisOnlineAppendix.Themechanism

tocompute

agg

regatecross-sectional

mispricingfollow

sStambau

gh,Yu,an

dYuan

(2015)andisexplained

indetailin

Section2.2.of

thepap

er.More

precisely,foreach

individual

anom

aly-m

onth-countrycombination,Ifirstrankstocksin

away

thatthepresumably

mostunderpriced(overpriced)

stock

receives

thelowest(highest)

rank.Ranksare

standardized

tobeuniform

lydistributedover

theinterval

(0,1]in

aeach

country

mon

th.A

few

anom

alies

(e.g.,

theearnings

announcementpremium

anomaly)are

based

onindicator

variables(e.g.,

expected

annou

ncementin

agiven

month

vs.

noexpectedevent).Tomaketherankingprocedure

forthesereturn

phenomenacomparab

le

totheap

proachfortheother

anomalies,

Iim

plementthefollow

ingmethod.Let

x%

oftheeligible

firm

sin

agiven

countrymon

th

havean

expectedearningsan

nouncement(oranother

eventexpectedto

yield

positiveabnormalreturns).Theseeventfirm

sare

then

assigned

arelativerankof

0.5*x

,andthenon-eventfirm

sare

assigned

arankof0.5+0.5*x.Asin

Stambau

gh,Yu,an

dYuan

(2015),astock’s

compositemispricingrankis

computedasthearithmetic

averageofits(upto

31)individualan

omalyranks.

To

obtain

acompositerankin

agiven

mon

th,thestock

under

considerationhasto

haveatleastfivevalidrankson

theStambau

gh,

Yu,an

dYuan(201

5)anom

alies

andeigh

tvalidrelativeranksontheremaining20anomalies.Ithen

form

long/shortportfolios

based

onquintiles,an

dpresenttheresultsseparately

fordeveloped

marketsandem

ergingmarkets.In

Panel

A,long/shortreturns

inagiven

mon

tharecomputedasthearithmetic

averageofalleligible

country-level

return

estimates.In

Pan

elB,alleligible

stocks

from

alleligible

countriesare

pooledbefore

acountry-neutraltime-series

oflong/short

returnsis

constructed

.Thesample

period

isJan

uary19

94to

Decem

ber

2013

.T-statistics(inparentheses)are

basedontheheterosked

asticity-con

sistentstan

darderrors

of

White(198

0).Two-tailed

statisticalsignificance

atthe10%,5%,and1%

level

isindicatedby*,**

,an

d**

*,respectively.

[Continued

overleaf]

28

Develop

edMarkets

EmergingMarkets

Difference

Developed

Markets

EmergingMarkets

Difference

Equally

weightedreturns

Valueweightedreturns

Panel

A:Countryaverage

Raw

returns

1.519*

**0.842***

0.677***

1.277***

0.862*

**0.414*

(5.35)

(3.19)

(3.28)

(3.83)

(3.17)

(1.66)

Threefactor

alphas

1.959*

**1.344***

0.615***

1.978***

1.379*

**0.598***

(11.46)

(8.06)

(3.04)

(9.87)

(7.42)

(2.65)

Panel

B:Countrycomposite

Raw

returns

1.346*

**1.135***

0.211

1.071***

0.668

***

0.403

(3.95)

(4.36)

(0.79)

(2.97)

(2.68)

(1.25)

Threefactor

alphas

1.629*

**1.405***

0.224

1.542***

0.917

***

0.625**

(8.26)

(6.88)

(0.96)

(6.44)

(4.33)

(2.09)

29

Tab

le16

:Mispricingbasedon

differentgroupsofanomalies:Three-factoralphasin

develop

edvs.

emergingmarkets

Thetable

reports

mon

thly

alphas

(in%)ob

tained

from

regressingquintile-basedlong/short

portfolios

ofag

gregatedmispricingon

aglob

alFam

aandFrench

(1993

)three-factor

model.Aggregate

mispricingisbasedonthreedifferentgroupsof

anom

alies.In

pan

el

A,Iconsider

anom

alies

based

primarilyonaccountingdata:failure

probability,

financialdistress,

accruals,net

operatingassets,

grossprofitability,assetgrowth,return

onassets,investm

ent-to-assets,R&D

intensity

anomaly,R&D

growth

anom

aly.In

pan

elB,I

consider

anomaliesprimarily

basedonmarket

data:momentum,net

stock

issues

anomaly,composite

equityan

omaly,

low

volatility

anom

aly,

low

betaan

omaly,

idiosyncraticrisk

anomaly,maxim

um

dailyreturn

anomaly,lottery-typestock

anom

aly,

short-term

return

reversal,

long-term

return

reversal,

turnover

anomaly,return

seasonality

anomaly,interm

ediate

momentum,continuou

s

inform

ationarrivalanom

aly,200

day

mov

ingaverageanomaly,52weekhighanomaly.In

pan

elC,Iconsider

other

anom

alies:

earningsannou

ncementpremium

anom

aly,dividend

month

anomaly,PEAD

basedon

announcementreturns,

PEAD

based

on

analyst

consensus,

andanalyst

forecast

dispersionanomaly.Computationaldetailsforallthesean

omaliesare

provided

inTab

le

1of

this

OnlineAppendix.In

allpan

els,

themechanism

tocompute

aggregate

cross-sectionalmispricingfollow

sStambau

gh,Yu,

andYuan

(2015

)andisexplained

indetailin

Section2.2.ofthepaper.More

precisely,foreach

individual

anom

aly-m

onth-cou

ntry

combination,Ifirstrankstocksin

away

thatthepresumably

most

underpriced(overpriced)stock

receives

thelowest(highest)

rank.Ran

ksarestan

dardized

tobeuniformly

distributedover

theinterval(0,1]in

aeach

countrymon

th.A

few

anom

alies(e.g.,the

earningsannou

ncementpremium

anomaly)are

basedonindicatorvariables(e.g.,expectedannou

ncementin

agiven

mon

thvs.no

expectedevent).Tomaketherankingprocedure

forthesereturn

phenomenacomparable

totheapproach

fortheother

anom

alies,

Iim

plementthefollow

ingmethod.Let

x%

oftheeligible

firm

sin

agiven

countrymonth

haveanexpectedearnings

annou

ncement

(oran

other

eventexpectedto

yield

positiveabnorm

alreturns).Theseeventfirm

sare

then

assigned

arelativerankof

0.5*x,an

d

thenon

-eventfirm

sare

assigned

arankof0.5+0.5*x.Asin

Stambaugh,Yu,andYuan(2015),astock’scompositemispricingrank

iscomputedas

thearithmetic

averag

eof

itsindividualanomaly

ranks.

Toobtain

acomposite

rankin

agiven

mon

th,thestock

under

considerationhas

tohaveat

least

fivevalidranksonindividualanomalies

inpanel

AorB

orthreevalidranks(dueto

the

lower

number

ofunderlyingan

omalies)

inpanel

C.Ithen

form

long/short

portfoliosbasedon

quintiles,

andpresenttheresults

separatelyfordeveloped

marketsan

dem

ergingmarkets.

“Countryaverage”

meansthatlong/short

returnsin

agiven

mon

thare

computedasthearithmeticaverage

ofalleligiblecountry-levelreturn

estimates.“Countrycomposite”meansthat

alleligiblestocks

from

alleligible

countriesare

pooledbefore

acountry-neutraltime-series

oflong/short

returnsis

constructed

.Thesample

period

isJan

uary19

94to

Decem

ber

2013

.T-statistics(inparentheses)are

basedontheheterosked

asticity-con

sistentstan

darderrors

of

White(198

0).Two-tailed

statisticalsignificance

atthe10%,5%,and1%

level

isindicatedby*,**

,an

d**

*,respectively.

[Continued

overleaf]

30

Developed

Markets

EmergingMarkets

Differen

ceDeveloped

Markets

EmergingMarkets

Differen

ce

Equallyweightedreturns

Valueweightedreturns

Panel

A:Aggregate

mispricingcomputedfrom

anomalies

(primarily)basedonaccountingdata

Countryav

erage

Threefactoralphas

1.027***

0.765***

-0.263**

0.966***

0.779***

-0.187

(10.92)

(7.13)

(-2.02)

(7.80)

(5.62)

(-1.03)

Countrycomposite

Threefactoralphas

1.078***

0.765***

-0.313**

0.938***

0.752***

-0.186

(11.18)

(6.44)

(-2.32)

(6.37)

(4.34)

(-0.83)

Panel

B:Aggregate

mispricingcomputedfrom

anomalies

(primarily)basedonmarket

data

Countryav

erage

Threefactoralphas

1.895***

1.382***

-0.513**

1.927***

1.290***

-0.637***

(10.19)

(7.67)

(-2.45)

(8.60)

(6.12)

(-2.64)

Countrycomposite

Threefactoralphas

1.423***

1.419***

-0.003

1.393***

0.890***

-0.504

(6.58)

(6.53)

(-0.01)

(5.77)

(3.75)

(-1.64)

Panel

C:Aggregate

mispricingcomputedfrom

other

anomalies

Countryav

erage

Threefactoralphas

1.289***

0.755***

-0.534***

1.041***

0.631***

-0.411**

(14.05)

(5.88)

(-3.89)

(8.46)

(3.89)

(-2.30)

Countrycomposite

Threefactoralphas

1.034***

0.641***

-0.393***

0.866***

0.439**

-0.428*

(10.21)

(5.22)

(-3.04)

(5.70)

(2.42)

(-1.89)

31

Figure

1:Fractionofstockswithvalidanomaly

rankings:

Argentina

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

32

Figure

2:Fractionofstockswithvalidanomaly

rankings:

Australia

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

33

Figure

3:Fractionofstockswithvalidanomaly

rankings:

Austria

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

34

Figure

4:Fractionofstockswithvalidanomaly

rankings:

Belgium

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

35

Figure

5:Fractionofstockswithvalidanomaly

rankings:

Brazil

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

36

Figure

6:Fractionofstockswithvalidanomaly

rankings:

Can

ada

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

37

Figure

7:Fractionofstockswithvalidanomaly

rankings:

Chile

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

38

Figure

8:Fractionofstockswithvalidanomaly

rankings:

China

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

39

Figure

9:Fractionofstockswithvalidanomaly

rankings:

Colombia

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

40

Figure

10:Fractionofstockswithvalidanomaly

rankings:

Denmark

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

41

Figure

11:

Fractionofstockswithvalidanomaly

rankings:

Egypt

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

42

Figure

12:Fractionofstockswithvalidanomaly

rankings:

Finland

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

43

Figure

13:

Fractionofstockswithvalidanomaly

rankings:

France

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

44

Figure

14:

Fractionofstockswithvalidanomaly

rankings:

German

y

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

45

Figure

15:Fractionofstockswithvalidanomaly

rankings:

Greece

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

46

Figure

16:

Fractionofstockswithvalidanomaly

rankings:

HongKong

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

47

Figure

17:

Fractionofstockswithvalidanomaly

rankings:

India

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

48

Figure

18:

Fractionofstockswithvalidanomaly

rankings:

Indon

esia

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

49

Figure

19:Fractionofstockswithvalidanomaly

rankings:

Ireland

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

50

Figure

20:Fractionofstockswithvalidanomaly

rankings:

Israel

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

51

Figure

21:

Fractionofstockswithvalidanomaly

rankings:

Italy

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

52

Figure

22:Fractionofstockswithvalidanomaly

rankings:

Japan

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

53

Figure

23:

Fractionofstockswithvalidanomaly

rankings:

Jordan

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

54

Figure

24:

Fractionofstockswithvalidanomaly

rankings:

Korea

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

55

Figure

25:

Fractionofstockswithvalidanomaly

rankings:

Malay

sia

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

56

Figure

26:

Fractionofstockswithvalidanomaly

rankings:

Mexico

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

57

Figure

27:Fractionofstockswithvalidanomaly

rankings:

Morocco

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

58

Figure

28:Fractionofstockswithvalidanomaly

rankings:

Netherlands

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

59

Figure

29:Fractionofstockswithvalidanomaly

rankings:

New

Zealand

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

60

Figure

30:Fractionofstockswithvalidanomaly

rankings:

Norway

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

61

Figure

31:

Fractionofstockswithvalidanomaly

rankings:

Pak

istan

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

62

Figure

32:

Fractionofstockswithvalidanomaly

rankings:

Philippines

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

63

Figure

33:

Fractionofstockswithvalidanomaly

rankings:

Poland

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

64

Figure

34:Fractionofstockswithvalidanomaly

rankings:

Portuga

l

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

65

Figure

35:

Fractionofstockswithvalidanomaly

rankings:

Russia

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

66

Figure

36:

Fractionofstockswithvalidanomaly

rankings:

Singap

ore

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

67

Figure

37:Fractionofstockswithvalidanomaly

rankings:

South

Africa

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

68

Figure

38:Fractionofstockswithvalidanomaly

rankings:

Spain

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

69

Figure

39:

Fractionofstockswithvalidanomaly

rankings:

Sweden

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

70

Figure

40:

Fractionofstockswithvalidanomaly

rankings:

Switzerlan

d

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

71

Figure

41:

Fractionofstockswithvalidanomaly

rankings:

Taiwan

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

72

Figure

42:Fractionofstockswithvalidanomaly

rankings:

Thailand

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

73

Figure

43:Fractionofstockswithvalidanomaly

rankings:

Turkey

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

74

Figure

44:Fractionofstockswithvalidanomaly

rankings:

UK

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

75

Figure

45:

Fractionofstockswithvalidanomaly

rankings:

USA

Iconsider

stocksthatsurvivethebasicdata

screen

soutlined

inSection2.1

ofthepaper.Thesample

periodcorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Agg

rega

te m

ispr

icin

g sc

ore

Fai

lure

pro

babi

lity

Ohl

son’

s O

(di

stre

ss)

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

sto

ck is

sues

Com

posi

te e

quity

Acc

rual

s

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Net

ope

ratin

g as

sets

Mom

entu

mG

ross

pro

fitab

ility

0.2.4.6.81 1994

1998

2002

2006

2010

2014

year

Ass

et g

row

thR

etur

n on

ass

ets

Inve

stm

ent−

to−

asse

ts

76

Table 17: Developed vs. emerging markets: Fraction of stocks with valid anomaly rankings

The table compares the fraction of stocks with valid (individual or composite)anomaly rankings in developed markets relative to emerging markets. For each coun-try, I consider stocks that survive the basic data screens outlined in Section 2.1. ofthe paper. The sample period for each country corresponds to the sample periodshown in panel B of Table 1 in the paper. I pool all valid country years and regressthem on a developed market dummy which is one (zero) if a country year is classifiedas a developed (emerging) market. Panel A shows results for the full sample period(1994-2013), panel B and C show results for the first and second half of the sampleperiod, respectively. In all panels, standard errors are double-clustered by countryand year. Two-tailed statistical significance at the 10%, 5%, and 1% level is indicatedby *, **, and ***, respectively.

Panel A: Full sample period (1994-2014)

Developed market dummy t-statistic Constant N R2

Mispricing score 0.129*** (3.11) 0.69 803 0.087

Failure probability 0.122*** (3.40) 0.62 803 0.079Ohlson’s O (distress) 0.101*** (2.75) 0.60 803 0.057Net stock issues -0.008 (-0.57) 0.94 803 0.004Composite equity 0.094*** (2.60) 0.63 803 0.064Accruals 0.111*** (3.06) 0.56 803 0.065Net operating assets 0.112*** (2.92) 0.64 803 0.066Momentum -0.018 (-1.39) 0.98 803 0.030Gross profitability 0.079** (2.33) 0.64 803 0.036Asset growth 0.126*** (2.82) 0.71 803 0.078Return on assets 0.125*** (2.84) 0.70 803 0.081Investment-to-assets 0.088** (2.52) 0.55 803 0.036

Panel B: First half of sample period (1994-2003)

Developed market dummy t-statistic Constant N R2

Mispricing score 0.254*** (5.43) 0.49 376 0.309

Failure probability 0.212*** (5.09) 0.45 376 0.241Ohlson’s O (distress) 0.200*** (5.08) 0.42 376 0.240Net stock issues -0.007 (-0.41) 0.92 376 0.002Composite equity 0.131** (2.45) 0.53 376 0.108Accruals 0.211*** (5.38) 0.38 376 0.259Net operating assets 0.219*** (5.18) 0.45 376 0.259Momentum -0.016 (-1.21) 0.97 376 0.023Gross profitability 0.150*** (3.67) 0.48 376 0.154Asset growth 0.253*** (5.10) 0.51 376 0.289Return on assets 0.256*** (5.23) 0.50 376 0.303Investment-to-assets 0.163*** (4.38) 0.36 376 0.167

Panel C: Second half of sample period (2004-2013)

Developed market dummy t-statistic Constant N R2

Mispricing score 0.030 (0.84) 0.86 427 0.012

Failure probability 0.053 (1.41) 0.76 427 0.027Ohlson’s O (distress) 0.024 (0.63) 0.75 427 0.006Net stock issues -0.008 (-0.53) 0.95 427 0.004Composite equity 0.068* (1.92) 0.71 427 0.056Accruals 0.034 (0.83) 0.71 427 0.011Net operating assets 0.030 (0.76) 0.79 427 0.010Momentum -0.019 (-1.38) 0.99 427 0.037Gross profitability 0.026 (0.71) 0.78 427 0.007Asset growth 0.024 (0.63) 0.88 427 0.007Return on assets 0.021 (0.57) 0.87 427 0.006Investment-to-assets 0.034 (0.76) 0.70 427 0.009

77

Figure

46:Number

ofindividualanomalies

underlyingthecomposite

mispricingscore

(1/4

)

Onacountry-by-countrybasis,thegraphscompare

theav

eragenumber

ofindividualanomalies

underlyingthecomposite

Stambaugh,Yu,andYuan(2015)

mispricingscore,conditionalontheavailabilityofatleast

fiveindividualanomaly

ranks.

Thesample

periodforeach

countrycorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

567891011 1994

1998

2002

2006

2010

2014

year

Arg

entin

aA

ustr

alia

Aus

tria

567891011 1994

1998

2002

2006

2010

2014

year

Bel

gium

Bra

zil

Can

ada

567891011 1994

1998

2002

2006

2010

2014

year

Chi

leC

hina

Col

ombi

a

567891011 1994

1998

2002

2006

2010

2014

year

Den

mar

kE

gypt

Fin

land

78

Figure

47:Number

ofindividualanomalies

underlyingthecomposite

mispricingscore

(2/4

)

Onacountry-by-countrybasis,thegraphscompare

theav

eragenumber

ofindividualanomalies

underlyingthecomposite

Stambaugh,Yu,andYuan(2015)

mispricingscore,conditionalontheavailabilityofatleast

fiveindividualanomaly

ranks.

Thesample

periodforeach

countrycorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

567891011 1994

1998

2002

2006

2010

2014

year

Fra

nce

Ger

man

yG

reec

e

567891011 1994

1998

2002

2006

2010

2014

year

Hon

g K

ong

Indi

aIn

done

sia

567891011 1994

1998

2002

2006

2010

2014

year

Irel

and

Isra

elIta

ly

567891011 1994

1998

2002

2006

2010

2014

year

Japa

nJo

rdan

Kor

ea

79

Figure

48:Number

ofindividualanomalies

underlyingthecomposite

mispricingscore

(3/4

)

Onacountry-by-countrybasis,thegraphscompare

theav

eragenumber

ofindividualanomalies

underlyingthecomposite

Stambaugh,Yu,andYuan(2015)

mispricingscore,conditionalontheavailabilityofatleast

fiveindividualanomaly

ranks.

Thesample

periodforeach

countrycorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

567891011 1994

1998

2002

2006

2010

2014

year

Mal

aysi

aM

exic

oM

oroc

co

567891011 1994

1998

2002

2006

2010

2014

year

Net

herla

nds

New

Zea

land

Nor

way

567891011 1994

1998

2002

2006

2010

2014

year

Pak

ista

nP

hilip

pine

sP

olan

d

567891011 1994

1998

2002

2006

2010

2014

year

Por

tuga

lR

ussi

aS

inga

pore

80

Figure

49:Number

ofindividualanomalies

underlyingthecomposite

mispricingscore

(4/4

)

Onacountry-by-countrybasis,thegraphscompare

theav

eragenumber

ofindividualanomalies

underlyingthecomposite

Stambaugh,Yu,andYuan(2015)

mispricingscore,conditionalontheavailabilityofatleast

fiveindividualanomaly

ranks.

Thesample

periodforeach

countrycorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.

567891011 1994

1998

2002

2006

2010

2014

year

Sou

th A

fric

aS

pain

Sw

eden

567891011 1994

1998

2002

2006

2010

2014

year

Sw

itzer

land

Tai

wan

Tha

iland

567891011 1994

1998

2002

2006

2010

2014

year

Tur

key

UK

US

A

81

Table 18: Number of individual anomalies underlying the composite mispricing score: Descriptivestatistics

On a country-by-country basis, the table compares the number of individual anoma-lies underlying the composite Stambaugh, Yu, and Yuan (2015) mispricing score,conditional on non-missing values of the score (i.e., conditional on the availability ofat least five individual anomaly ranks). The unit of observation is the average num-ber of individual anomalies in a country year. The sample period for each countrycorresponds to the sample period shown in panel B of Table 1 in the paper.

Country Sample Start Sample End N Mean SD Min Min

Argentina 2000 2009 10 9.73 0.88 8.06 10.41Australia 1994 2013 20 10.17 0.16 9.96 10.48Austria 1994 2013 20 9.30 0.33 8.35 9.78Belgium 1994 2013 20 9.59 0.31 9.08 10.10Brazil 1998 2013 16 9.75 0.35 8.98 10.18Canada 1994 2013 20 9.93 0.22 9.65 10.32Chile 1994 2013 20 10.01 0.13 9.71 10.16China 1995 2013 19 10.24 0.48 8.81 10.70Colombia 2007 2013 7 7.53 1.65 5.62 9.37Denmark 1994 2013 20 9.35 0.23 8.87 9.66Egypt 2004 2013 10 9.05 1.34 5.63 9.78Finland 1994 2013 20 9.90 0.68 8.58 10.63France 1994 2013 20 9.87 0.43 8.94 10.32Germany 1994 2013 20 9.81 0.41 8.77 10.29Greece 1994 2013 20 9.77 0.54 8.80 10.51Hongkong 1994 2013 20 9.79 0.13 9.56 10.04India 1994 2013 20 10.31 0.25 9.73 10.72Indonesia 1994 2013 20 9.78 0.14 9.57 9.96Ireland 1996 2013 18 8.95 1.63 5.00 10.06Israel 1998 2013 16 9.39 1.00 6.40 10.29Italy 1994 2013 20 9.27 0.37 8.65 9.86Japan 1994 2013 20 10.38 0.15 10.14 10.63Jordan 2007 2008 2 8.42 0.05 8.39 8.46Korea 1994 2013 20 10.07 0.28 9.45 10.46Malaysia 1994 2013 20 10.26 0.20 10.02 10.57Mexico 1994 2013 20 9.95 0.15 9.68 10.19Morocco 2006 2013 8 8.65 1.32 5.68 9.44Netherlands 1994 2013 20 10.26 0.29 9.67 10.58New Zealand 1997 2013 17 10.30 0.20 9.93 10.66Norway 1994 2013 20 9.65 0.37 9.02 10.15Pakistan 1994 2998 15 9.59 0.42 8.62 10.00Philippines 1994 2013 20 9.40 0.39 8.27 9.84Poland 1998 2013 16 8.81 1.07 6.54 10.30Portugal 1994 2013 20 10.03 0.28 9.38 10.31Russia 2005 2013 9 9.36 0.55 8.45 10.06Singapore 1994 2013 20 10.23 0.17 9.92 10.51South Africa 1994 2013 20 9.80 0.28 9.23 10.18Spain 1994 2013 20 9.76 0.22 9.42 10.15Sweden 1994 2013 20 9.98 0.42 9.13 10.47Switzerland 1994 2013 20 9.57 0.17 9.27 9.79Taiwan 1994 2013 20 10.31 0.22 9.92 10.60Thailand 1994 2013 20 9.81 0.26 9.40 10.26Turkey 1994 2013 20 9.72 0.66 7.75 10.17UK 1994 2013 20 10.19 0.16 9.94 10.40USA 1994 2013 20 10.12 0.09 9.99 10.29

82

Figure

50:Number

ofindividual

anomaliesunderlyingthecomposite

mispricingscore:Averagedeveloped

vs.

average

emergingmarket

Sep

arately

foreach

year,

thegraphcomparestheav

eragenumber

ofindividualanomalies

underlyingava

lidcomposite

Stambaugh,Yu,andYuan(2015)

mispricing

score

intheav

eragedeveloped

market

with

theresp

ectivenumber

intheav

erageem

erging

market.Thesample

period

foreach

country

correspondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.Please

note

thatthis

figure

correspondsto

the“countryav

erage”

weighting

schem

erelied

onin

thepaper,whileFigure

51relies

onthe“countrycomposite”weightingschem

e.567891011 19

9419

9820

0220

0620

1020

14

Em

ergi

ng m

arke

tsD

evel

oped

mar

kets

83

Table 19: Number of individual anomalies underlying the composite mispricing score: Developed

vs. emerging markets

Separately for each year, panel A compares the average number of individual anoma-

lies underlying a valid composite Stambaugh, Yu, and Yuan (2015) mispricing score in

the average developed market with the respective number of anomalies in the average

emerging market. More precisely, I regress the average number of individual anoma-

lies on a developed market dummy which is one (zero) if a country year is classified

as a developed (emerging) market. T-statistics are based on the heteroskedasticity-

consistent standard errors of White (1980). Panel B shows results from a panel

regression covering all developed and emerging markets as well as a sample period

from 1994 to 2013. Standard errors are double-clustered by country and year. In both

panels, two-tailed statistical significance at the 10%, 5%, and 1% level is indicated

by *, **, and ***, respectively.

Panel A: Year-by-year comparison

Year Developed market dummy t-statistic Constant N R2

1994 0.18 (0.78) 9.50 33 0.020

1995 0.15 (0.72) 9.57 34 0.017

1996 -0.27 (-0.97) 9.79 35 0.022

1997 -0.25 (-0.93) 9.85 36 0.019

1998 0.18 (0.53) 9.38 39 0.008

1999 -0.12 (-0.54) 9.63 39 0.009

2000 -0.15 (-0.73) 9.64 40 0.015

2001 0.05 (0.29) 9.51 40 0.002

2002 0.11 (0.65) 9.58 40 0.012

2003 0.04 (0.32) 9.79 40 0.003

2004 0.25 (0.94) 9.68 41 0.027

2005 0.23 (1.31) 9.77 42 0.046

2006 0.28 (1.16) 9.71 43 0.035

2007 0.42* (1.83) 9.53 45 0.075

2008 0.40* (1.70) 9.61 45 0.065

2009 0.28 (1.28) 9.82 43 0.043

2010 0.26 (1.46) 9.85 42 0.061

2011 0.16 (1.47) 9.98 42 0.054

2012 0.10 (0.84) 10.04 42 0.016

2013 0.13 (1.10) 10.06 42 0.030

Panel B: Panel regression

Developed market dummy t-statistic Constant N R2

1994-2013 0.13 (1.02) 9.72 803 0.009

84

Figure

51:Number

ofindividual

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alies

underlyingthecomposite

mispricingscore:Composite

develop

edvs.

compositeem

ergingmarket

Sep

arately

foreach

year,

thegraphcomparestheav

eragenumber

ofindividualanomalies

underlyingava

lidcomposite

Stambaugh,Yu,andYuan(2015)

mispricingscore

inthepooledstock-level

observationsin

developed

marketswiththepooledstock-level

observationsin

developed

markets.

Thesample

periodforeach

countrycorrespondsto

thesample

periodshow

nin

panel

BofTable

1in

thepaper.Please

note

thatthisfigure

correspondsto

the“country

composite”weightingschem

erelied

onin

thepaper,whileFigure

50relies

onthe“countryav

erage”

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e.567891011 19

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evel

oped

mar

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85

Figure 52: Simulated composite mispricing based on five randomly selected individual anomalies

(universe: 11 Stambaugh, Yu, and Yuan (2015) anomalies, equally weighted returns): Alpha

difference between developed markets and emerging markets

The upper figure on the following page shows the distribution of the difference between the monthly

alpha (in %, equally weighted returns) obtained from exploiting aggregate cross-sectional mispricing

in developed markets and the alpha obtained in emerging markets. The lower figure shows the

distribution of the respective two-tailed t-statistic. The distribution is based on 10,000 simulations.

For each simulation, I randomly select five individual anomalies from the 11 individual anomalies

considered in Stambaugh, Yu, and Yuan (2015) as well as in the paper (failure probability, financial

distress, net stock issues, composite equity, total accruals, net operating assets, momentum, gross

profitability, asset growth, return on assets, investments-to-assets). I allow for the overweighting

of specific anomalies in that a specific anomaly can be drawn several times within one simulation.

The mechanism to compute aggregate cross-sectional mispricing based on these randomly selected

individual anomalies follows Stambaugh, Yu, and Yuan (2015) and is explained in detail in Section

2.2. of the paper as well as in Tables 14 and 15 of this Online Appendix. In each country month, the

portfolio goes long (short) stocks in the bottom (top) quintile of mispricing. Long/short returns for

developed or emerging markets in a given month are computed as the arithmetic average of all eligible

country-level return estimates. Alphas are then computed relative to a global Fama and French

(1993) three-factor model (as explained in Section 2.3. of the paper). The sample period is January

1994 to December 2013. The graphs focus on the difference of the alpha (and the corresponding

two-tailed t-statistic) obtained in developed and emerging markets. The average alpha difference is

26 bp. The difference is positive in 92.71% of the cases. The corresponding t-statistic is larger than

2 (smaller than -2) in 43.03% (0.00%) of the 10,000 simulations.

86

020

040

060

0F

requ

ency

−.5 0 .5 1alpha in developed markets − alpha in emerging markets

020

040

060

0F

requ

ency

−2 0 2 4 6T−statistic of the difference in alpha

87

Figure 53: Simulated composite mispricing based on five randomly selected individual anoma-

lies (universe: 11 Stambaugh, Yu, and Yuan (2015) anomalies, value weighted returns): Alpha

difference between developed markets and emerging markets

The upper figure on the following page shows the distribution of the difference between the monthly

alpha (in %, value weighted returns) obtained from exploiting aggregate cross-sectional mispricing

in developed markets and the alpha obtained in emerging markets. The lower figure shows the

distribution of the respective two-tailed t-statistic. The distribution is based on 10,000 simulations.

For each simulation, I randomly select five individual anomalies from the 11 individual anomalies

considered in Stambaugh, Yu, and Yuan (2015) as well as in the paper (failure probability, financial

distress, net stock issues, composite equity, total accruals, net operating assets, momentum, gross

profitability, asset growth, return on assets, investments-to-assets). I allow for the overweighting

of specific anomalies in that a specific anomaly can be drawn several times within one simulation.

The mechanism to compute aggregate cross-sectional mispricing based on these randomly selected

individual anomalies follows Stambaugh, Yu, and Yuan (2015) and is explained in detail in Section

2.2. of the paper as well as in Tables 14 and 15 of this Online Appendix. In each country month, the

portfolio goes long (short) stocks in the bottom (top) quintile of mispricing. Long/short returns for

developed or emerging markets in a given month are computed as the arithmetic average of all eligible

country-level return estimates. Alphas are then computed relative to a global Fama and French

(1993) three-factor model (as explained in Section 2.3. of the paper). The sample period is January

1994 to December 2013. The graphs focus on the difference of the alpha (and the corresponding

two-tailed t-statistic) obtained in developed and emerging markets. The average alpha difference is

26 bp. The difference is positive in 88.03% of the cases. The corresponding t-statistic is larger than

2 (smaller than -2) in 29.01% (0.00%) of the 10,000 simulations.

88

020

040

060

0F

requ

ency

−.5 0 .5 1alpha in developed markets − alpha in emerging markets

020

040

060

0F

requ

ency

−2 0 2 4 6T−statistic of the difference in alpha

89

Figure 54: Simulated composite mispricing based on five randomly selected individual anomalies

(universe: 31 anomalies, equally weighted returns): Alpha difference between developed markets

and emerging markets

The upper figure on the following page shows the distribution of the difference between the monthly

alpha (in %, equally weighted returns) obtained from exploiting aggregate cross-sectional mispric-

ing in developed markets and the alpha obtained in emerging markets. The lower figure shows the

distribution of the respective two-tailed t-statistic. The distribution is based on 10,000 simulations.

For each simulation, I randomly select five individual anomalies from the 31 individual anomalies

described in detail in Table 1 of this Online Appendix (failure probability, financial distress, net

stock issues, composite equity, total accruals, net operating assets, momentum, gross profitability,

asset growth, return on assets, investments-to-assets, low volatility anomaly, low beta anomaly, id-

iosyncratic risk anomaly, maximum daily return anomaly, lottery-type stock anomaly, short-term

return reversal, long-term return reversal, turnover anomaly, return seasonality anomaly, intermedi-

ate momentum, continuous information arrival anomaly, earnings announcement premium anomaly,

dividend month anomaly, PEAD based on announcement returns, PEAD based on analyst consen-

sus, R&D intensity anomaly, R&D growth anomaly, 200 day moving average anomaly, 52 week high

anomaly, and analyst forecast dispersion anomaly). I allow for the overweighting of specific anoma-

lies in that a specific anomaly can be drawn several times within one simulation. The mechanism to

compute aggregate cross-sectional mispricing based on these randomly selected individual anomalies

follows Stambaugh, Yu, and Yuan (2015) and is explained in detail in Section 2.2. of the paper as

well as in Tables 14 and 15 of this Online Appendix. In each country month, the portfolio goes long

(short) stocks in the bottom (top) quintile of mispricing. Long/short returns for developed or emerg-

ing markets in a given month are computed as the arithmetic average of all eligible country-level

return estimates. Alphas are then computed relative to a global Fama and French (1993) three-factor

model (as explained in Section 2.3. of the paper). The sample period is January 1994 to December

2013. The graphs focus on the difference of the alpha (and the corresponding two-tailed t-statistic)

obtained in developed and emerging markets. The average alpha difference is 37 bp. The difference

is positive in 92.03% of the cases. The corresponding t-statistic is larger than 2 (smaller than -2) in

56.59% (0.44%) of the 10,000 simulations.

90

020

040

060

0F

requ

ency

−.5 0 .5 1 1.5alpha in developed markets − alpha in emerging markets

020

040

060

0F

requ

ency

−5 0 5 10T−statistic of the difference in alpha

91

Figure 55: Simulated composite mispricing based on five randomly selected individual anomalies

(universe: 31 anomalies, value weighted returns): Alpha difference between developed markets

and emerging markets

The upper figure on the following page shows the distribution of the difference between the monthly

alpha (in %, value weighted returns) obtained from exploiting aggregate cross-sectional mispricing in

developed markets and the alpha obtained in emerging markets. The lower figure shows the distribu-

tion of the respective two-tailed t-statistic. The distribution is based on 10,000 simulations. For each

simulation, I randomly select five individual anomalies from the 31 individual anomalies described

in detail in Table 1 of this Online Appendix (failure probability, financial distress, net stock issues,

composite equity, total accruals, net operating assets, momentum, gross profitability, asset growth,

return on assets, investments-to-assets, low volatility anomaly, low beta anomaly, idiosyncratic risk

anomaly, maximum daily return anomaly, lottery-type stock anomaly, short-term return reversal,

long-term return reversal, turnover anomaly, return seasonality anomaly, intermediate momentum,

continuous information arrival anomaly, earnings announcement premium anomaly, dividend month

anomaly, PEAD based on announcement returns, PEAD based on analyst consensus, R&D inten-

sity anomaly, R&D growth anomaly, 200 day moving average anomaly, 52 week high anomaly, and

analyst forecast dispersion anomaly). I allow for the overweighting of specific anomalies in that a

specific anomaly can be drawn several times within one simulation. The mechanism to compute

aggregate cross-sectional mispricing based on these randomly selected individual anomalies follows

Stambaugh, Yu, and Yuan (2015) and is explained in detail in Section 2.2. of the paper as well as

in Tables 14 and 15 of this Online Appendix. In each country month, the portfolio goes long (short)

stocks in the bottom (top) quintile of mispricing. Long/short returns for developed or emerging

markets in a given month are computed as the arithmetic average of all eligible country-level re-

turn estimates. Alphas are then computed relative to a global Fama and French (1993) three-factor

model (as explained in Section 2.3. of the paper). The sample period is January 1994 to December

2013. The graphs focus on the difference of the alpha (and the corresponding two-tailed t-statistic)

obtained in developed and emerging markets. The average alpha difference is 30 bp. The difference

is positive in 84.97% of the cases. The corresponding t-statistic is larger than 2 (smaller than -2) in

36.15% (0.56%) of the 10,000 simulations.

92

010

020

030

040

050

0F

requ

ency

−.5 0 .5 1 1.5alpha in developed markets − alpha in emerging markets

010

020

030

040

050

0F

requ

ency

−4 −2 0 2 4 6T−statistic of the difference in alpha

93

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