How Important Is the Financial Media in Global...

How Important Is the Financial Media inGlobal Markets?

John M. GriffinUniversity of Texas at Austin, McCombs School of Business

Nicholas H. HirscheyUniversity of Texas at Austin, McCombs School of Business

Patrick J. KellyNew Economic School and University of South Florida

Thisarticle studies differences in the information content of 870,000 news announcementsin 56 markets around the world. In most developed markets, a firm’s stock price movesmuch more on days with public news about the firm. In contrast, in many emerging marketsvolatility is similar on news and non-news days. We examine several hypotheses for ourfindings. Cross-country differences in stock price reactions are best explained by insidertrading, followed by differences in the quality of the news dissemination mechanism. Ourfindings are useful for quantifying the extent of insider trading and how the financial mediaaffects international markets. (JEL G14, G15)

Public news announcements are a major mechanism for disseminating in-formation to investors. Each day the financial media releases thousands ofarticles covering companies in markets worldwide. Investors use this news toestimate assets’ fundamental values. Despite the perceived importance of the

The first two authors are from the McCombs School of Business at the University of Texas at Austin.Kelly is at the New Economic School in Moscow and the University of South Florida in Tampa. Wethank Utpal Bhattacharya, Bernie Black, Zhiwu Chen, Craig Doidge, Alexander Dyck, Miguel Ferreira,Gary Gebhardt, Jay Hartzell, Ravi Jagannathan, Yu Jing, Lidia Kelly, Alok Kumar, David Lesmond, MarcLipson, Christian Leuz, Xi Liu, Lilian Ng, Peter Mackay, Federico Nardari, David Ng, Clemens Sialm, StefanSiegel, Paul Tetlock, Sheridan Titman, and seminar participants at the Chinese University of Hong Kong,Chulalongkorn Accounting and Finance Symposium, Darden International Conference, International FinanceConference at McGill University, Fudan University, Jiao Tong University, Hong Kong University of Scienceand Technology, Nanyang Technological University, New Economic School, Peking University, SingaporeManagement University, University of Melbourne, University of Texas at Austin, and University of Washingtonfor helpful comments and discussion. We are grateful to Mikael Bergbrant, Garret Fair, and Jordan Nickersonfor their diligent research assistance. Send correspondence to John M. Griffin, University of Texas at Austin,McCombs School of Business, 1 University Station B6600 Austin, TX 78746; telephone: (512) 471-6621; fax:(512) 471-3034. E-mail: [email protected].

c© The Author 2011. Published by Oxford University Press on behalf of The Society for Financial Studies.All rights reserved. For Permissions, please e-mail: [email protected]:10.1093/rfs/hhr099 Advance Access publication October 25, 2011

by Patrick Kelly on N

ovember 15, 2011

http://rfs.oxfordjournals.org/D

ownloaded from

http://rfs.oxfordjournals.org/

TheReview of Financial Studies / v 24 n 12 2011

financialmedia, there has been little attempt to either quantify its importanceinternationally or to understand why its impact may vary across countries.

This article studies differences in the information content of public newsannouncements in international equity markets. We quantify how the marketreaction to these announcements varies across countries and examine potentialexplanations for these differences. These analyses deepen our understanding ofdifferences in the international information environment and how informationis disseminated to investors.

There are several reasons why the market reaction to news announcementsmay differ across countries. First, there may be differences in how wellinvestors anticipate the information in a news announcement using publicnews channels, such as peer firms’ news announcements. Second, the pricemay incorporate the information in a news release prior to its announcementbecause of insider trading. Third, if journalists are more sophisticated in certaincountries, one might expect news coverage in countries with sophisticatedjournalists to provide a more precise signal and lead to larger market reactions.Fourth, accounting quality may differ across countries. We find the mostsupport for insider trading and, to a lesser extent, differences in the qualityof the news dissemination mechanism.

We start by collecting a large sample of articles about international stocksfrom the Factiva news archive. Our sample of general news articles fromJanuary 2003 to June 2009 covers 2,593 firms, with 572,987 news articles in26 developed markets and 298,614 in 30 emerging markets.

To facilitate comparison across stocks, we first study a news event that iscommon to all firms—earnings announcements. We ensure the accuracy ofBloomberg earnings announcement dates through Factiva by a simple algo-rithm, which leads to extremely accurate earnings announcement dates. Wefind that volatility on event days varies substantially both between developedand emerging markets and within each category. Stock price moves range from50% more than normal volatility in a number of developed markets (Denmark,the United Kingdom, Sweden, the Netherlands, the United States, Finland,Hong Kong, and Germany) to less than 5% more than normal in severalemerging markets (Thailand, Turkey, Mexico, and Indonesia). China exhibitsearnings reactions only 12% above normal.

Second, we extend our study to all news articles. News is generally moreuseful for explaining idiosyncratic stock return variation in developed marketsthan in emerging markets. We find that our results are not driven by earningsannouncements, time-stamp conventions, or the type of international financialnews.

Third, we use cross-country variables related to the four main hypothe-ses regarding public informed trading, insider trading, news dissemination,and accounting quality. We investigate cross-country regressions with bothearnings and general news. With the stock price reactions to earnings newsand standard cross-country regressions, we find that a survey measure of the

3942


ovember 15, 2011


ownloaded from


HowImportant Is the Financial Media in Global Markets?

prevalence of insider trading, a measure of technological development thatwe interpret as relating to the speed of news dissemination, and accountingstandards are most pertinent. We then investigate a wider set of variablesrelating to our main hypotheses and other variables commonly used in cross-country studies through the Bayesian Stochastic Search Variable Selection(SSVS) methodology ofGeorge and McCulloch(1993). The SSVS selectsimportant explanatory variables based on a combination of their economicand statistical significance while incorporating skepticism to account for theprobability a variable is picked by chance. For earnings news, the procedureconfirms that insider trading, technological development, and accountingstandards are highly important. When investigating the importance of all typesof news, accounting standards are no longer significant and insider tradingand technological development stand out as the most important determinants.Overall, the cross-country regressions show the most support for the insidertrading hypothesis, followed by technological development as a proxy for thenews dissemination mechanism.

We investigate two other hypotheses related to insider trading to furtherscrutinize our findings. If insider trading is part of the reason that public newsevents are not as important in low-news-reaction countries, then we should seelarger price run-ups ahead of mergers as insiders trade on their knowledge ofthe impending acquisition. Consistent with this prediction, markets in whichstock prices have little response to earnings news have nearly three timesmore pre-announcement news leakage than markets where earnings news isimportant. Second, in countries where non-public information channels aremore important, we expect relatively more informed trading on days withoutpublic news. In countries with high average earnings announcement reactions(where prices move more with public news events), we find evidence thatreversals concentrate around non-news days, similar to prior studies using onlyU.S. data. However, in markets where prices respond little to news, we findthat non-news days have smaller reversals. This evidence is consistent withrelatively more informed trading and proportionally less liquidity trading inlow-reaction countries than in high-reaction countries.

This study is related to a number of studies that examine the role of themedia in financial markets.Roll (1988) shows that stocks exhibit similarlylow market-modelR2 on days with and without news and finds that residualvolatility is only slightly higher on news days.Chan(2003) andTetlock(2010)find that stock returns reverse only when the initial price move occurs whenthere is no news about the stock.Tetlock(2007) andTetlock, Saar-Tsechansky,and Macskassy(2008) find that the market rapidly incorporates most of theinformation associated with the linguistic content of news articles.1Huberman

1 Busheeet al. (2010) find that the initiation of media coverage leads to more trading and larger absolute stockreturn movements for NASDAQ stocks around earnings announcements.Bhattacharya et al.(2009) show thatdifferences in media coverage did not drive differences in Internet stock valuations during the NASDAQ bubble.Fang and Peress(2009) find that stocks with no news earn higher abnormal returns.

3943


ovember 15, 2011


ownloaded from



andRegev(2001) andTetlock (2011) provide evidence that even stale newsmoves prices. A common theme in this U.S.-based literature is that news, evenstale news, can drive stock price movements.

Much less is known about news for stocks in international markets.Bhattacharya et al.(2000) examine 75 hand-collected news announcementdates in Mexico from 1994 to 1997 and find no stock price reaction in the newsannouncement window. In addition to a number of individual country studiesof earnings announcements,DeFond, Hung, and Trezevant(2007) examineearnings reaction differences across 26 primarily developed markets. However,their test design, focus, and methodologies are quite distinct from those weemploy.2 Bailey, Karolyi, and Salva(2006) look at earnings event reactionsbefore and after a U.S. cross-listing and find that U.S. accounting requirementslead to increases in earnings announcement reactions.

Our article also fits into a large literature that seeks to understand howemerging and developed markets differ in terms of various aspects of effi-ciency. Despite a large international literature, there is relatively little under-standing of differences in the informational environment across countries, andfew studies that attempt to quantify the importance of public news interna-tionally.3,4 Griffin, Kelly, and Nardari(2010) find that it is not feasible tomake statements of relative market efficiency internationally using traditionalmeasures unless one can control for the information environment. Our findingsquantify one crucial aspect of the information environment and suggest that themagnitude of news response can be used to identify the presence of informedtrading.

Our article also relates to a growing literature that seeks to documenttrading on inside information in the United States.Griffin, Shu, and Topaloglu(2011) suggest that evidence of trading on information leaked from investmentbankers to their clients prior to takeovers and earnings announcements in theUnited States is less prevalent than is suggested by prior academic work andthe U.S. financial media. Our findings suggest that insider trading prior totakeovers is much more prevalent in emerging and some smaller developedmarkets, where stocks respond little to earnings news.

2 Thereare three important differences with our study. First, we find that I/B/E/S earnings announcements dates,such as those used byDeFond, Hung, and Trezevant(2007), are on average accurate only 23% of the time indeveloped non-U.S. markets. Second, they examine stock price reactions in 23 developed markets with onlythree emerging markets. Third, they examine earnings reactions only, whereas we also focus on general newsevents, takeovers, and return reversals.

3 Using international microstructure data,Lia, Ng, and Zhang(2009) find small differences in the probability ofinformed trading between developed and emerging markets.

4 Gagnonand Karolyi(2009) examine the relation between reversals and continuation (associated with liquidityand informed trading) and trading volume around the world.Lang, Lins, and Miller(2003) find that cross-listingimproves a firm’s information environment as proxied for by analyst coverage and accuracy, andBae, Bailey,and Mao(2006) find that analyst coverage increases following liberalization. The enforcement of insider tradinglaws has been linked to a lower cost of capital (Bhattacharya and Daouk 2002) and higher idiosyncratic volatilityin developed markets (Fernandes and Ferreira 2009).

3944


ovember 15, 2011


ownloaded from



Section1 develops our hypotheses. Section2 describes the data sources.Section3 displays our event reactions for earnings events, and Section4reports results for general news. In Section5, we test our hypotheses withcross-country data, while Section6 provides additional tests related to insidertrading. Section7 concludes.

1. Hypothesis Development

We test four hypotheses for why the price response to public news announce-ments might differ across countries. We believe that differences in marketreactions across countries may be driven by variation in 1) pre-announcementpublic news dissemination; 2) insider trading; 3) the quality of the newstransmission mechanism; and 4) accounting quality.

1.1 Public information disseminationPublic news channels provide substantial information about changes in firmvalue. An important channel is cross-firm learning through peer firms’ earningsannouncements (Foster 1981; Freeman and Tse 1992). Givoly and Palmon(1982) find that late-reporting firms have dampened stock price reactions,because investors learn about their earnings from earlier-reporting firms’earnings announcements.Hou (2007) documents important lead-lag patternsin intra-industry returns, andPatton and Verardo(2010) show that the order ofearnings announcements affects firm betas. In an international sample of firms,if early- or late-reporting firms concentrate in certain countries, then this mayaffect those countries’ average market reaction to earnings news.

Hypothesis H1:Countries with firms reporting late in the earnings report-ing cycle will have lower stock price responses to news.

To capture the importance of the earnings reporting cycle, we compute eachfirm’s reporting order relative to all other firms in the global industry. We usethis reporting-order variable in firm-level regressions and the country averagein country-level regressions.

1.2 Information dissemination through insider tradingIf insiders trade on private information, prices may incorporate value-relevantinformation prior to its public release. This results in a smaller market responseto official public announcements of firm information.Bhattacharya et al.(2000), for example, conclude that insider trading is the reason the marketdoes not react to news in Mexico. On the other hand, by the start of our sampleperiod, nearly all countries in our sample have enforced insider trading laws,so there may be little variation in insider trading across countries.

Hypothesis H2A: In countries where insider trading is more prevalent,stock prices move less on announcement dates.

3945


ovember 15, 2011


ownloaded from



We use the response from a survey of executives in each country around theworld to a question asking whether or not insider trading is common in theircountry as our primary measure for the prevalence of insider trading in cross-country analysis.

We consider additional testable hypotheses for insider trading. One testablehypothesis relates to a major standardized event, takeover announcements, thatare difficult to predict with public news. Markets with insider trading shouldhave more stock price run-up prior to the release of any public takeover news.5

Hypothesis H2B: In countries where insider trading is more prevalent,stock prices will increase more rapidly prior to the first takeover announcementdate.

Another implication is that on non-news days, prices will exhibit less reversalin markets where there is greater private informed trading.Chan(2003) andTetlock(2010) have shown that in the United States, reversals are concentratedon non-news days. For markets with relatively low levels of insider trading,such as the United States, we expect non-news-day trading to be predominatelyliquidity trading, so there should be reversals on non-news days. However,we expect there to be relatively more informed trading on non-news days inmarkets with high levels of private informed trading, which should result insmaller non-news-day reversals.

Hypothesis H2C:If cross-country differences in event reactions are relatedto informed trading, we expect little difference in reversals between news andnon-news days in markets with low event reactions.

We test these predictions both by examining reversals following sorts onextreme return events and for all return moves through cross-sectionalregressions.

1.3 News transmissionThe quality of news gathering and transmission may differ across countries.More sophisticated journalists may be better at focusing on value-relevantnews. In contrast, if most news is simply a transmission of company pressreleases with little filtering or the quality of journalists around the world issimilar, stock price reactions to news may not be affected by differences in thequality of the journalism.

Hypothesis H3A: Prices respond to news more in countries with higher-quality journalism.

5 Bris (2005)examines general patterns of stock price run-ups prior to takeovers before and after the implemen-tation of insider trading laws.

3946


ovember 15, 2011


ownloaded from



Direct measures of journalism quality are difficult to observe, but we inves-tigate several alternatives. First, as a firm-level proxy, we use an indicator ofwhether the news was from a reputable international business news source:TheWall Street Journal, Financial Times, Dow Jones Newswires, and Reuters.6

Second,in markets with more sophisticated journalists, journalists may focustheir attention on a firm when particularly value-relevant information is beingreleased. Hence, as a proxy for the financial sophistication of the news, weuse the ratio of the number of articles in the earnings-event window relativeto the number of articles in the pre-event window.7 Third, as another proxyfor journalist sophistication, we count the number of articles with more than500 words per firm mentioned as a measure of in-depth coverage. Fourth, itis possible that in countries where there is greater press freedom, it is saferfor journalists to root out information and report it accurately. We examinecountry-level press freedom as a general proxy for journalism quality.

A related hypothesis is the means of news dissemination. News is likelydistributed faster and to a broader investor base through Internet sourcesin countries that are more technologically sophisticated, with the result thatinformation is more rapidly incorporated into prices.

Hypothesis H3B: News has a larger impact on prices in countries withgreater technological development.

1.4 Accounting qualityPrices will be more responsive to news when the content of the news hasgreater economic meaning. For earnings-related news, valuation signals areclearer when there are stronger accounting standards and better financialdisclosure because the income statements are more trustworthy. This leads toour final hypothesis:

Hypothesis H4:In countries with higher accounting quality, prices respondmore to earnings news.

We use two measures to measure accounting quality. The first is the percentageof firms that follow U.S. Generally Accepted Accounting Principles (GAAP)or International Financial Reporting Standards (IFRS). Because accountingstandards can be more stringently or less stringently applied even amongcountries with firms following IFRS, we also use a survey-based measure fromthe Global Competitiveness Report, “Strength of Accounting Standards.”

6 Dyck, Volchkova, and Zingales(2008) find that the foreign media can play a disciplining role in Russia becausefirms care about their reputation abroad.

7 We thank an anonymous referee for these helpful ideas. This variable also might be thought of more broadly asa measure of news importance.

3947


ovember 15, 2011


ownloaded from



We examine these hypotheses first in cross-country regressions, but we alsouse firm-level data and regressions to check our inferences with firm-levelcontrols.

2. Data

The main data in this article consist of firm stock returns, news articles,earnings and takeover announcements, and country-level descriptive variables.

2.1 Preliminaries: Sample and returnsDaily returns, accounting for dividends and capital structure changes, andmarket capitalizations are from CRSP for the United States and from ThomsonFinancial’s Datastream for the rest of the world. Since we wish to restrict oursample to common equity, for the United States we use stocks with a CRSPshare code of 10 or 11. For non-U.S. securities, we follow the substantialscreens ofGriffin, Kelly, and Nardari (2010), which eliminate preferredstock, warrants, unit or investment trusts, duplicates, GDRs or cross-listings,and other non-common equity. Because we desire to capture the effects ofinformation release on stock prices, we require stocks to be actively traded;all stocks are required to have price changes on at least 50% of the tradingdays in the prior year. This way we avoid capturing returns that largely reflectstale information. This measure of liquidity, the percentage of zero returndays in a year, is the main measure of liquidity used byBekaert, Harvey, andLundblad(2007) and is similar to theLesmond et al.(1999) andLesmond(2005) transaction costs measure, but less subject to estimation problems.

2.2 News articlesWe collect a sample of news articles from the Dow Jones Factiva news archive.Factiva contains articles from over 28,000 sources published in 159 countriesand 23 languages.8 Thedatabase includes major business news publishers suchas The Wall Street Journal, Financial Times, Dow Jones, and Reuters. Weidentify news for a firm using full-text searches for the firm’s name in theheadline or lead paragraph of articles. The computational burden of the webretrieval process is quite intense, so we collect news for a random sample of5,875 firms over three windows from January 2003 to June 2009.9 Beforenewsand liquidity filters, we initially structure the developed market sample to haveapproximately 50 firms from all countries other than the United States.

8 http://www.factiva.com/sources.asp.For our dataset the news is from over 5,000 local and foreign sources.

9 First,we downloaded news from January 2003 through August 2008 for 4,365 firms from emerging markets andnon-OECD developed markets. We over-sample emerging markets, because emerging market firms receive lessmedia coverage and we want to ensure that we have a large sample with earnings announcements covered by themedia. Second, because U.S. firms represent such a large fraction of firms in the world, we downloaded newsfrom January 2004 through December 2008 for 500 U.S. firms. Third, we downloaded news from January 2004through June 2009 for 1,173 firms in countries on the MSCI’s list of developed markets in 2001.

3948


ovember 15, 2011


ownloaded from

http://www.factiva.com/sources.asp.



Table1 reports summary statistics for this sample of news. For a firm yearto be included, in addition to the 50% price change filter discussed above, werequire that a firm has at least one news article during a given year. However,to ensure a sufficient number of non-news days, no more than 75% of thetrading days during the year can have news articles. The sample consists of2,593 firms with 1,342 firms from 26 developed markets and 1,251 from 30emerging markets. This results in 572,987 news articles for developed marketstocks and 298,614 articles for emerging market stocks from 4,202 sources.This compares favorably to other firm-level news samples such asTetlock,Saar-Tsechansky, and Macskassy(2008), whose sample contains over 350,000articles about S&P 500 firms from the Dow Jones News Service andThe WallStreet Journal(also collected from Factiva). We have an average of 427 articlesper firm in developed markets and 239 in emerging markets, which translatesto at least one news article appearing for each firm on 15.8% of trading daysin developed markets and 12.2% of days in emerging markets.

2.3 Earnings announcement datesWithout accurate earnings announcement event dates, any analysis of marketreactions is flawed. For this reason, we spend considerable effort investigatingmethods for obtaining accurate earnings announcement dates.

We use an automated event checking procedure. We start with Bloombergannouncement dates because we find they are more than twice as accurateas I/B/E/S dates.10 The procedure then isolates a subset of events for whichthe date corresponds to the first release of earnings news in Factiva. Theprocedure isolates a subset of events for which the date from Bloombergcorresponds to the first release of earnings news in Factiva. We verify that eachBloomberg event date corresponds to an article announcing earnings in Factivaand exclude events where earnings news is released prior to the Bloombergannouncement date. Because of Factiva download constraints and the newsscreening restrictions, the number of events that pass our Factiva date-checkingprocedure is considerably smaller than the total Bloomberg announcements ineach country shown in the Online Appendix Table IA.2.11 Our sample rangesfrom January 2004 to September 2008 and is obviously tilted toward firms withmore financial media coverage. The automated procedure leads to earnings

10 Throughoutthe article, we refer to results that one might refer to as “in unreported results” by referring to theirposition in an Online Appendix. Viewing these tables and figures is not required for reading the article but theyare posted for completeness. In Online Appendix Table IA.3, we find that I/B/E/S dates have a confirming articlewithin a [−1, 1] day window around the date listed by I/B/E/S only 23% of the time in developed markets and8.4% in emerging markets.

11 In addition, differences in news checking accuracy across countries create differences in the percentage of eventsin a country that pass our filters. For example, as shown in Online Appendix Table IA.2, China starts out withmore firms than Malaysia, but Malaysia ends up with more events in the final sample because a higher percentageof the Malaysian events pass the accuracy filters. We aggregate most of our data to the country level to put equalweight on inferences in each market.

3949


ovember 15, 2011


ownloaded from



T abl

e1

New

ssu

mm

ary s

tatis

tics

De v

elop

edTo

talfi

rms

Tota

lart

icle

sA

vg.%

Eng

.A

vg.%

Bus

.A

vg.%

WC

<25

0E

mer

ging

Tota

lfirm

sTo

tala

rtic

les

Avg

.%E

ng.

Avg

.%B

us.

Avg

.%W

C<

250

Aus

tral

ia20

6,15

598

.02.

657

.3A

rgen

tina

5121

,60

525

.211

.967

.1A

ustr

ia20

20,40

628

.141

.453

.9B

razi

l19

3,34

176

.539

.972

.8B

elgi

um19

13,16

320

.811

.859

.2B

ulg a

ria8

2,55

298

.21.

740

.4C

anad

a8

2,17

899

.77.

142

.2C

hile

2510

,55

451

.215

.469

.4C

ypru

s17

2,08

694

.015

.576

.9C

hina

231

31,25

596

.93.

736

.1D

enm

ark

2613

,19

828

.120

.659

.5C

olom

bia

209,

444

35.4

28.3

69.4

Fin

land

2514

,06

454

.916

.166

.0C

roat

ia2

765

82.8

21.0

59.5

Fra

nce

134,

685

28.3

15.0

64.9

Cze

chR

epub

lic9

9,39

560

.826

.369

.2G

erm

any

1315

,87

849

.012

.042

.9E

gypt

302,

795

96.3

14.5

81.0

Gre

ece

435,

496

92.8

13.2

45.0

Est

onia

131,

376

64.2

0.8

65.1

Hon

gK

ong

188

22,94

492

.919

.637

.7H

unga

ry28

12,44

266

.414

.465

.9Ir

elan

d8

4,57

396

.426

.145

.8In

dia

138

57,40

499

.57.

139

.7Is

rael

364,

684

92.8

8.4

51.2

Indo

nesi

a28

4,94

996

.512

.262

.2Ita

ly27

34,86

419

.916

.559

.1K

enya

255,

633

96.2

5.2

25.9

Japa

n39

7,93

286

.856

.179

.4La

tvia

83,

847

62.2

1.2

42.8

Net

herla

nds

2021

,70

224

.740

.765

.5Li

thua

nia

3113

,10

552

.61.

158

.0N

ewZ

eala

nd23

13,11

999

.68.

649

.1M

alay

sia

154

21,20

293

.613

.561

.9N

orw

ay23

12,16

345

.439

.265

.7M

e xic

o19

8,06

742

.329

.358

.3P

ortu

gal

1312

,94

026

.630

.660

.4M

oroc

co21

1,27

457

.226

.273

.4S

inga

pore

114

23,66

993

.016

.434

.3Pa

kist

an73

7,64

698

.27.

462

.6S

pain

2553

,05

221

.615

.471

.8P

eru

224,

010

47.9

23.0

68.9

Sw

eden

3016

,66

735

.125

.461

.5P

hilip

pine

s29

6,24

698

.418

.433

.6S

witz

erla

nd17

13,22

511

.211

.759

.8P

olan

d48

17,96

333

.15.

055

.1Ta

iwan

217

46,70

093

.649

.860

.1R

oman

ia30

1,60

091

.54.

365

.9U

.K.

173,

695

97.8

26.9

47.9

Slo

vaki

a2

1,03

267

.22.

064

.2U

.S.

341

183,

749

96.1

20.7

43.5

Slo

veni

a11

1,35

488

.57.

669

.8S

outh

Afr

ica

4611

,89

896

.912

.948

.9T

haila

nd67

11,76

097

.65.

734

.7T

urke

y58

10,82

253

.114

.179

.1V

enez

uela

53,

278

28.5

46.2

71.5

Tota

l1,

342

572,

987

1,25

129

8,61

4M

ean

51.6

22,0

38.0

62.6

21.8

56.2

41.7

9,95

3.8

71.8

14.0

59.1

Dev

.-E

mg.

−9.

27.

8−

2.9

(t-s

tat.)

(−1.

14)

(2.2

8)(−

0.83

)

We

iden

tify

firm

new

sus

ing

full-

text

sear

ches

for

the

firm

’sna

me

inth

ehe

adlin

eor

lead

para

grap

hof

artic

les

inF

activ

a.F

irmna

mes

are

the

Dat

astr

eam

expa

nded

nam

efie

ldw

ithco

mm

onch

arac

ter

strin

gs(A

,DE

AD

,CO

,SA

,etc

.)re

mov

ed.T

hesa

mpl

eis

cons

truc

ted

asdi

scus

sed

inS

ectio

n2

and

foot

note

9.“A

vg.%

Eng

.”is

the

aver

age

perc

enta

geof

artic

les

ina

firm

year

writ

ten

inE

nglis

h.“A

vg.%

Bus

.”is

the

aver

age

perc

ento

fart

icle

sin

afir

mye

arth

atar

epu

blis

hed

byD

owJo

nes,

Reu

ters

,the

Fin

an

cia

lTim

es,

orT

he

Wa

llS

tre

etJ

ou

rna

l.“

Avg

.%W

C<

250”

isth

eav

erag

epe

rcen

tofa

rtic

les

ina

firm

year

with

less

than

250

wor

ds.A

coun

try

iscl

assi

fied

asde

velo

ped

ifth

egr

oss

natio

nali

ncom

epe

rca

pita

isgr

eate

rth

an10

,725

U.S

.dol

lars

inca

lend

arye

ar20

05,f

ollo

win

gth

eW

orld

Ban

k’s

clas

sific

atio

nfo

r“h

igh

inco

me”

coun

trie

s.A

llot

her

coun

trie

sar

ecl

assi

fied

asem

ergi

ng.O

EC

Dm

embe

rshi

pis

from

cale

ndar

year

2005

.

3950


ovember 15, 2011


ownloaded from



dateswith considerable accuracy (as discussed in Online Appendix A andTable IA.5).

Panel A of Table2 shows the number of events by country. Our final sampleof earnings announcements contains 3,504 events from 26 developed marketsand 2,079 events from 30 emerging markets. Despite the focus on accuracy, thesample seems to have broad coverage across markets. Panel B of Table2 showsthe number of articles around earnings announcements for each of three sizebins. Each December we sort all U.S. stocks listed on NYSE/AMEX/NASDAQinto equal portfolios based on dollar-denominated size breakpoints. In thesmall- and large-size bins, developed markets have slightly more news articlesin the non-earnings announcement window. However, in the announcementwindow the news discrepancy is large, with developed markets exhibitingroughly one more news article per day for small and medium firms, and over2.6 more articles per day in the large-size bin.

2.4 Takeover announcement datesThe sample of initial merger announcements is composed of the earliest datefrom among three sources: SDC, FactSet, and Bloomberg.12 Additionally, wecheck our dates by making sure there is no article in Factiva with both thefirm’s name and a merger keyword in the headline or lead paragraph from 60to two calendar days before the merger. For the 22 languages we check articlesin, we end up with over 1,500 translated merger words, as discussed in OnlineAppendix IA.2. Panel A of Table2 shows that we obtain 466 merger eventsfrom 23 developed markets and 105 events from 11 emerging markets.

2.5 Country-level variablesWe use country-level variables from many sources that have been used in theprior international literature. We describe these variables in detail in AppendixTable A1. Major sources include the World Bank Development Database andthe World Economic Forum’s Global Competitiveness Report (GCR). TheGCR reports the results of a survey of over 4,000 executives in 59 countries.When available, we average annual levels over 2003 through 2008 to reduceyear-to-year noise in these variables.13

3. Earnings Announcements and Stock Returns across Countries

We wish to quantify the relevance of information disseminated on newsdays. Earnings announcements are advantageous in that they are a common

12 We require targets to have at least one article in the news from 60 calendar days to two trading days before theevent to ensure that the lack of an article discussing rumored mergers is not due to errors that prevented us fromdownloading any news for the firm.

13 Onenotable exception is a GCR survey question that asks executives if “insider trading in your country’s stockmarkets is (1=pervasive, 7=extremely rare).” We average the responses in the editions from 1999, 2000, and2002–2003, because the question was not surveyed in later years.

3951


ovember 15, 2011


ownloaded from



Tabl

e2

Ear

ning

san

dm

erge

ran

noun

cem

ents

sum

mar

yst

atis

tics

Pan

elA

:Firm

and

Eve

ntCou

nts

Dev

elop

edC

ount

ries

Ear

ning

sF

irms

Ear

ning

sE

vent

sM

erge

rE

vent

sE

mer

ging

Cou

ntrie

sE

arni

ngs

Firm

sE

arni

ngs

Eve

nts

Mer

ger

Ev

ents

Aus

tral

ia25

3958

Arg

entin

a29

66−

Aus

tria

2648

−B

razi

l20

34−

Bel

gium

4810

03

Chi

le30

63−

Can

ada

3460

125

Chi

na16

727

0−

Den

mar

k45

838

Col

ombi

a4

4−

Fin

land

5710

64

Cro

atia

13

−F

ranc

e44

7812

Cze

chR

epub

lic1

1−

Ger

man

y21

3724

Egy

pt15

182

Gre

ece

3350

17E

ston

ia4

4−

Hon

gK

ong

319

516

37H

unga

ry19

37−

Irel

and

2344

1In

dia

224

307

33Is

rael

3448

2In

done

sia

4476

1Ita

ly64

129

12K

enya

89

−Ja

pan

105

250

−La

tvia

23

−N

ethe

rland

s65

130

8Li

thua

nia

33

−N

ewZ

eala

nd41

993

Mal

aysi

a41

470

923

Nor

way

4277

11M

exic

o22

47−

Por

tug a

l29

62−

Mor

occo

22

1S

inga

pore

188

325

16Pa

kist

an4

41

Sou

thK

orea

7410

023

Per

u5

12−

Spa

in55

105

1P

hilip

pine

s9

15−

Sw

eden

4877

18P

olan

d89

133

2S

witz

erla

nd47

813

Rom

ania

77

1Ta

iwan

290

371

49S

lova

kia

12

−U

.K.

3674

8S

love

nia

11

−U

.S.

210

415

23S

outh

Afr

ica

4868

21S

riLa

nka

11

−T

haila

nd62

100

14T

urke

y62

766

Ven

ezue

la2

4−

Tota

l2,

003

3,50

446

6To

tal

1,30

02,

079

105

Ave

rage

7713

520

Ave

rage

4369

10

(co

ntin

ue

d)

3952


ovember 15, 2011


ownloaded from



Tabl

e2

Con

tinue

d

Pan

elB

:Ave

rage

Dai

lyN

ews

Cov e

rage

Dev

elop

ed[−

55, −

2][ −

1,2]

Em

ergi

ng[ −

55, −

2][ −

1,2]

SM

LS

ML

SM

LS

ML

Aus

tral

ia0.

320.

452.

752.

284.

6413

.03

Arg

entin

a0.

180.

450.

780.

822.

563.

54A

ustr

ia0.

672.

142.

432.

388.

389.

33B

razi

l0.

000.

430.

370.

501.

791.

24B

elgi

um0.

440.

512.

532.

963.

407.

29C

hile

0.50

0.39

1.22

1.54

1.94

2.97

Can

ada

0.42

0.28

1.96

2.00

1.85

8.13

Chi

na0.

080.

280.

720.

581.

683.

59D

enm

ark

0.36

0.48

1.77

3.07

3.73

10.8

2H

ung a

ry0.

330.

600.

941.

442.

752.

71F

inla

nd0.

220.

341.

421.

151.

484.

20In

dia

0.34

0.58

1.45

1.25

2.06

3.86

Fra

nce

0.21

0.54

1.04

0.86

1.66

4.45

Indo

nesi

a0.

060.

220.

690.

500.

812.

13G

erm

any

0.65

0.79

2.51

1.07

4.22

9.86

Mal

aysi

a0.

060.

170.

490.

520.

972.

09G

reec

e0.

030.

030.

530.

340.

321.

40M

exic

o0.

131.

391.

110.

253.

794.

16H

ong

Kon

g0.

070.

140.

530.

561.

314.

24P

olan

d0.

330.

541.

540.

992.

003.

69Ir

elan

d0.

010.

680.

990.

551.

804.

71S

outh

Afr

ica

0.15

0.11

0.58

0.79

0.88

2.43

Isra

el0.

110.

250.

300.

710.

961.

75T

haila

nd0.

180.

351.

110.

701.

362.

79Ita

ly1.

051.

621.

373.

344.

054.

15T

urke

y0.

080.

621.

050.

471.

053.

11Ja

pan

0.12

0.13

0.32

0.82

1.04

1.53

Oth

erE

mer

g.0.

260.

630.

821.

091.

552.

56N

ethe

rland

s0.

280.

751.

542.

144.

677.

85N

ewZ

eala

nd0.

370.

551.

592.

193.

355.

57N

orw

ay0.

410.

721.

393.

002.

904.

97P

ortu

gal

0.42

0.75

1.13

3.61

2.59

3.39

Sin

gapo

re0.

110.

260.

940.

891.

604.

01S

outh

Kor

ea0.

040.

070.

450.

940.

381.

38S

pain

0.39

0.65

2.24

1.56

2.13

6.38

Sw

eden

0.34

0.79

1.57

2.33

4.20

7.10

Sw

itzer

land

0.11

0.76

1.37

2.15

5.93

7.29

Taiw

an0.

140.

180.

680.

810.

962.

26U

.K.

0.85

1.30

0.88

2.95

4.53

5.52

U.S

.0.

220.

330.

701.

101.

793.

45

(co

ntin

ue

d)

3953


ovember 15, 2011


ownloaded from



Tabl

e2

Con

tinue

d

Pan

elB

:Ave

rage

Dai

lyN

ews

Cove

rage

Dev

elop

ed[ −

55, −

2][ −

1,2]

Em

ergi

ng[ −

55, −

2][ −

1,2]

SM

LS

ML

SM

LS

ML

Avg

.0.

320.

601.

341.

762.

845.

540.

190.

480.

920.

821.

802.

92Lg

-S

m1.

023.

780.

732.

10( t

-sta

t.)(8

.17)

(7.0

5)(1

0.25

)(9

.20)

Dev

.-E

mg.

0.13

0.11

0.42

0.94

1.04

2.62

(t-s

tat.)

(2.0

7)(0

.89)

(2.4

9)(4

.24)

(2.4

1)(4

.12)

Dev

.E

mg.

Dev

.–E

mg.

SM

LS

ML

SM

L

[-1,

2]-[

-55,

-2]

1.44

2.24

4.20

0.63

1.32

2.00

0.81

0.93

2.20

(t-s

tat.)

(8.7

7)(7

.51)

(8.7

6)(8

.68)

(8.1

7)(1

2.44

)(4

.53)

(2.7

3)(4

.35)

Pane

lAre

port

sfir

man

dev

entc

ount

s.T

heea

rnin

gsan

noun

cem

ents

ampl

efo

rK

orea

cont

ains

even

tsfr

omF

ebru

ary

2001

toJa

nuar

y20

08.T

heea

rnin

gsan

noun

cem

ents

ampl

efo

ral

loth

erco

untr

ies

cont

ains

even

tsfr

omJa

nuar

y20

04to

Mar

ch20

08.T

hem

erge

ran

noun

cem

ents

ampl

eco

ntai

nsev

ents

from

Janu

ary

2001

thro

ugh

Apr

il20

07.E

arni

ngs

anno

unce

men

tsar

eda

tes

from

Blo

ombe

rgth

atpa

ssed

anau

tom

ated

date

-che

ckin

gpr

oces

s.T

hepr

oces

sco

nsid

ers

anea

rnin

gsan

noun

cem

ent

date

from

Blo

ombe

rgac

cura

teif

itis

with

in+

/−

3da

ysof

aF

activ

aar

ticle

that

mee

tsth

efo

llow

ing

crite

ria:

1)th

efir

m’s

nam

eis

inth

ehe

adlin

eor

lead

para

grap

h;2)

the

artic

leis

tagg

edby

Fac

tiva

asea

rnin

gsne

ws

(tag

c151

);3)

the

head

line

cont

ains

ach

arac

ter

strin

gin

dica

tive

ofan

earn

ings

anno

unce

men

tart

icle

;and

4)th

ere

isno

artic

lem

eetin

gcr

iteria

1–3

inth

epr

ior

60da

ys.S

eeO

nlin

eA

ppen

dix

Afo

rm

ore

deta

ilson

the

auto

mat

edch

ecki

ngpr

oced

ure.

The

sam

ple

isth

eun

ion

ofth

esu

bset

ofF

activ

aco

nfirm

edea

rnin

gsan

noun

cem

ents

with

retu

rns

that

pass

our

filte

rsan

da

set

ofha

nd-c

heck

edK

orea

nda

tes.

The

sam

ple

ofm

erge

ran

noun

cem

ents

isco

mpo

sed

ofth

eea

rlies

tda

tefr

omon

eof

thre

eso

urce

s:S

DC

,F

actS

et,

and

Blo

ombe

rg.

We

rest

rict

the

sam

ple

toin

itial

bids

(no

bids

for

the

targ

etin

the

prio

rtw

oye

ars)

and

mer

gers

whe

reth

eta

rget

has

atle

ast

one

artic

lew

ithth

efir

m’s

nam

ein

the

head

line

orle

adpa

ragr

aph

betw

een

60ca

lend

arda

ysan

dtw

otr

adin

gda

yspr

ior

toth

ean

noun

cem

ent

and

noar

ticle

with

am

erge

rke

ywor

din

the

sam

etim

efr

ame.

The

lang

uage

-spe

cific

mer

ger

keyw

ords

are

Goo

gle

tran

slat

ions

ofac

quire

,ac

quire

s,ac

quis

ition

,bi

d,bi

ds,

buyo

ut,

deal

,m

erge

,m

erge

r,se

ll,ta

keov

er,

and

talk

s.S

peci

alch

arac

ter

sets

prev

ente

dus

from

tran

slat

ing

Chi

nese

,Ja

pane

se,

and

Rus

sian

mer

ger

keyw

ords

,so

we

excl

ude

mer

gers

from

Chi

na,

Japa

n,an

dR

ussi

a.F

orm

ost

lang

uage

s,w

est

emw

ords

usin

gth

eS

now

ball

algo

rithm

s(

http

://sn

owba

ll.ta

rtar

us.o

rg)an

dco

mpa

rero

otw

ords

rath

erth

anth

een

tire

char

acte

rst

ring.

The

rew

ere

nost

emm

ers

avai

labl

efo

rIta

lian,

Hun

garia

n,P

olis

h,T

urki

sh,

orS

lova

k,so

for

thes

ew

ese

arch

edar

ticle

sfo

rth

efu

lltr

ansl

ated

mer

ger

keyw

ords

.P

anel

Bre

port

sth

eav

erag

enu

mbe

rof

artic

les

per

day

for

afir

mdu

ring

[−

55,−

2]an

d[−

1,2]

trad

ing-

day

win

dow

sar

ound

earn

ings

anno

unce

men

ts.T

heS

,M

,an

dL

grou

ping

sst

and

for

smal

l-,m

ediu

m-,

and

larg

e-si

zepo

rtfo

lios

form

edus

ing

prio

r-D

ecem

ber

NY

SE

/NA

SD

AQ

/AM

EX

brea

kpoi

nts.

InP

anel

B,c

ount

ries

with

few

erth

an20

firm

-yea

rob

serv

atio

nsar

egr

oupe

din

toth

eca

tego

ry“O

ther

Em

erg.

”be

fore

aver

agin

g.

3954


ovember 15, 2011


ownloaded from

http://snowball.tartarus.org



event across countries. Here, we first outline our methodology for examiningearnings-event reactions.

3.1 PreliminariesAlthough unexpected positive earnings news is typically accompanied by apositive stock price reaction, the market may form expectations differentlyacross markets. Our concern is whether or not information release is con-centrated around news events, and not whether the information is positive ornegative. Hence, we focus our analysis on volatility around the announcement.We take the absolute value of a stock’s return in excess of its local value-weighted market return as our measure of abnormal volatility.14

Normalizedvolatility is the average abnormal volatility during the eventwindow divided by the average abnormal volatility during the 55 days beforeand 55 days after the event minus one. It has intuitive appeal in that it measuresabsolute event returns in proportion to absolute returns outside the earningswindow. If the two periods are equivalent, the ratio will take a value ofzero. Differenced volatility is the average abnormal event volatility minus theaverage abnormal volatility in the±55 days around the event. To ensure thatour country-level findings are not driven by imprecise measurement, for mostof our analysis we include countries with at least 20 events, leaving us with 13emerging and 26 developed markets with more than 20 events. We group the17 emerging markets with fewer than 20 earnings announcement events intoan “Other Emerging” category.

3.2 Event reaction resultsPanel A of Figure1 ranks all of the countries from highest to lowest basedon the event volatility ratio. Statistical significance (denoted by stripes) isdetermined with a non-parametrict-test at the five-percent level.

First, the figure highlights how event reactions vary widely around theworld. Denmark, the U.K., Sweden, the Netherlands, the United States,Finland, Hong Kong, and Germany have event reactions over 0.5, meaningthat volatility during the four-day event window is 50% greater than normalstock volatility. Thailand, Turkey, Mexico, Indonesia, Poland, Argentina,and China exhibit event reactions less than 0.15, indicating that volatilityon earnings announcement days is similar to volatility on days with noearnings news. Second, developed markets in black are typically on theleft side of the graph with much higher event reactions, while emergingmarkets in gray are to the right with low event reactions. The top 18 eventreactions are developed markets. These markets are followed by some ofthe more established emerging markets, India, South Africa, Brazil, andChile, and another group of developed markets, Taiwan, Portugal, Austria,

14 For individual stocksGriffin (2002) shows that bench marking with a local factor model yields more accurateexpected return estimates than a global or international model.

3955


ovember 15, 2011


ownloaded from


The Review of Financial Studies / v 24 n 12 2011

Figure 1(Continued)

3956


ovember 15, 2011


ownloaded from


How Important Is the Financial Media in Global Markets?

Figure 1Earnings announcement reactionsThis figure plots three types of earnings announcement reaction volatility: Normalized Volatility, DifferencedVolatility, and Residual Differenced Volatility. Normalized Volatility in Panel A and Differenced Volatility inPanel B are calculated as in Table3. Panel C aggregates the firmi and yeart residual reactions from regressionsweighted by the reciprocal of the number of events in the country, so as not to over-weight countries with morefirms:

V olEventi,t − V olNormal

i,t = α + I ndustryi + β1ln(Prior Dec.U SD MVi,t

)

+β2Prior Y ear Pct. Price Tradei,t + β3Accuracycountry

+Prior News Coveragei,t .

V olEventi,t is the mean absolute abnormal return over the [-1, 2] event window relative to the earnings

announcement date.V olNormali,t is the mean absolute abnormal return during the [-55, -2] and [3, 55] windows

calculated as described in Section3.1.Industryis a dummy variable for the firm’s Fama and French 17 industry.Prior Dec. USD MVis the prior December-end market value in U.S. dollars.Prior Year Pct. Price Tradeisthe percentage of trading days with non-zero price changes during the prior calendar year.Accuracyfor eachcountry is reported in Online Appendix Table IA.5.Prior News Coverageis the number of Factiva articles in the[-55, -2] window. In Panels A and B, countries with event volatility significantly greater than normal volatilityas indicated by aCorrado(1989) non-parametric rankt-test are denoted by striped bars. Countries with fewerthan 20 firm-year observations are grouped into the category “Other Emerg.”

Italy, South Korea, Greece, and Canada. Interestingly, Canada seems outof place relative to its advanced economy and stock market; however,Bhattacharya(2006) argues that security law enforcement is extremely laxin Canada relative to the United States. Third, the bottom 11 countrieswith low event reactions are emerging markets, except for Spain. The

3957


ovember 15, 2011


ownloaded from



non-parametrict-statisticsfor the figure are also reported in the last columns ofTable3 and show that there is no significant difference between event-day andnon-event-day volatility in most of these 11 low-reaction countries. Finally, thelow event reaction in Mexico is remarkably consistent with the findings fromBhattacharya et al.(2000), who find no market reaction to 75 news events inMexico during an earlier time period (1994 to 1997).

One concern is that differences in reactions might be due to differences inthe distribution of firm size across countries. Table3 summarizes normalizedvolatility for firms sorted into three size portfolios. Panel A is for developedmarkets and Panel B for emerging markets. First, there are significant dif-ferences between developed and emerging markets for the two largest sizebins, indicating that firm-size differences are not likely driving the differencesbetween developed and emerging markets. Second, surprisingly, there aretypically relatively small differences in reactions across size portfolios. Third,for robustness we use a standard market model adjustment usingDimson(1979) (infrequent trading corrected) betas. There is little difference betweenthe Dimson market model adjusted volatility ratios and those calculated usinga simple return minus the market. Finally, the difference between developedand emerging markets is relatively large. In developed markets, the averageevent reaction is 0.38, meaning that average daily volatility during the four-dayearnings event window is 38% more volatile than a typical day. In emergingmarkets, event-window volatility is substantially lower—earnings days exhibitonly 15% more absolute return movement than normal days. These findingsindicate that event days have two and a half times more abnormal volatility indeveloped markets.

Since ratios can be inflated by denominators near zero, Panel B ofFigure1 examines the difference between (as opposed to the ratio of) earningsannouncement window absolute excess returns and absolute excess returnsduring normal times. While the precise ordering varies somewhat from PanelA, the general patterns are similar. There are only three emerging marketsamong the top 25 markets, and 11 emerging markets are among the bottom15 markets (counting the “Other Emerging” category as one country). Table3 shows that developed markets exhibit 0.56% more volatility on earningsdays, whereas emerging markets only have an additional 0.23% in excessreturn movement. Conceptually, we prefer the volatility ratio to the volatilitydifference, because the ratio makes for more intuitive comparisons of abnormalvolatility across countries with different levels of idiosyncratic volatility.

The differences in average country reactions could be due to differences infirm characteristics across countries, such as industry differences in the uncer-tainty and importance of earnings. We estimate a cross-sectional regression atthe firm level to remove these effects and plot residuals from these regressionsat the country level in Panel C of Figure1. The ordering changes somewhatfrom Panel A, but is generally similar. Most developed markets are again onthe left side of the graph with much higher volatility during the announcement

3958


ovember 15, 2011


ownloaded from



Tabl

e3

Ear

ning

sev

entr

eact

ions

Pan

elA

:Dev

elop

edM

ark ets

Nor

mal

ized

Vola

tility

Diff

eren

ced

Vola

tility

(%)

Ran

k-B

ased

t -st

at.

Siz

eP

ortfo

lio(M

kt.A

dj.)

Cou

ntry

-leve

lC

ount

ry-le

vel

Cou

ntry

SM

LM

kt.A

dj.

Dim

.Adj

.M

kt.A

dj.

Dim

.Adj

.M

kt.A

dj.

Dim

.A

dj.

Aus

tral

ia0.

360.

470.

570.

430.

460.

820.

89(3

.69)

(3.9

3)A

ustr

ia0.

340.

150.

210.

200.

230.

280.

34(2

.40)

(2.3

2)B

elgi

um0.

320.

680.

350.

490.

540.

580.

62(6

.07)

(6.3

3)C

anad

a0.

120.

330.

250.

180.

140.

390.

32(3

.50)

(2.4

9)D

enm

ark

0.47

0.54

0.77

0.60

0.61

0.71

0.70

(5.9

9)(5

.91)

Fin

land

0.62

0.54

0.40

0.52

0.61

0.73

0.77

(5.5

5)(6

.18)

Fra

nce

0.28

0.34

0.71

0.38

0.40

0.51

0.53

(3.4

5)(3

.59)

Ger

man

y0.

540.

340.

610.

510.

510.

910.

93(4

.26)

(4.3

6)G

reec

e0.

060.

380.

260.

180.

170.

300.

27(2

.07)

(1.6

7)H

ong

Kon

g0.

550.

550.

310.

510.

510.

920.

94(7

.57)

(7.7

4)Ir

elan

d0.

040.

430.

500.

390.

330.

520.

43(3

.44)

(2.9

5)Is

rael

0.20

0.36

0.27

0.31

0.28

0.37

0.35

(2.7

6)(2

.17)

Italy

0.21

0.14

0.40

0.20

0.20

0.25

0.25

(3.7

1)(3

.37)

Japa

n0.

390.

460.

430.

430.

430.

540.

54(5

.37)

(5.3

0)N

ethe

rland

s0.

360.

600.

700.

560.

580.

700.

72(6

.35)

(6.3

1)N

ewZ

eala

nd0.

520.

360.

470.

440.

410.

470.

46(4

.75)

(4.7

9)N

orw

ay0.

590.

300.

290.

410.

400.

690.

68(4

.46)

(4.5

8)P

ortu

gal

0.13

0.24

0.28

0.21

0.31

0.20

0.29

(3.0

3)(3

.82)

Sin

gapo

re0.

390.

370.

240.

360.

330.

560.

53(5

.72)

(5.2

1)S

outh

Kor

ea−

0.03

0.12

0.25

0.19

0.15

0.36

0.29

(3.2

6)(2

.79)

Spa

in−

0.02

0.05

0.29

0.17

0.17

0.18

0.18

(2.9

3)(2

.86)

Sw

eden

0.54

0.51

0.70

0.56

0.58

0.89

0.90

(4.4

7)(4

.78)

Sw

itzer

land

0.45

0.35

0.53

0.41

0.44

0.51

0.52

(4.7

0)(4

.81)

Taiw

an0.

220.

250.

160.

220.

200.

380.

35(3

.00)

(2.7

3)U

.K.

0.43

0.60

0.67

0.57

0.50

0.84

0.77

(5.8

0)(5

.51)

U.S

.0.

390.

530.

770.

530.

510.

850.

84(8

.02)

(7.7

6)

Dev

.Avg

.0.

330.

380.

440.

380.

380.

560.

55(8

.56)

(8.3

9) (co

ntin

ue

d)

3959


ovember 15, 2011


ownloaded from



Tabl

e3

Con

tinue

d

Pan

elB

:Em

ergi

ngM

arket

s

Nor

mal

ized

Vola

tility

Diff

eren

ced

Vola

tility

(%)

Ran

k-B

ased

t -st

at.

Siz

eP

ortfo

lio(M

kt.A

dj.)

Cou

ntry

-leve

lC

ount

ry-le

v el

Cou

ntry

SM

LM

kt.A

dj.

Dim

.Adj

.M

kt.A

dj.

Dim

.Adj

.M

kt.A

dj.

Dim

.A

dj.

Arg

entin

a−

0.01

0.29

−0.

060.

100.

110.

140.

15(0

.74)

(0.9

7)B

razi

l2.

130.

570.

170.

280.

350.

440.

52(3

.35)

(3.4

3)C

hile

−0.

400.

410.

280.

250.

270.

240.

27(2

.62)

(3.0

7)C

hina

0.00

0.13

0.13

0.12

0.10

0.20

0.18

(1.8

6)(1

.82)

Hun

gary

0.24

0.14

−0.

080.

170.

210.

280.

31(1

.05)

(1.5

2)In

dia

0.36

0.31

0.18

0.30

0.29

0.55

0.55

(5.8

2)(5

.44)

Indo

nesi

a0.

170.

06−

0.12

0.04

0.02

0.07

0.03

(1.1

3)(0

.59)

Mal

aysi

a0.

160.

210.

230.

170.

180.

270.

29(4

.06)

(3.8

9)M

exic

o−

0.63

0.04

0.08

0.04

0.11

0.05

0.14

(0.3

2)(1

.32)

Oth

erE

mer

g.0.

130.

140.

310.

180.

110.

260.

17(1

.90)

(1.6

2)P

olan

d0.

080.

120.

030.

090.

100.

170.

19(2

.43)

(2.3

8)

(co

ntin

ue

d)

3960


ovember 15, 2011


ownloaded from



Tabl

e3

Con

tinue

d

Pan

elB

:Em

ergi

ngM

ark et

s

Nor

mal

ized

Vola

tility

Diff

eren

ced

Vola

tility

(%)

Ran

k-B

ased

t-st

at.

Siz

eP

ortfo

lio(M

kt.A

dj.)

Cou

ntry

-leve

lC

ount

ry-le

v el

Cou

ntry

SM

LM

kt.A

dj.

Dim

.Adj

.M

kt.A

dj.

Dim

.Adj

.M

kt.A

dj.

Dim

.A

dj.

Sou

thA

fric

a0.

220.

340.

280.

290.

300.

400.

42(3

.47)

(3.7

5)T

haila

nd0.

06−

0.06

0.10

0.02

0.02

0.03

0.04

(0.7

7)(1

.22)

Tur

key

0.10

0.03

−0.

210.

020.

080.

040.

13(0

.15)

(1.4

4)

Em

g.A

vg.

0.19

0.19

0.09

0.15

0.16

0.23

0.24

(6.2

0)(6

.32)

Dev

.–E

mg.

0.14

0.19

0.34

0.23

0.22

0.33

0.31

( t-s

tat.)

(0.8

2)(3

.40)

(5.9

7)(5

.93)

(5.4

1)(5

.28)

(4.8

8)

Thi

sta

ble

repo

rts

sum

mar

yst

atis

tics

fort

here

actio

nsto

the

earn

ings

even

tsde

scrib

edin

Tabl

e2.

Rea

ctio

nsar

eba

sed

eith

eron

mar

ket-

adju

sted

abno

rmal

retu

rns

(Mkt

.Adj

.)or

Dim

son-

beta

adju

sted

abno

rmal

retu

rns

(Dim

.A

dj.)

.S

tock

retu

rns

are

mea

sure

din

loca

lcur

renc

y.E

xtre

me

one-

day

retu

rns

over

200%

and

retu

rns

that

reve

rtqu

ickl

yw

itha

one-

day

abso

lute

retu

rnov

er10

0%an

da

two-

day

retu

rnof

less

than

20%

are

elim

inat

edto

redu

cepo

tent

iald

ata

erro

rs.

Inor

der

for

anev

ent

tobe

incl

uded

,th

ere

mus

tbe

atle

ast

50no

n-m

issi

ngre

turn

sin

the

perio

d25

0to

126

days

befo

reth

eev

entf

orth

epu

rpos

esof

mar

ketm

odel

estim

atio

n.B

etas

are

calc

ulat

edfo

llow

ing

Dim

son

(197

9),u

sing

thre

ele

ads

and

lags

ofth

eva

lue-

wei

ghte

dlo

cal

mar

ketr

etur

n.T

here

actio

nst

atis

tics

for

each

coun

try

jar

eco

mpo

sed

oftw

opa

rts:

even

tvol

atili

tyan

dno

rmal

vola

tility

.Eve

ntvo

latil

ityis

the

mea

nab

solu

teab

norm

alre

turn

calc

ulat

edas

desc

ribed

inS

ectio

n3.1

over

the

[ −1,

2]ev

entw

indo

wre

lativ

eto

the

earn

ings

anno

unce

men

tdat

e,

Vol

Even

tj

=1 N

N ∑ i=1

1 4

2 ∑ t=−

1

|AR

i,t|

,

whe

rei

isth

eev

ent,t

isth

eev

entd

ay,a

ndNis

the

num

ber

ofev

ents

inea

chm

arke

t.N

orm

alvo

latil

ityis

the

mea

nab

solu

teab

norm

alre

turn

durin

gth

e[

−55

,−2]

and

[3,5

5]w

indo

ws,

Vol

Nor

ma

lj

=1 N

N ∑ i=1

1 107

−

2∑

t=−

55

∣ ∣ AR

i,t∣ ∣ +

55 ∑ t=3

∣ ∣ AR

i,t∣ ∣

,

whe

rei

isth

eev

enta

ndt

isth

eev

entd

ay.N

orm

aliz

edVo

latil

ityis

aver

age

even

tvol

atili

ty,

Vol

Even

tj

, div

ided

byav

erag

eno

rmal

vola

tility

,Vol

Nor

ma

lj

,m

inus

one.

Diff

eren

ced

Vola

tility

isev

entv

olat

ility

min

usno

rmal

vola

tility

.Siz

epo

rtfo

lios

are

base

don

prio

rD

ecem

ber

NY

SE

/AM

EX

/NA

SD

AQ

terc

ilebr

eakp

oint

sfr

om20

00to

2006

.The

last

two

colu

mns

repo

rtno

n-pa

ram

etric

rank

-bas

edt-s

tats

for

each

coun

try

calc

ulat

edfo

llow

ingC

orra

do(1

989)

for

test

sof

the

hypo

thes

isth

atev

ent-

perio

dvo

latil

ityeq

uals

norm

al-p

erio

dvo

latil

ity.

The

aver

age

atth

ebo

ttom

ofea

chpa

neli

sth

eav

erag

eof

even

trea

ctio

nsw

ithin

eith

erde

velo

ped

orem

ergi

ngm

arke

ts.C

ount

ries

with

few

erth

an20

firm

-yea

rob

serv

atio

nsar

egr

oupe

din

toth

eca

tego

ry“O

ther

Em

erg.

”be

fore

aver

agin

g.

3961


ovember 15, 2011


ownloaded from



window. The five highest-reaction countries are Germany, Hong Kong, theUnited States, Sweden, and the U.K., and the five lowest-reaction countriesare Indonesia, Poland, Thailand, Mexico, and Turkey.

We Winsorize firm-level turnover data at the first and 99th percentiles andexamine turnover before and after earnings announcements for the low- andhigh-earnings-reaction countries for robustness. Confirming inferences fromabsolute returns, turnover spikes on earnings announcements in high-earnings-announcement-reaction countries, but the relative increase is much smaller forlow-reaction countries.15

Overall, relative to normal absolute returns, earnings announcement reac-tions show a striking tendency to be two and a half times higher in developedmarkets. The cross-country differences also indicate interesting variation forcross-country analysis.

4. General News Announcements and Stock Returns

A natural question is whether the patterns we observe around earningsannouncements generalize to the typical news release. We examine the extentto which news-media releases affect returns. After aligning news according toa market’s time zone, we start by examining all news that occurs after the priorday’s close and before the current day’s open.

4.1 Firm-level regressionsBecause the influence of news on prices may differ on a firm-by-firm basis, weestimate the following simple firm-level regressions:

ln(1 +

∣∣ARi,t

∣∣) = αi + βNewsDay,i NewsDayi,t−1

+ βArtCount,i Ar tCounti,t−1 + εi,t , (1)

where the dependent variable is the natural log of one plus the absoluteabnormal return,NewsDayt−1 indicateswhether there is at least one articlewith the firm’s name in the headline or lead paragraph appearing just priorto that trading day, andArtCountt−1 is the number of articles meeting thiscriteria. News days with more articles may be events of greater importance, orthe market may respond to a greater extent simply because more investors areaware of the news.16

TheadjustedR2 from these regressions give a simple sense for how closelyreturns are related to variation in news coverage. We estimate this regressionat the firm level and then average the adjustedR2 acrossall firms in a country.Figure 2 reports the average news adjustedR2 for each country with 26

15 Theseresults are presented in Online Appendix Figure IA.2. For the rest of the article, we focus on absolutereturns because we believe Datastream’s volume data are less accurate than return data.

16 Roll (1988)does not run such regressions with absolute returns but does show that return volatility is slightlylower when public news days are excluded from a sample of U.S. stocks.

3962


ovember 15, 2011


ownloaded from



Figure 2(Continued)

3963


ovember 15, 2011


ownloaded from



Figure 2Adjusted R2 from regressions of returns on newsThis figure plots average adjustedR2 from firm-level regressions of returns on news variables. Details of thecalculation of the average adjustedR2 are discussed in Table4 and Section4.1. In Panel A and the left panel ofPanel C, the charts include only “Pre-Trade News” articles with time stamps that occurred between the previousday’s market close and the current day’s market open. In Panel B and the right panel of Panel C, we count allarticles as news even if there is no time stamp as in Panel C of Table4. Emerging markets with fewer than 20firm years are grouped together in the “Other Emerg.” category. Panel C presents the developed- and emerging-market average of the country-level average Adj.R2 measures. Averages are also presented for firms in three“news bins”: 5 to 15 news days in a year, 15 to 25, and 25 and more. Before averaging across countries, wedelete a news-bin observation if there are fewer than five firm-year observations in the bin. In Panels A and B,stripes indicate that the Adj.R2 is significantly higher, using a two-sided bootstrappedt-test withalpha=0.05.

developed markets in black and 25 emerging markets in gray.17 Bars are stripedif the average adjustedR2 is significantly higher than a random reassignmentof (the same number of) news days, using a two-sided bootstrappedt-testwith alpha = 0.05. Countries are ordered according to the magnitude of theadjustedR2. Panel A in Figure2 shows that most developed markets are tothe left with a greater fraction of return volatility “explained” by news daysand article counts on those news days. The average adjustedR2 is over 3%in Denmark, Sweden, the United States, the Netherlands, the U.K., Portugal,and Belgium, whereas Peru, Argentina, Kenya, Pakistan, Latvia, Morocco, thePhilippines, Taiwan, Egypt, and Slovenia exhibit adjustedR2s below 0.005.All emerging markets have average adjustedR2s below 0.014. The developedmarkets of Cyprus, Australia, Canada, Greece, and Taiwan do not conform tothe developed/emerging split as they have adjustedR2s below 0.01.

Corresponding to Figure2, Panel A1 of Table4 presents more detail anddeveloped and emerging-market averages. Panel A1 shows that in developedmarkets, the average adjustedR2 is 0.024, whereas in emerging marketsit is only 0.007, a significant difference using a bootstrapped test; thet-statistic is 5.85. We also ask whether the regression fit is improved on anindividual firm basis. The news variables help explain volatility for 40% ofdeveloped market firms, but only 18% of emerging market firms.

17 We require a minimum of 20 firm-year observations for the country to be included. Six emerging markets withless than 20 firm years are in the “Other emerging” group and count as one of the 25 markets.

3964


ovember 15, 2011


ownloaded from



Tabl

e4

New

sre

gres

sion

fit(a

dj. R

2)

P ane

lA1:

Pre

-Tra

ding

Day

New

sOnl

y

Cou

ntry

Firm

-Yea

rC

ount

Adj

.R2

-A

llN

ews

Impr

oves

Fit

(%of

Firm

s)A

dj.R

2[5

,15)

Adj

.R2

[15,

25)

Adj

.R2

[25,

Inf)

Aus

tral

ia44

0.00

8∗22

.7%

0.00

5∗0.

017∗

0.01

8∗

Aus

tria

660.

021∗

42.4

0.00

20.

032∗

0.02

3∗

Bel

gium

700.

033∗

60.0

0.01

8∗0.

026∗

0.04

2∗

Can

ada

200.

008∗

25.0

––

0.01

1∗

Cyp

rus

210.

009∗

33.3

––

–D

enm

ark

610.

057∗

62.3

–0.

020∗

0.06

5∗

Fin

land

690.

029∗

43.5

0.03

9∗0.

050∗

0.03

4∗

Fra

nce

450.

016∗

37.8

0.01

6∗0.

030∗

Ger

man

y35

0.01

6∗40

.00.

007

0.00

20.

044∗

Gre

ece

570.

006∗

24.6

0.01

5∗–

0.00

8H

ong

Kon

g47

40.

015∗

27.4

0.02

1∗0.

037∗

0.00

8∗

Irel

and

360.

020∗

33.3

0.01

7∗0.

035∗

0.01

9∗

Isra

el53

0.01

4∗30

.20.

005

0.04

0∗0.

028∗

Italy

800.

021∗

35.0

0.00

3–

0.02

5∗

Japa

n15

90.

029∗

50.3

0.03

2∗0.

030∗

0.01

2∗

Net

herla

nds

710.

039∗

46.5

0.02

9∗0.

040∗

0.05

4∗

New

Zea

land

700.

016∗

32.9

0.01

4∗0.

027∗

0.00

5N

orw

ay71

0.02

8∗45

.10.

001

0.02

9∗0.

033∗

Por

tuga

l42

0.03

4∗45

.20.

010∗

0.00

50.

044∗

Sin

gapo

re24

20.

019∗

36.8

0.01

9∗0.

035∗

0.01

9∗

Spa

in86

0.01

7∗33

.70.

014∗

0.00

10.

023∗

Sw

eden

810.

055∗

69.1

0.06

6∗0.

033∗

0.06

7∗

Sw

itzer

land

520.

021∗

40.4

0.00

8∗0.

014∗

0.03

4∗

Taiw

an86

40.

002∗

8.9

0.00

10.

004∗

0.00

9∗

U.K

.37

0.03

5∗59

.50.

033∗

0.04

2∗0.

031∗

U.S

.87

30.

045∗

53.8

0.01

8∗0.

040∗

0.05

8∗

(co

ntin

ue

d)

3965


ovember 15, 2011


ownloaded from



Tabl

e4

Con

tinue

d

Pan

elA

1:P

re-T

radi

ngD

ayN

ews Onl

y

Cou

ntry

Firm

-Yea

rC

ount

Adj

.R2

-A

llN

ews

Impr

oves

Fit

(%of

Firm

s)A

dj.R

2[5

,15)

Adj

.R2

[15,

25)

Adj

.R2

[25,

Inf)

Arg

entin

a13

60.

005∗

16.9

0.00

6∗0.

001

0.00

4∗

Bra

zil

490.

007∗

16.3

0.00

2−

0.00

30.

019∗

Chi

le77

0.00

7∗19

.50.

009∗

0.01

1∗0.

005∗

Chi

na41

80.

008∗

23.2

0.00

9∗0.

005∗

0.01

6∗

Col

ombi

a43

0.01

2∗25

.60.

005

–0.

028∗

Egy

pt86

0.00

24.

7−

0.00

1–

0.01

2∗

Hun

gary

920.

010∗

25.0

0.01

3∗0.

010∗

0.01

3∗

Indi

a44

10.

011∗

26.3

0.01

6∗0.

017∗

0.01

7∗

Indo

nesi

a61

0.00

5∗11

.5−

0.00

10.

007

0.01

2∗

Ken

ya10

00.

005∗

16.0

0.00

8∗0.

016∗

0.00

0La

tvia

300.

003

20.0

0.00

0–

–Li

thua

nia

620.

006∗

14.5

0.00

8∗0.

005

0.00

1M

alay

sia

432

0.01

0∗21

.30.

018∗

0.02

5∗0.

011∗

Mex

ico

520.

014∗

25.0

0.01

4∗0.

008∗

0.02

5∗

Mor

occo

430.

003

11.6

––

–O

ther

Em

erg.

620.

006∗

14.5

0.00

50.

003

0.01

0∗

Paki

stan

127

0.00

4∗17

.30.

004∗

0.00

50.

033∗

Per

u41

0.00

5∗12

.20.

003

0.00

00.

016∗

Phi

lippi

nes

690.

002

7.2

0.00

30.

005

0.00

0P

olan

d13

30.

009∗

20.3

0.00

7∗0.

003

0.01

6∗

Rom

ania

200.

005∗

20.0

––

–S

love

nia

250.

001

4.0

0.00

3–

–S

outh

Afr

ica

145

0.00

9∗24

.10.

012∗

0.01

0∗0.

007∗

Tha

iland

102

0.00

7∗23

.50.

011∗

0.00

50.

004

Tur

key

167

0.01

1∗26

.30.

011∗

0.01

8∗0.

023∗

(co

ntin

ue

d)

3966


ovember 15, 2011


ownloaded from



Tabl

e4

Con

tinue

d

Cou

ntry

Cou

ntry

Cou

ntA

dj.R

2-

All

New

sIm

prov

esF

it(%

ofF

irms)

Adj

.R2

[5,1

5)A

dj.R

2[1

5,25

)A

dj.R

2[2

5,In

f)

Pane

lA1:

Pre

-Tra

ding

Day

New

sO

nly (con

tinue

d)

Dev

elop

ed26

0.02

440

.00.

017

0.02

70.

030

Em

ergi

ng25

0.00

717

.90.

007

0.00

80.

013

Dif

fere

nce

0.01

722

.10.

010

0.01

90.

017

( t-s

tat.)

(5.8

5)(7

.36)

(3.2

0)(5

.70)

(4.6

4)

Pane

lA2:

Pre

-Tra

ding

Day

New

s:S

ampl

ew

ithF

activ

aco

nfirm

edea

rnin

gsan

noun

cem

ents

Dev

elop

ed18

0.04

550

.70.

045

0.04

30.

047

Em

ergi

ng13

0.01

225

.00.

010

0.01

30.

019

Dif

fere

nce

0.03

225

.70.

035

0.03

00.

028

(t-s

tat.)

(4.6

2)(5

.02)

(3.4

8)(4

.60)

(2.8

3)

Pane

lA3:

Pre

-Tra

ding

Day

New

s:E

xclu

ding−

1to

+2

arou

ndea

rnin

gsann

ounc

emen

ts

Dev

elop

ed18

0.04

147

.80.

041

0.03

80.

046

Em

ergi

ng13

0.01

224

.60.

009

0.01

40.

019

Dif

fere

nce

0.02

823

.30.

032

0.02

40.

027

(t-s

tat.)

(4.6

2)(4

.45)

(3.4

0)(3

.37)

(3.0

3)

P ane

lB:L

ocal

Tim

eS

tam

ped

News

Dev

elop

ed26

0.03

943

.00.

025

0.04

10.

043

Em

ergi

ng25

0.01

019

.00.

010

0.01

00.

017

Dif

fere

nce

0.02

924

.00.

015

0.03

10.

026

(t-s

tat.)

(6.3

9)(7

.09)

(2.9

1)(5

.71)

(4.8

7)

Pane

lC:A

llA

vaila

ble

Art

icle

s(n

ottim

ezon

eadju

sted

)

Dev

elop

ed26

0.03

744

.30.

028

0.02

30.

043

Em

ergi

ng29

0.01

324

.00.

007

0.01

40.

019

Dif

fere

nce

0.02

420

.30.

021

0.00

80.

024

(t-s

tat.)

(4.9

1)(5

.07)

(2.5

3)(1

.44)

(4.2

4)

(co

ntin

ue

d)

3967


ovember 15, 2011


ownloaded from



Tabl

e4

Con

tinue

d

Cou

ntry

Cou

ntry

Cou

ntA

dj.R2

-A

llN

ews

Impr

oves

Fit

(%of

Firm

s)A

dj.R

2[5

,15)

Adj

.R2

[15,

25)

Adj

.R2

[25,

Inf)

Pane

lD:D

owJo

nes,

Reu

ters

,F

ina

nci

alT

ime

s,an

dTh

eW

all

Str

ee

tJo

urn

al,

GM

TD

ate Sta

mp

Dev

elop

ed25

0.04

143

.00.

030

0.04

90.

052

Em

ergi

ng23

0.01

423

.20.

015

0.01

40.

023

Dif

fere

nce

0.02

719

.70.

015

0.03

50.

030

(t-s

tat.)

(5.6

9)(5

.54)

(2.5

4)(4

.75)

(4.3

8)

Thi

sta

ble

show

sav

erag

ead

just

edR2

from

firm

-leve

lreg

ress

ions

ofre

turn

son

new

sva

riabl

esde

rived

from

the

artic

les

sum

mar

ized

inTa

ble

1.T

head

just

edR

2co

mes

from

the

follo

win

gre

gres

sion

run

each

firm

year

:

ln( 1

+∣ ∣ A

Ri,

t∣ ∣)=

αi+

βN

ews

Da

y,iN

ews

Da

y i,t−

1+

βA

rtC

ou

nt,i

Art

Co

un

t i,t−

1+

ε i,t

,

whe

re∣ ∣ A

Ri,

t∣ ∣is

firm

i ’s

abno

rmal

vola

tility

onda

ytca

lcul

ated

asde

scrib

edin

Sec

tion3.

1,N

ews

Da

y i,t−

1is

anin

dica

tor

ifth

ere

isan

artic

lew

ithth

efir

m’s

nam

ein

the

head

line

orle

adpa

ragr

aph

onda

yt−1,

and

Ar t

Co

un

t i,t−

1is

the

num

ber

ofth

ese

artic

les.

Pan

elA

only

uses

pre-

trad

ene

ws,

whi

chm

eans

artic

les

that

appe

arfr

omth

ecl

ose

ofth

em

arke

ton

day

t−1

toth

em

arke

top

enon

day t.

Con

sequ

ently

,P

anel

Are

quire

sth

atar

ticle

sha

vetim

est

amps

we

can

conv

ert

tolo

calt

imes

.P

anel

A1

uses

allc

ount

ry-fi

rm-y

ears

.P

anel

A2

pres

ents

aver

ages

over

coun

try-

firm

-yea

rsw

here

we

have

confi

rmed

earn

ings

date

s.P

anel

A3

pres

ents

aver

ages

over

coun

try-

firm

-yea

rsw

here

we

excl

ude

earn

ings

date

s.In

Pan

els

Bth

roug

hD

,w

eus

eco

ntem

pora

neou

sne

ws, N

ews

Da

y i,t

and

Ar t

Co

un

t i,t.

Pan

elB

also

requ

ires

artic

les

have

time

stam

ps,b

utw

eas

sign

artic

les

toda

yt

ifth

eyar

epu

blis

hed

from

the

clos

eon

day

t-1

toth

ecl

ose

onda

yt .P

anel

Cus

esar

ticle

sev

enif

we

dono

thav

eth

eir

loca

ltim

est

amps

and

uses

the

date

publ

ishe

d,re

cord

edin

Gre

enw

ich

Mea

nT

ime,

asth

eda

teof

the

artic

le.P

anel

Dis

the

sam

eas

C,e

xcep

ttha

twe

rest

rictt

hear

ticle

sto

thos

efr

omD

owJo

nes,

Reu

ters

,F

ina

nci

alT

ime

s,orT

he

Wa

llS

tre

etJ

ou

rna

l.T

heto

pof

Pan

elA

1re

port

sav

erag

eA

dj.

R2

forc

ount

ries,

whi

leth

ebo

ttom

ofP

anel

A1

and

Pan

els

A2

thro

ugh

Dre

port

aver

ages

over

emer

ging

and

deve

lope

dm

arke

tsan

dth

eir

diffe

renc

es.C

ount

sfo

rco

untr

ies

inth

eto

pof

Pan

elA

1lis

tthe

num

ber

offir

mye

ars

incl

uded

,but

inth

ere

mai

nder

ofP

anel

A1

and

Pan

els

A2

thro

ugh

D,t

heco

unts

are

the

num

ber

ofco

untr

ies

inth

ecr

oss-

sect

ion.

Adj

.R

2-A

lllis

tsth

eav

erag

eA

dj.R2

for

allfi

rmye

ars.

“New

sIm

prov

esF

it”is

the

perc

enta

geof

firm

year

sw

here

noti

nclu

ding

the

new

s-da

ydu

mm

yan

dar

ticle

coun

tsre

sults

insi

gnifi

cant

lyw

orse

fitba

sed

ona

good

ness

-of-

fitF

-tes

tat

alp

ha

=0.

05.A

dj.R

2-

[5,1

5)is

the

aver

age

Adj

.R2

over

firm

year

sw

ith5

to15

days

with

new

sar

ticle

sdu

ring

the

year

,Adj

.R

2-

[15,

25)

isth

eav

erag

eA

dj.R2

over

firm

year

sw

ith15

to25

new

sda

ys,a

ndA

dj.R2

-[2

5,In

f)is

the

aver

age

Adj

.R2

over

firm

year

sw

ith25

orm

ore

new

sda

ys.T

obe

incl

uded

,eac

hne

ws

coun

tpor

tfolio

mus

tcon

tain

atle

astfi

vefir

mye

ars.

Afir

mye

aris

incl

uded

ifth

ere

are

atle

ast1

00tr

adin

gda

ysw

ithre

turn

s,an

d20

firm

year

spe

rco

untr

y.A

bnor

mal

retu

rns,

AR

i,t,

are

Win

soriz

edat

the

1stan

d99

thpe

rcen

tiles

.Ast

aron

a

coun

try-

leve

lAdj

.R2

inP

anel

A1

indi

cate

sth

atth

eA

dj.R2

issi

gnifi

cant

lyhi

gher

,usi

nga

two-

side

dbo

otst

rapp

edt -te

stw

itha

lph

a=

0.05

,tha

nsp

ecifi

catio

nsw

here

the

sam

enu

mbe

rof

new

sda

ysan

dar

ticle

coun

tsar

era

ndom

lyas

sign

edth

eda

ysin

the

year

.To

calc

ulat

eth

eco

untr

y-le

velb

oots

trap

ped

t -st

atis

ticfo

rea

chof

500

itera

tions

,we

rand

omiz

eth

ene

ws

days

with

inea

chfir

m-y

ear

sam

ple,

estim

ate

the

regr

essi

onde

scrib

edab

ove,

then

calc

ulat

eth

et-

stat

istic

for

the

diffe

renc

ebe

twee

nth

eac

tual

Adj

.R

2an

dth

ebo

otst

rapp

edA

dj.R2.

Sig

nific

ance

isba

sed

onth

edi

strib

utio

nof

thes

eco

untr

y-le

vel

t -st

atis

tics.

Test

sfo

rth

edi

ffere

nce

betw

een

leve

lsin

emer

ging

and

deve

lope

dm

arke

tsar

et -

stat

istic

sbas

edon

boot

stra

pped

stan

dard

erro

rsfr

om1,

000

draw

sw

ithre

plac

emen

tfro

mth

esa

mpl

eof

coun

try-

leve

lave

rage

Adj

.R

2s.

3968


ovember 15, 2011


ownloaded from



Individual articles may have more information content in markets wherethere are fewer articles. Hence, in the last three columns of Table4, wegroup all firms in a market into three bins according to the number of daysin the year that there is news about the firm. The large difference betweendeveloped and emerging markets holds for each news-days-per-year bin.In Panel A2 of Table4, we first limit our sample to the subset of firmswhere earnings announcements have been cross-checked with Factiva newsarticles. In Panel A3, we use this same subset of firms but exclude theearnings announcement window. We still find similar large differences betweenemerging and developed markets.

Rather than only examine pre-trade news, we extend the analysis in PanelB of Table4 to all news over the day (from market close on dayt-1 to theclose on dayt) for the sample of firms (the same as in Panel A) where wehave a time stamp to confirm the exact local time of the article. Next, weexpand the sample in Panel B of Figure2 and Panel C of Table4 even furtherto include all articles, even if we do not have a local time stamp. Both testsshow similar patterns, with most developed markets exhibiting much higheraverage explanatory power in the news regressions. Panel B of Figure2 showsthat financial news bears the strongest relation to prices in northern Europeancountries and the weakest relation in Morocco, Pakistan, Peru, Zimbabwe, andTaiwan.18 Panel C of Figure2 presents developed and emerging averages bybins based on the number of pre-trade news articles in the left panel and onall news in the right panel. In most bins, we find that the explanatory power ofnews for absolute returns is more than twice as high in developed markets. Weexamine news on only those days when the article is published by Dow Jones,The Wall Street Journal, Financial Times, or Reuters in Panel D of Table4 toinvestigate whether the type of news outlet affects inferences. We find similarlarge differences between developed and emerging markets.

Overall, the findings indicate that coverage by the financial media helpsexplain idiosyncratic variation in stock prices much better in developed than inemerging markets.

4.2 RankingThe regressions in the previous subsection are arguably superior to the simplecomparisons between the volatility on news and non-news days we present inthis subsection, because they allow us to model return volatility as a function ofexistence of any news (the news-day dummy) and the importance of the news(the article counts). However, simple comparisons of news-day to non-news-day volatility allow us to more directly quantify the magnitude of increased

18 For Taiwanese earnings announcements, we find that revenues are often released ahead of the earningsannouncement. Thus, one possibility is that the news in the articles is mostly known beforehand through therevenue information.

3969


ovember 15, 2011


ownloaded from



returnvolatility on news days.19 We scale each rank within a country by thenumber of observations to obtain a percentile ranking to make the rankingscomparable across countries, where 0.50 would be the mean percentile rankif news-day and non-news-day volatility were the same. We then calculate theaverage percentile rank and subtract 0.50 to center the averages around zero.

Panel A of Figure3 shows that the distribution of news-day volatility ishigher than non-news-day volatility in most developed markets. Statisticallysignificant shifts in distribution between news-day and non-news-day volatilityare indicated in stripes and are based on the non-parametric Fligner-Policellotest for differences in central tendency. Most developed markets are to the leftof the graph, with relatively higher news-day volatility, and many emergingmarkets to the right, with little difference between news and non-news days.However, there is variation, with Hungary, India, and Thailand exhibitingnews-day volatilities that rank high, whereas the developed markets of Taiwan,Greece, and Cyprus exhibit no meaningful difference between news andnon-news days. There are many emerging markets that show no signifi-cant differences between news days and non-news days (Indonesia, Brazil,Latvia, Romania, the Philippines, Lithuania, Kenya, Pakistan, Morocco, andSlovenia).

In Panel B of Figure3, we report the ratio of the average news-day volatilityto the average non-news-day volatility minus one. In Panel B of Figure3, wesee large differences between news and non-news days. The ratio above 0.70for Japan indicates that the average day with news prior to the market open ismore than 70% more volatile than the average non-news day.20 Overall, thepatterns across countries are similar to Panel A except that more emergingmarkets (Romania, China, Malaysia, Thailand, and India) gravitate to the left,meaning higher reactions, because the aggregation can put considerable weighton extreme observations in a country with few stocks. In Panel C, the medianratio is displayed. Here, the patterns are similar to those in Panel A, with mostdeveloped markets having higher reactions. However, there are still emergingmarkets (Thailand, Romania, India, and Poland) that exhibit higher reactions,whereas a few developed markets (Taiwan, Greece, and Cyprus) have lowermedian reactions. In general, the mean ratios show a slightly more importantrole for news in emerging markets than the medians. This indicates that thereare some large events with news in emerging markets, but in most emergingmarkets the typical news day is no different from a non-news day.

We also examine, for robustness, the developed and emerging averagenews-day rankings, mean and median news to non-news-day volatility ratiosfor samples and classifications of what defines a news day. We examine

19 We start by calculating the average daily excess return volatility for each firm year on news and non-news daysseparately (using news prior to the market opening). Then we pool the average firm-year news and non-newsvolatilities within each country and rank them from lowest to highest average absolute return movement.

20 For significance, we use the two-side 5% level based ont-tests with bootstrapped standard errors.

3970


ovember 15, 2011


ownloaded from



earnings articles, pre-trading-day news bins, local time-stamped news, allarticles regardless of time stamp, and the major world business news outlets(Dow Jones,The Wall Street Journal, Financial Times, or Reuters) in the

Figure 3(Continued)

3971


ovember 15, 2011


ownloaded from



Figure 3Pre-trade news-day to non-news-day return volatilityThis figure plots measures that relate average news-day volatility to non-news-day volatility, where volatility,|ARi,t |, is calculated as described in Section3.1. This figure uses only “Pre-Trade News,” as defined in Table4. Volatility is averaged separately for news and non-news days to the firm-year level. In Panel A, we rankaverage firm-year news and non-news volatility from highest to lowest and report the mean rank of the news-day firm-year averages. We divide each rank by the total number of observations to convert the ranks to apercentile and subtract 50 from the percentile for comparability across markets. In Panel B, we average acrossfirm years to get country average news-day and non-news-day volatility, then report the ratio of average news-day to non-news-day volatility minus one. Panel C is the same as B, except using medians. Stripes indicatestatistical significance atalpha = 0.05. In Panels A and B, standard errors for the difference between news-and no-news-day volatility are bootstrapped with 100,000 draws with replacement. In Panel C, the tests areFligner-Policello difference in central tendency tests written in SAS by Paul von Hippel and available athttp://www.sociology.ohio-state.edu/people/ptv/macros/fligner policello.htm.

Online Appendix Table IA.7. Overall, markets tend to move more on newsdays in developed markets than emerging.

5. Investigating Determinants through Cross-country Analysis

Our findings indicate a clear distinction between the market reaction to newsin developed and emerging markets and wide variation within these categoriesthat should be useful for investigating the possible determinants of the return-news relation. We begin investigating hypotheses H1, H2A, H3A, H3B, andH4 through cross-country regressions.

We estimate cross-country OLS regressions with combinations of variablesclosely tied to our hypotheses in Section1. We also examine inferences using aBayesian model selection procedure. The purpose of the Bayesian procedure isto systematically compare our favored hypotheses to alternatives suggested by

3972


ovember 15, 2011


ownloaded from

http://www.sociology.ohio-state.edu/people/ptv/macros/fligner_policello.htm.



theliterature. For our purposes, the main advantage of the Bayesian procedureover alternative methods is that it is a robust way to compare a number ofalternative specifications in a small sample.

5.1 Earnings news5.1.1 Basic cross-country regressions.We start by examining nine vari-ables directly linked to our four major hypotheses from Section1. Thevariables that map to these hypotheses are detailed in the Appendix, TableA1. Since we do not have a time series of observations for all explanatoryvariables, we calculate a measure of the average earnings reaction in eachcountry across our four-year sample period. This is used as the dependentvariable. Aggregation across time can reduce noise and does not seem toimpose much cost, given that there is relatively little change in market featuresover our short 2004-to-2008 time period.21

Table5 starts out with nine regressions, each with an intercept and a singlevariable of interest as explanatory variables. Then, as is common in the cross-country literature, we addGDP per capitato capture an alternative null thatour cross-country patterns are simply related to the economic development ofmarkets. The regressions in Table5, Panel A, provide little evidence to supporthypothesis 1: Intra-industry timing of earnings announcements is unableto explain cross-country differences in event reactions. All other variablesare significant in the direction consistent with our hypotheses (HypothesesH2A, H3A, H3B, and H4).Insider trading, technological development, andaccounting standardshave the highestt-statistics and adjustedR2s. Insidertrading, technological development, andaccounting standardsare still highlysignificant, even when includingGDP per capitain the regressions. In supportof the journalism quality hypothesis,news clusteringaround announcementdays andfree pressare also significant, witht-statistics above 2. In short,where insider trading is more prevalent, the price response to earnings news islower. Better news transmission (news clustering, free press, andtechnologicaldevelopment) also yields a stronger response to earnings news.

In Panel B of Table5 we estimate regressions using combinations of twovariables from among those that were significant in Panel A.Insider trading,accounting standards, and technological developmentare highly significant.Free pressis significant in all but one specification withaccounting standards.News clusteringis only significant in one specification. In Panel C, we estimatecombinations of these remaining four significant variables.Insider tradingissignificant in all specifications, andaccounting standardsand technologicaldevelopmentin three of four. Overall, the specifications show considerablesupport for cross-country differences in earnings event reactions being driven

21 We have also collected variables from other papers or from data sources that only cover part of the 2004-to-2008period. In these cases, we use the average value of the data available.

3973


ovember 15, 2011


ownloaded from



Tabl

e5

OLS

regr

essi

ons

ofln

earn

ings

reac

tions

onco

untr

ych

arac

teris

tics

Pan

elA

:Reg

ress

ions

with

One

Varia

ble

ofInte

rest

No

Coe

f.A

dj.R

2G

DP

Coe

f.G

DP

Adj

.R2

Pre

-Eve

ntP

ublic

Info

rmat

ion

Dis

sem

inat

ion

lnA

nn

ou

nce

me

ntO

rde

r−

0.03

−0.

030.

050.

670.

41(−

0.20

)(0

.38)

(5.3

1)In

side

rTra

ding

Insi

de

rT

rad

ing

−0.

790.

62−

0.67

0.17

0.62

(−7.

91)

(−4.

52)

(1.1

2)N

ews

Tra

nsm

issi

onF

ina

nci

alP

ress

Du

rin

gE

ven

t [-1,2

]0.

370.

120.

100.

620.

41(2

.45)

(0.7

0)(4

.45)

New

sC

lust

erin

g0.

510.

240.

280.

540.

47(3

.57)

(2.1

5)(4

.20)

In-d

ep

thA

rtic

les

Du

rin

gE

ven

t [-1,2

]0.

450.

180.

180.

580.

43(3

.04)

(1.2

7)(4

.18)

Fre

eP

ress

0.61

0.36

0.37

0.47

0.51

(4.7

2)(2

.77)

(3.5

2)Te

ch.D

eve

lop

me

nt

0.76

0.57

0.58

0.29

0.61

(7.2

0)(4

.35)

(2.1

4)A

ccou

ntin

gQua

lity

Acc

ou

ntin

gS

tan

da

rds

0.80

0.63

0.63

0.27

0.67

(8.1

4)(5

.39)

(2.3

3)P

ct.I

ntl.

GA

AP

0.40

0.14

0.12

0.62

0.43

(2.6

6)(0

.87)

(4.4

7)C

ontr

ol GD

PP

er

Ca

pita

0.66

0.42

––

–(5

.36)

––

(co

ntin

ue

d)

3974


ovember 15, 2011


ownloaded from



Tabl

e5

Con

tinue

d

Pan

elB

:Reg

ress

ions

with

Two

Varia

bles

ofInte

rest

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

Insi

de

rTra

din

g−

0.43

−0.

65−

0.50

−0.

77(−

3.04

)(−

6.04

)(−

3.78

)(−

5.98

)N

ews

Clu

ste

rin

g0.

040.

170.

280.

29(0

.31)

(1.5

0)(1

.92)

(2.8

3)F

ree

Pre

ss0.

290.

200.

350.

48(2

.67)

(1.6

0)(3

.31)

(3.3

1)Te

ch.D

eve

lop

me

nt

0.40

0.36

0.61

0.67

(2.9

7)(2

.52)

(5.8

6)(6

.48)

Acc

ou

ntin

gSta

nd

ard

s0.

470.

680.

520.

72(3

.30)

(5.5

7)(3

.63)

(6.5

4)

Adj

.R2

0.70

0.67

0.69

0.61

0.65

0.68

0.64

0.66

0.40

0.64

P ane

lC:R

egre

ssio

nsw

ithT

hree

and

Fou

rVa

riabl

esof

Inte

rest

[1]

[2]

[3]

[4]

[5]

Insi

de

rTra

din

g−

0.42

−0.

35−

0.40

−0.

33(−

3.02

)(−

2.38

)(−

3.10

)(−

2.30

)F

ree

Pre

ss0.

180.

250.

220.

20(1

.61)

(2.5

8)(1

.93)

(1.8

5)Te

ch.D

eve

lop

me

nt

0.25

0.36

0.38

0.27

(1.7

4)(2

.88)

(2.7

4)(1

.97)

Acc

ou

ntin

gSta

nd

ard

s0.

370.

340.

370.

22(2

.41)

(2.1

8)(2

.36)

(1.3

1)

Adj

.R2

0.71

0.71

0.73

0.70

0.73

Thi

sta

ble

cont

ains

regr

essi

ons

ofav

erag

ena

tura

llog

ofno

rmal

ized

earn

ings

reac

tions

for

39co

untr

ies

onco

untr

ych

arac

teris

tics.

We

cons

ider

nine

pote

ntia

lexp

lana

tory

varia

bles

that

refle

cthy

poth

eses

abou

tpu

blic

and

priv

ate

pre-

even

tin

form

atio

ndi

ssem

inat

ion,

new

str

ansm

issi

on,

and

acco

untin

gqu

ality

.Va

riabl

esar

ede

scrib

edin

deta

ilin

App

endi

xTa

ble

A1.

All

varia

bles

are

stan

dard

ized

.Pan

elA

pres

ents

regr

essi

ons

with

one

expl

anat

ory

varia

ble.

Pan

elB

pres

ents

regr

essi

ons

with

two

expl

anat

ory

varia

bles

usin

gal

lcom

bina

tions

ofva

riabl

esth

atar

esi

gnifi

cant

inP

anel

Aw

henGD

PP

er

Ca

pita

isus

edas

aco

ntro

l.P

anel

Cpr

esen

tsre

gres

sion

sw

ithth

ree

and

four

expl

anat

ory

varia

bles

usin

gva

riabl

esth

atw

ere

cons

iste

ntly

sign

ifica

ntin

the

Pan

elB

regr

essi

ons.

Par

enth

eses

indi

cate

t -st

atis

tics.

3975


ovember 15, 2011


ownloaded from



by differences in the amount of insider trading, the extent of technologicaldevelopment, and accounting standards.

5.1.2 Stochastic search variable selection.One challenge in the cross-country literature is that there are many explanations for why stocks in onecountry might be different from those in another. Since the sample of countriesis small, the inclusion or exclusion of one variable in a regression may changeinferences about another. A second problem is that for each hypothesis, thereare multiple empirical proxies to choose from. In order to combat these twoproblems and examine the validity of our findings in a systematic frameworkdesigned to deal with such issues, we turn to a well-known Bayesian variableselection procedure.

The Stochastic Search Variable Selection (SSVS) methodology ofGeorgeand McCulloch(1993) embeds standard multiple regression in a hierarchicalBayesian model and is used to identify an important subset of explanatoryvariables. A primary advantage of SSVS over other variable selection methodsis that it selects variables based on the size of their impact on the dependentvariable—their economic significance. The model assumes that the dependentvariable is generated by a combination of the independent variables, and foreach combination, provides a probability that it generated the observed data.These posterior probabilities take into account the number of models andvariables being tested. One is also able to come up with a posterior probabilitythat a variable has a coefficient that is meaningfully different from zero. Weuse these posterior probabilities to identify variables that explain a country’saverage market reaction to news.22

We use a much broader list of variables in the SSVS tests: 33 in theearnings reactions test and 29 in the general news tests. These variableseither relate to our four hypotheses or to more general explanations related toeconomic and financial development, the regulatory environment, governance,or the characteristics of equity markets that are common in the internationalliterature.

SSVS requires priors for the residual variance of the regression,σ 2, thevariances of important and unimportant coefficients,v1 andv0, respectively,and the probability that the independent variables are important,p. We usetwo different priors for the probability that a given independent variable isimportant. The prior ofp = 0.5 says the probability a variable is included in themodel that best explains the dependent variable is the same as the probabilityit is not. This is a standard default prior forp. Since we look at a large numberof variables, we think we should be more skeptical that any given variable is inthe true model. We incorporate this greater skepticism through a second prioron the probability a variable is important,p = 0.15. This more skeptical prior

22 This methodology is similar to the one used byCremers(2002) in selecting important market wide predictorvariables.

3976


ovember 15, 2011


ownloaded from



shrinksposterior probabilities toward zero. Another way to think about this isthat we think there is some probability that a variable looks important in thedata due to random chance, and to account for this, we use a skeptical priorprobability (p = 0.15) that the variables we consider are important. We discussour priors and other details of the SSVS model in Table6.

Panel A of Table6 contains the probability that each independent variableis important for explaining earnings reactions. With the prior ofp = 0.15,financial market sophistication, accounting standards,insider trading, andtechnological developmentare all relatively more important as they have poste-rior probabilities above 0.1. With the less skeptical prior (p = 0.5), these fourvariables plus an investor protection rank and the average number of in-depthnews articles appearing before the event all have posterior probabilities above0.30. Notably, some traditional cross-country determinants, such as insider-trading enforcement, trading costs, and market-level characteristic variables(including market modelR2),23 arenot likely to help explain differences inearnings event reactions.

Panel B of Table6 contains the four best models from each of the two priorsettings, each of those models’ posterior probabilities, along with coefficientestimates and 95% credible intervals.Financial market sophisticationis in thebest model under both priors. With the prior ofp = 0.15,accounting standardsis the second-best model,insider tradingalone third, andfinancial marketsophisticationandinsider tradingtogether are the fourth best model. With theless skeptical (p = 0.5) prior, this model is the third best. The second-bestmodel with the less skeptical prior containsfinancial market sophistication,technological development, the number ofin-depth articles before the event,investor protection, and news clustering. Because we are not exactly surewhat financial market sophisticationis measuring, we take more economiccontent from the other variables. The variable is most highly correlated withaccounting standards,financial disclosure,technological development, andinsider trading(as shown in Online Appendix Table IA.8). The survey variablemay have an advantage over other variables as it captures several facets of theinformation environment related to three of the four main hypotheses.

One concern may be that the cross-country differences are a function ofthe size and industry composition of firms in the market. To control for thesedifferences in Table6, Panel C, we estimate event-level regressions in whichwe first demean each firm’s earnings reaction by size and industry averagereaction.Financial market sophisticationis the most important, followedby the prevalence ofinsider trading. Accounting standardsis the fourthmost important variable, buttechnological developmentis not among the topten. Hence, except fortechnological development, the firm-level regressionsconfirm inferences from the country-level regressions.

23 Kelly (2007) andBartram, Brown, and Stulz(2011) examineR2 andidiosyncratic risk and do not find supportfor it as a measure of information production.

3977


ovember 15, 2011


ownloaded from



Tabl

e6

Reg

ress

ions

ofln

earn

ings

reac

tions

onco

untr

ych

arac

teris

tics

with

Bay

esia

nm

odel

sele

ctio

n

Pan

elA

:Mar

gina

lPos

terio

rP

roba

bilit

yth

atth

eVa

riabl

eis

Impo

rtan

t

Set

tingf

orp

0.15

0.5

0.15

0.5

Fin

an

cia

lMa

rke

tSo

ph

istic

atio

n0.

561

0.69

7P

ct.I

ntl.

GA

AP

0.01

60.

072

Acc

ou

ntin

gS

tan

da

rds

0.26

80.

319

An

ti-S

elf-

De

alin

gIn

dex

0.01

40.

104

Insi

de

rT

rad

ing

0.22

60.

312

Sh

ort

Sa

les

Fea

sib

le0.

014

0.07

8Te

ch.D

eve

lop

me

nt

0.15

80.

324

Ma

rke

tMo

de

lR2

0.01

30.

066

Fin

an

cia

lDis

clo

sure

0.09

60.

196

Fin

an

cia

lPre

ssD

urin

gE

ven

t0.

013

0.09

4In

vest

or

Pro

tect

ion

Ra

nk

0.07

40.

420

Sh

ort

Sa

les

Leg

al

0.01

30.

075

Co

un

try

Ris

k0.

064

0.15

7F

ina

nci

alP

ress

Be

fore

Eve

nt

0.01

30.

082

Fre

eP

ress

0.05

30.

199

lnA

nn

ou

nce

me

ntO

rde

r0.

012

0.06

1In

-de

pth

Art

icle

sB

efo

reE

ven

t0.

039

0.34

3In

sid

er

Tra

din

gE

nfo

rce

d0.

012

0.05

7In

-de

pth

Art

icle

sD

urin

gE

ven

t0.

032

0.20

4D

iscl

osu

reIn

dex

0.01

20.

074

Ma

rke

tTu

rnov

er/

GD

Px

10

00.

031

0.12

4P

ct.D

ays

No

n-Z

ero

Price

Ch

g.0.

012

0.06

9U

.K.L

aw

0.03

00.

143

Dire

cto

rL

iab

ility

Ind

ex0.

012

0.07

6G

DP

pe

rC

ap

ita0.

029

0.11

4C

ost

toE

nfo

rce

Co

ntr

act

s0.

011

0.06

2N

ews

Clu

ste

rin

g0.

028

0.21

5L

OT

Tra

din

gC

ost

0.01

10.

064

Sh

are

ho

lde

rL

aw

suits

Ind

ex0.

020

0.09

0A

vera

geL

ogF

irm

Siz

e0.

010

0.05

7In

vest

or

Pro

tect

ion

Ind

ex0.

018

0.11

2A

vera

geF

irm

-Lev

elP

/E0.

010

0.05

9O

nly

An

nu

alE

arn

.An

n.

0.01

60.

118

(co

ntin

ue

d)

3978


ovember 15, 2011


ownloaded from



Tabl

e6

Con

tinue

d

Pan

elB

:Bes

tMod

els

Set

tingf

orp=

0.15

Set

ting

for p

=0.

50(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Fin

an

cia

lMa

rke

tSo

ph

istic

atio

n0.

770.

511.

120.

810.

510.

77[.0

51,1

.04]

[0.1

6,0.

86]

[0.8

0,1.

44]

[0.4

5,1.

16]

[0.1

6,0.

85]

[0.5

1,1.

04]

Acc

ou

ntin

gS

tan

da

rds

0.74

[.048

,1.0

0]In

sid

er

Tra

din

g-0

.70

-0.3

8-0

.38

[-0.

96,-

0.45

][-

0.71

,-0.

05]

[-0.

71,-

0.06

]Te

ch.D

eve

lop

me

nt

0.33

[0.0

9,0.

58]

In-d

ep

thA

rtic

les

Be

fore

Eve

nt

-0.3

3-0

.46

[-0.

57,-

0.09

][-

0.70

,-0.

21]

Inve

sto

rP

rote

ctio

nR

an

k0.

410.

38[0

.17,

0.65

][0

.15,

0.60

]N

ews

Clu

ste

rin

g0.

31[0

.08,

0.55

]

P( γ

|Y)

0.20

20.

087

0.04

50.

035

0.00

50.

005

0.00

40.

004

(co

ntin

ue

d)

3979


ovember 15, 2011


ownloaded from



T abl

e6

Con

tinue

d

Pan

elC

:Firm

-Lev

elM

argi

nalP

oste

rior

Pro

babi

lity

that

Top

10Va

riabl

esar

eIm

port

ant

Set

tingf

orp

0.15

0.5

0.15

0.5

Fin

an

cia

lMa

rke

tSo

ph

istic

atio

n0.

120

0.42

7M

ark

etT

urn

ove

r/G

DP

x1

00

0.01

10.

050

Insi

de

rT

rad

ing

0.02

00.

083

Fre

eP

ress

0.00

90.

045

Inve

sto

rP

rote

ctio

nIn

dex

0.01

70.

093

U.K

.La

w0.

008

0.06

1A

cco

un

ting

Sta

nd

ard

s0.

015

0.06

2F

ina

nci

alP

ress

Art

icle

Du

rin

gE

ven

t0.

008

0.04

7S

ha

reh

old

er

La

wsu

itsIn

dex

0.01

30.

092

Fin

an

cia

lDis

clo

sure

0.00

80.

038

Thi

sta

ble

cont

ains

regr

essi

ons

ofth

ena

tura

llog

ofon

epl

usth

eno

rmal

ized

vola

tility

repo

rted

inTa

ble

3fo

r39

coun

trie

son

33po

tent

iale

xpla

nato

ryva

riabl

esde

scrib

edin

Onl

ine

Tabl

eA

.1.A

llva

riabl

esar

est

anda

rdiz

edby

subt

ract

ing

the

cros

s-co

untr

ym

ean

and

divi

ding

byth

est

anda

rdde

viat

ion.

We

use

the

Bay

esia

nS

toch

astic

Sea

rch

Varia

ble

Sel

ectio

nm

etho

dolo

gyof

Geo

rge

and

McC

ullo

ch(19

93):

Y=

Xβ

+ε,

ε∼

N( 0,

σ2

I),

(1)

σ2

∼vλ/χ

2 ν,

(2)

βi|

γi

∼

N( 0,

v 1)w

he

nγi

=1

N( 0,

v 0)w

he

nγi

=0,

(3)

f(γ

)∼∏

pγi i( 1

−p i) 1

−γi.

(4)

The

prio

rsar

eco

ntro

lled

byth

ehy

perp

aram

eter

sλ,ν,v

0,v 1

,an

dp .

One

may

thin

kof

λas

anes

timat

efo

rre

sidu

alva

rianc

ean

dν

asth

esa

mpl

esi

zefo

rth

ere

sidu

alva

rianc

ees

timat

e.W

ech

oose

the

sam

ple

varia

nce

ofth

ede

pend

ent

varia

ble

asou

rva

lue

for

λ,a

ndw

ese

tνat

3.T

hev 0

andv 1

hype

rpar

amet

ers

are

the

prio

rva

rianc

esof

coef

ficie

nts

onun

impo

rtan

tan

dim

port

anti

ndep

ende

ntva

riabl

es,r

espe

ctiv

ely.

We

belie

veth

atan

econ

omic

ally

sign

ifica

ntva

riabl

eis

one

for

whi

cha

one-

stan

dard

-dev

iatio

nch

ange

lead

sto

atle

asta

one-

tent

hst

anda

rd-

devi

atio

nch

ange

inth

ede

pend

entv

aria

ble,

whi

chis

≈[m

ax(N

orm

aliz

edV

ola

tility

)–m

in(N

orm

aliz

ed

Vo

latil

ity)]

/[N

o.o

fCo

un

trie

s].T

his

thre

shol

dco

rres

pond

sto

any

v 0an

dv1

satis

fyin

g0.

12=

log(

v 1/v

0)/

(v−

10

−v−

11

) ,an

dw

eus

ev1/v

0=

3,23

3 .W

eco

nsid

ertw

ova

lues

forp ,

the

prio

rpr

obab

ility

that

anin

depe

nden

tvar

iabl

eis

impo

rtan

t:0.

5an

d0.

15.W

eus

eth

eG

ibbs

sam

pler

toob

tain

2,00

0,00

0dr

aws

from

the

post

erio

rdis

trib

utio

nf(

γ|Y

)af

tera

1,00

0dr

awbu

rn-in

perio

d.P

anel

Are

port

sth

em

argi

nalp

oste

riorp

roba

bilit

yth

atγ

i=

1fo

rall

inde

pend

ent

varia

bles

.In

Pan

elB

,we

iden

tify

the

four

mod

els

with

the

high

estp

oste

rior

prob

abili

tyfr

omea

chof

the

two

prio

rsp

ecifi

catio

nsan

dre

port

mea

nva

lues

ofth

eim

port

antc

oeffi

cien

tsfr

omth

ose

mod

eldr

aws.

The

num

bers

inbr

acke

tsar

e95

%cr

edib

lein

terv

als.

We

also

run

the

proc

edur

eat

the

firm

leve

l,w

ithth

ede

pend

ent

varia

ble

dem

eane

dby

size

and

indu

stry

port

folio

grou

ps.W

eus

eU

.S.s

ize

terc

ilebr

eakp

oint

san

dF

ama

and

Fre

nch

48in

dust

ries.

Pan

elC

repo

rts

post

erio

rpr

obab

ilitie

sfo

rth

eto

p10

varia

bles

from

the

firm

-leve

lres

ults

.

3980


ovember 15, 2011


ownloaded from



5.2 General news regressions and robustnessBeyond earnings news, we examine news more broadly by constructing asimple composite measure of news importance using the standardized averageof the variables from Panel A of Figure2 and the three panels of Figure3.24

We first estimate OLS cross-country regressions in Table7. Only prevalenceof insider trading and technological development survive the simple GDP per-capita controls.

We again apply SSVS to test the robustness of our findings. Panel A ofTable 8 shows that with the prior ofp = 0.15, technological development,the prevalence ofinsider-trading survey variable, and the in-depth articlevariable are the only three variables with posterior probabilities above 0.10.Panel B of Table8 shows that with the prior of 0.15, the favored specificationis technological developmentalone, followed byinsider tradingalone, andthen in the third specification both variables. With the less skeptical prior(p = 0.50), the specification withtechnological development, insider trading,in-depth articles before the event, andfinancial disclosureis the favored speci-fication, followed bytechnological developmentalone, and thentechnologicaldevelopment, insider trading, andfinancial disclosure.

As with the responses to earnings news for robustness, we use this samemethodology at the event level in Panel C of Table8. In these generalnews regressions,financial disclosure,freedom of the press, insider trading,investor protection, andtechnological developmentare the five variables withthe highest posterior probabilities. It is comforting that both the prevalenceof insider tradingand technological developmentare again important in thefirm-level regressions.

Among our earnings reaction and general news regressions, both at themarket and firm level, the prevalence ofinsider tradingis always one of themost important variables.Technological developmentis important in all SSVStests except the firm-level earnings reactions test. Quite sensibly,accountingstandardsare important for understanding the reaction to earnings, but notgeneral news. Overall, the hypothesis with the most support in the data isinsider trading (Hypothesis H2A), followed by news transmission (HypothesisH3B).

6. Implications for Informed and Uninformed Trading

Because our cross-country regressions point to the importance of insidertrading, we seek additional verification by testing Hypotheses H2B and H2C,both of them related to insider trading.

24 Thefour measures areMean Percentile Rank of News Day Volatility, Ratio of Mean News to No-News Volatility,Ratio of Median News to No-News Volatility, andAdj. News R2.

3981


ovember 15, 2011


ownloaded from



Table 7OLS regressions of ln general news on country characteristics

Panel A: Regressions with One Variable ofInterest

No controls GDPcontrol

Coef. Adj. R2 Coef. GDP Adj. R2

Pre-Event Public Information Disseminationln Announcement Order −0.04 −0.02 0.00 0.33 0.17

(−0.31) (0.03) (3.11)InsiderTrading

Insider Trading −0.46 0.39 −0.46 −0.01 0.37(−4.97) (−3.34) (−0.07)

News TransmissionFinancial Press During Event[-1, 2] 0.24 0.08 0.10 0.29 0.19

(2.04) (0.76) (2.41)News Clustering 0.29 0.12 0.16 0.27 0.21

(2.43) (1.22) (2.26)In-depthArticles During Event[-1, 2] 0.12 0.00 −0.05 0.36 0.18

(1.02) (−0.43) (2.96)Free Press 0.23 0.08 0.07 0.30 0.18

(2.02) (0.58) (2.37)Tech. Development 0.47 0.41 0.43 0.05 0.40

(5.18) (3.60) (0.44)AccountingQuality

Accounting Standards 0.36 0.23 0.25 0.18 0.25(3.50) (1.92) (1.40)

Pct. Intl. GAAP 0.21 0.06 0.06 0.31 0.18(1.78) (0.50) (2.54)

ControlGDPPer Capita 0.33 0.20 – – –

(3.17) – –

Panel B: Regression with Two Variables ofInterest

[1]

InsiderTrading −0.24(−1.88)

Tech. Development 0.29(2.20)

Adj. R2 0.45

Thistable contains regressions of a country-level measure of market reaction to news for 38 countries on countrycharacteristics. The measure is the average of four standardized measures: theMean Percentile Rank of NewsDay Volatility, theRatio of Mean News to No-News Volatility, theRatio of Median News to No-News Volatility,and theNews Regression Fit (adj. R2). Construction of the three volatility measures is discussed in Figure3, and Table4 discusses the adj.R2 measure.We consider nine potential explanatory variables which reflecthypotheses about pre-event information dissemination, public and private, news transmission, and accountingquality. Variables are described in detail in Appendix Table A1. All variables are standardized. Panel A presentsregressions with one explanatory variable. Panel B presents one regression with the two explanatory variablesthat are significant in Panel A whenGDP Per Capitais used as a control. Parentheses indicatet-statistics.

6.1 Leakage prior to takeoversAccording to Hypothesis H2B, if the low stock price reaction to news is drivenby insider trading, we should see much more stock price run-up ahead ofmergers in low-reaction markets than in markets where stock prices respondmore to public news. Hence, we separate markets into two groups based onthe ranking of the target firm’s country of origin in the earnings announcement

3982


ovember 15, 2011


ownloaded from



Tabl

e8

Reg

ress

ions

ofth

ege

nera

lnew

sre

actio

nm

easu

reon

coun

try

char

acte

ristic

sw

ithB

ayes

ian

mod

else

lect

ion

Pan

elA

:Mar

gina

lPos

terio

rP

roba

bilit

yth

atth

eVa

riabl

eis

Impo

rtan

t

Set

tingf

orp

0.15

0.5

0.15

0.5

T ech

.Dev

elo

pm

en

t0.

635

0.73

6S

ho

rtS

ale

sFe

asi

ble

0.02

10.

120

Insi

de

rT

rad

ing

0.36

90.

664

Co

stto

En

forc

eC

on

tra

cts

0.01

90.

094

In-d

ep

thA

rtic

les

Be

fore

Eve

nt

0.10

80.

556

An

ti-S

elf-

De

alin

gIn

dex

0.01

70.

124

Co

un

try

Ris

k0.

072

0.25

5D

irect

or

Lia

bili

tyIn

dex

0.01

70.

132

Fin

an

cia

lDis

clo

sure

0.05

30.

440

Inve

sto

rP

rote

ctio

nR

an

k0.

016

0.12

1U

KL

aw

0.03

60.

231

Sh

ort

Sa

les

Leg

al

0.01

60.

096

Dis

clo

sure

Ind

ex0.

033

0.29

7F

ree

Pre

ss0.

016

0.09

8A

vera

geF

irm

-Lev

elP

/E0.

032

0.16

5O

nly

An

nu

alE

arn

.An

n.

0.01

40.

104

Inve

sto

rP

rote

ctio

nIn

dex

0.02

90.

176

Pct

.Da

ysN

on

-Ze

roP

rice

Ch

g.0.

014

0.08

1F

ina

nci

alM

ark

etS

op

his

tica

tion

0.02

70.

156

Ma

rke

tMo

de

lR2

0.01

40.

076

Acc

ou

ntin

gS

tan

da

rds

0.02

70.

143

Fin

an

cia

lPre

ssB

efo

reE

ven

t0.

014

0.08

3M

ark

etT

urn

ove

r/G

DP

x1

00

0.02

50.

207

Insi

de

rT

rad

ing

En

forc

ed

0.01

40.

076

Sh

are

ho

lde

rL

aw

suits

Ind

ex0.

023

0.14

0L

OT

Tra

din

gC

ost

0.01

30.

076

Pct

.In

tl.G

AA

P0.

023

0.12

7A

vera

geL

ogF

irm

Siz

e0.

013

0.09

2G

DP

pe

rC

ap

ita0.

022

0.13

1

Pane

lB:B

estM

odel

s

Set

tingf

orp =

0.15

Set

ting

forp

=0.

50(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Tech

.Dev

elo

pm

en

t0.

640.

420.

730.

580.

640.

550.

46[0

.35,

0.94

][0

.04,

0.80

][0

.43,

1.02

][0

.24,

0.94

][0

.35,

0.93

][0

.17,

0.93

][0

.11,

0.81

]In

sid

er

Tra

din

g-0

.63

-0.3

5−

0.71

−0.

64−

0.45

[ −0.

93,−

0.33

][-

0.74

,0.0

3][−

1.13

,−0.

31]

[−1.

08,−

0.20

][−

0.82

,−0.

09]

In-d

ep

thA

rtic

les

Be

fore

Eve

nt

−0.

28−

0.33

−0.

34[−

0.54

,0.0

0][−

0.57

,−0.

08]

[−0.

61,−

0.08

]F

ina

nci

alD

iscl

osu

re−

0.49

−0.

51[ −

0.90

, −0.

08]

[ −0.

93, −

0.08

]

P( γ

|Y)

0.30

40.

155

0.02

70.

025

0.00

80.

006

0.00

50.

005

(co

ntin

ue

d)

3983


ovember 15, 2011


ownloaded from



Tabl

e8

Con

tinue

d

Pan

elC

:Firm

-Lev

elM

argi

nalP

oste

rior

Pro

babi

lity

that

Top

10Va

riabl

esar

eIm

port

ant

Set

tingf

orp

0.15

0.5

0.15

0.5

Fin

an

cia

lDis

clo

sure

0.04

60.

182

Dis

clo

sure

Ind

ex0.

014

0.08

1F

ree

Pre

ss0.

024

0.17

0F

ina

nci

alM

ark

etS

op

his

tica

tion

0.01

30.

064

Insi

de

rT

rad

ing

0.02

40.

099

An

ti-S

elf-

De

alin

gIn

dex

0.01

30.

073

Inve

sto

rP

rote

ctio

nIn

dex

0.02

30.

126

Dire

cto

rL

iab

ility

Ind

ex0.

011

0.06

7Te

ch.D

eve

lop

me

nt

0.02

20.

094

Ma

rke

tMo

de

lR2

0.01

00.

056

Thi

sta

ble

cont

ains

regr

essi

ons

ofa

coun

try-

leve

lmea

sure

ofm

arke

trea

ctio

nto

new

sfo

r38

coun

trie

son

coun

try

char

acte

ristic

s.T

hem

easu

reis

the

aver

age

offo

urst

anda

rdiz

edva

riabl

esas

desc

ribed

inTa

ble7.

We

cons

ider

29ex

plan

ator

yva

riabl

es,a

llof

whi

char

est

anda

rdiz

ed.W

eus

eth

eS

toch

astic

Sea

rch

Varia

ble

Sel

ectio

n(S

SV

S)

mod

eldi

scus

sed

inTa

ble

6.P

anel

Are

port

sth

em

argi

nalp

oste

rior

prob

abili

tyth

atea

chva

riabl

eis

impo

rtan

tfor

expl

aini

ngth

eda

ta.I

nP

anel

B,w

eid

entif

yth

efo

urm

odel

sw

ithth

ehi

ghes

tpos

terio

rpr

obab

ility

from

each

ofth

etw

opr

ior

spec

ifica

tions

and

repo

rtm

ean

valu

esof

the

impo

rtan

tco

effic

ient

sfr

omth

ose

mod

eldr

aws.

The

num

bers

inbr

acke

tsar

e95

%cr

edib

lein

terv

als.

We

also

run

the

proc

edur

eat

the

firm

leve

l,w

ithth

ede

pend

ent

varia

ble

dem

eane

dby

size

and

indu

stry

port

folio

grou

ps.

We

use

U.S

.si

zete

rcile

brea

kpoi

nts

and

Fam

aan

dF

renc

h48

indu

strie

s.P

anel

Cre

port

spo

ster

ior

prob

abili

ties

for

the

top

10va

riabl

esfr

omth

efir

m-le

velr

esul

ts.A

ppen

dix

A1

cont

ains

desc

riptio

nsof

allr

egre

ssor

sco

nsid

ered

inth

eS

SV

Spr

oced

ure.

3984


ovember 15, 2011


ownloaded from



Figure 4Merger buy-and-hold abnormal returns (BHARs) as a percentage of BHARs for the entire periodThis figure shows buy-and-hold abnormal returns (BHARs) for mergers from 55 days before an announcementthrough two days after an announcement as a percent of the BHAR for the entire [-55, 2] period. The sample ofmerger announcements is collected from Bloomberg, Mergerstat, and SDC. We restrict the sample to initial bids(no bids for the target in the prior two years) and mergers where the target has at least one article written about itbetween 60 calendar days and two trading days prior to the announcement and no merger-related articles in thesame time frame. A merger event in any of the three sources is considered the same event as one from anothersource if the bids are for the same target and within two years of each other. We take the earliest announcementdate for each event from the union of all three sources. Events are divided according to whether the firm’s homecountry has an average earnings announcement reaction above or below the median for all countries with atleast 20 earnings announcements and SUEs as calculated in Figure1, Panel A. There are 293 events in the high-reaction group and 278 events in the low-reaction group. Abnormal returns are market adjusted, which meansthey are the buy-and-hold return for a stock minus the buy-and-hold return for the market. In order for an eventto be included, the stock must have 50% of trading days with price changes in the calendar year prior to theevent. The shaded region in each panel marks the [-1, 2] event window. 95% confidence intervals for the BHARsare marked with smaller, lighter circles or triangles.

reactions in Figure1. As described in the data section, we go to great extremesto ensure that the announcement date is the first public announcement datefrom three sources, and there are no news articles that hint of a pending merger.These restrictive criteria leave us with 571 merger events from 34 countries.

Figure4 plots the takeover run-up for the high- and low-earnings-reactioncountries. Although the average price run-up is similar, each group’s pricemove is scaled by the average total run-up for comparison purposes. In high-earnings-reaction countries, the trading ahead of earnings announcements isresponsible for 14% of the pre-announcement run-up by two days prior tothe first public news announcement or rumor date. In contrast, low-earnings-reaction countries have experienced 39% of their price run-up—nearly threetimes the information leakage. The difference in run-up is statistically signifi-cant, with a one-sidedp-value of 0.034. As an additional check on our findings,we relax the requirement that targets have at least one news article in the days

3985


ovember 15, 2011


ownloaded from



prior to the merger, increasing the sample to 1,301 events. The differencesare similar.25 The correlation between event-day volatility around earningsannouncements and merger announcements is 0.44, with at-statistic of 1.88despite having only 17 countries that we can use to calculate the correlation,indicating similar forces driving both events.26

6.2 Implications for reversalsNext, we seek to test the implications for reversals of Hypothesis H2C. Evi-dence in the United States has shown that there are greater reversals followingextreme returns on non-news days than on news days. We hypothesize thatmarkets with more insider trading will have less reversal following extremereturns on non-news days, because markets with more insider trading will haverelatively more informed trading than liquidity trading on non-news days. Forextreme return events, we choose daily returns that are at least two standarddeviations from the prior 250-day mean absolute return. Additionally, to ensureindependence across events, we only use extreme-return events that have noextreme return in the prior 20 days. We separate positive and negative extremereturn events according to whether the country is a high- or low-averageearnings-announcement-reaction country and examine the extent of the returnreversal in the following 20 days.

In high-reaction markets, reversals are present only after non-news days, asshown in Table9. Following news days, reversals are much smaller. Non-news-day reversals are much smaller in low-reaction markets than in high-reactionmarkets. This is prevalent for both positive and negative stock price moves. Forexample, large daily negative stock price moves in high-reaction markets arefollowed by a reversal of 1.31% in high-reaction markets, whereas the reversalis only 0.55 and statistically significantly lower in low-reaction markets. InPanel B, we scale these reversals relative to the size of the initial shock. Thepatterns are similar.27 We also examine reversals more generally across alldays and find that reversals following non-news days are much greater in high-reaction countries than in low-reaction markets (as shown in Online AppendixTables IA.12 and IA.13).

Weaker reversals in low-reaction markets also suggest that there is propor-tionally less liquidity trading in these markets and that reactions to news eventsare important for providing insight into the differences in the magnitude ofreturn reversals across markets. Both the takeover and reversal findings are

25 Theone-sidedp-value is 0.082 and is displayed in Figure IA.3.

26 To reduce noise, we require a country to have at least 10 mergers to be included in this calculation, reducing thenumber of markets to 17. When we repeat the findings in Figure IA.4, Panel B, starting with the larger sampleof 1,301 events, the correlation increases to 0.59, with at-statisticof 3.49.

27 In supplemental results (Panels B1 and B2 of Figure IA.5 and Table IA.11), we sort into three groups based onthe prior percentage of days traded as a liquidity measure. We find that the patterns of no reversals on non-newsdays for stocks in low-reaction markets are prevalent in the most liquid stock group. This is consistent with thepatterns not being driven by illiquidity.

3986


ovember 15, 2011


ownloaded from



Tabl

e9

Buy

-and

-hol

dex

cess

retu

rns

from

days

+1

to+

20fo

llow

ing

extr

eme

retu

rnev

ents

Pan

elA

:Buy

-and

-Hol

dE

xces

s Ret

urns

Ext

rem

eRet

urn

with

No

New

sN

ews

Neg

.Ret

(-)

Pos

.Ret

(+)

Neg

.Ret

(-)

Pos

.Ret

(+)

Hig

hR

eact

ion

1.31

∗−

0.91

∗−

0.33

0.35

Low

Rea

ctio

n0.

55∗

−0.

22−

0.27

0.82

∗

Dif

fere

nce

0.76

−0.

68−

0.06

−0.

48t −

test

2.0

2−

2.2

2−

0.1

2−

1.0

7p -

valu

e(0

.04)

(0.0

3)(0

.90)

(0.2

8 )

Pane

lB:B

uy-a

nd-H

old

Exc

ess

Ret

urns

asa

Per

cent

age

ofD

ayZ

ero

Ret

urns

Ext

rem

eRet

urn

with

No

New

sN

ews

Neg

.Ret

(-)

Pos

.Ret

(+)

Neg

.Ret

(-)

Pos

.Ret

(+)

Hig

hR

eact

ion

−19

.18∗

−11

.33∗

4.19

4.07

Low

Rea

ctio

n−

8.85

∗−

3.18

4.49

12.2

1∗

Dif

fere

nce

−10

.32

−8.

16−

0.31

−8.

14t −

test

−1

.80

−2

.00

−0

.04

−1

.35

p−va

lue

(0.0

7)(0

.05)

(0.9

7)(0

.18)

Thi

sta

ble

pres

ents

buy-

and-

hold

exce

ssre

turn

s(r

etur

nm

inus

the

valu

e-w

eigh

ted

coun

try

port

folio

)fo

llow

ing

days

with

extr

eme

retu

rns.

We

assi

gnda

ysto

bins

depe

ndin

gon

whe

ther

the

extr

eme

retu

rnis

posi

tive

orne

gativ

e,w

heth

erth

est

ock

isfr

oma

high

-or

low

-rea

ctio

nco

untr

y,an

dw

heth

erth

ere

isor

isno

tne

ws

for

the

stoc

kon

the

extr

eme

retu

rnda

y.A

nex

trem

ere

turn

day

isa

day

with

exce

ssre

turn

sat

leas

ttw

ost

anda

rdde

viat

ions

away

from

the

mea

nco

mpa

red

toex

cess

retu

rns

over

the

prev

ious

250

trad

ing

days

.Hig

h-an

dlo

w-r

eact

ion

coun

try

clas

sific

atio

nis

defin

edas

inF

igur

e4.W

ere

quire

alls

tock

sto

have

atle

ast

one

new

sar

ticle

inth

epr

ior

60tr

adin

gda

ysto

ensu

reth

atth

ese

are

firm

sco

vere

dby

our

new

sso

urce

and

noot

her

extr

eme

retu

rns

inth

e20

days

prio

rto

the

obse

rved

extr

eme

retu

rnev

ent

toav

oid

clus

terin

gof

the

even

ts.

Pan

elA

pres

ents

the

raw

day

+1

to+

20bu

y-an

d-ho

ldex

cess

retu

rns

follo

win

gda

ysw

ithex

trem

ere

turn

sin

perc

ent.

Pan

elB

pres

ents

the

aver

age

port

folio

day

+1

to+

20bu

yan

dho

ldex

cess

retu

rns

asa

perc

ento

fthe

aver

age

port

folio

day

zero

retu

rns.

The

diffe

renc

ein

mea

nste

sts

use

apo

oled

/unp

oole

dt-

test

whe

reth

enu

llof

equa

lave

rage

sbe

twee

nem

ergi

ngan

dde

velo

ped

mar

kets

iste

sted

.A

pool

edt-

test

isus

edw

hen

afo

lded

F-t

est

indi

cate

stha

tsam

ple

varia

nces

are

insi

gnifi

cant

lydi

ffere

ntat

the

five-

perc

ents

igni

fican

cele

vel;

othe

rwis

e,an

unpo

oled

t-te

stis

used

.∗in

dica

tess

igni

fican

ceat a

lph

a=

0.05

.

3987


ovember 15, 2011


ownloaded from



consistentwith insider trading driving stock price reactions to news acrosscountries.

7. Conclusion

Despite the fact that developed market firms have more news articles writtenabout them and more days with news coverage, market reactions to theirpublic news events are considerably stronger than for emerging markets.This is surprising given that with less news coverage, one might think thatemerging market news events should be particularly important. Additionally,even among developed or emerging markets, there are large differences inthe extent to which stock prices respond to news. We hypothesize that thedifferences could be due to the extent of public news dissemination beforethe news announcement, insider trading, the quality of the news transmissionmechanism, and the quality of accounting. The prevalence of insider tradingand, to a slightly lesser extent, the quality of news transmission play thestrongest role in explaining cross-market differences in the information contentof financial news. In a sample of mergers carefully screened for prior mergernews, we find that stock price run-up is greater prior to announcements incountries where the response to news is weaker, suggesting substantial privateinformation leakage. Similarly, in markets where public news is less importantfor stock prices, there are fewer reversals following extreme returns on non-news days, which is consistent with relatively more informed and less liquiditytrading on non-news days.

Our cross-country findings may be useful to policymakers, stock exchanges,and investors as they seek to understand differences in the extent to whichmarkets rely on public and non-public information. Even in today’s relativelysophisticated and global capital markets, there appears to be substantial insidertrading in many markets; news from the media is only a partial surprise.Additionally, since understanding the extent of news incorporation and insidertrading has implications for many other aspects of financial markets, wehope our findings will be useful for future academic research to advance ourunderstanding of cross-country differences in trading volume, liquidity, foreign(outsider) ownership, and market valuations.

3988


ovember 15, 2011


ownloaded from



Appendix A:Variable Description

Appendix Table A1Variable descriptionVariable Description

Pre-Event Public Information Disseminationln Announcement Order All U.S. firms and all firms in sample are sorted in to industry according to

Factset’s Primary SIC. All firms from Datastream are matched on ISIN andSEDOL. All firms from CRSP are matched on CUSIP. Each annual earningsannouncement date is compared to all annual earnings announcement datesfrom firms in the same Primary SIC within the prior 120 calendar days. Allannouncements are ranked from first to last and the Announcement Order isthis rank. We take the log of the order and average to the country level incross-country regressions.

Insider TradingInsider Trading The average of the 1999, 2000, and 2002-2003 GCR responses. The question

asks if “Insider trading in your country’s stock markets is (1=pervasive,7=extremely rare).” We reverse this variable so, extremely rare is 1 andpervasive is 7.

Insider Trading Enforced Dummy variable that equals one if insider trading laws exist and are enforcedas of 2008 [fromBhattacharya and Daouk(2002) and updated by authors in2008].

News TransmissionFinancial Press BeforeEvent

Log of one plus the count of articles about the firm inThe Wall Street Journal,Financial Times, Dow Jones, or Reuters in the 55 to 2 days before the event.

Financial Press DuringEvent

Log of one plus the count of articles about the firm inThe Wall Street Journal,Financial Times, Dow Jones, or Reuters in the -1 to 2 days around the event.

In-depth Articles BeforeEvent

The count of articles about the firm in the window 55 to 2 days before theevent with more than 500 words per firm mentioned.

In-depth ArticlesDuring Event

The count of articles about the firm in the window -1 to 2 days around theevent with more than 500 words per firm mentioned.

News Clustering The log ratio of one plus the number of articles per day in the window -1 to 2days around the event to one plus the number of articles per day in the window55 to 2 days before the event.

Free Press Free Pressis from the 2003, 2004, and 2006 GCR and asks if “the mediacan publish/broadcast stories of their choosing without fear of censorship orretaliation.”

Tech. Development Technological Developmentis the average of three variables: TheTechnological Availability measure from 2007, which asks, ”Towhat extent are the latest technologies available in your country?(1 = not available; 7 = widely available)”; the Technological Readinessmeasure from 2006, which asks, ”What is your country’s position intechnology relative to world leaders’? (1 = Behind; 7 = Ahead)”; andTechnological Sophistication from the 2003 GCR, which asks, ”Yourcountry’s position in technology (1=generally lags behind most countries, 7=is among the world’s leaders)”.

Accounting QualityAccounting Standards Average response to question about the strength of accounting standards.

From the 2003, 2004, 2006, and 2007 Global Competitiveness Report, where7 is high/strongly agree and 1 is low/strongly disagree on these surveys.

Financial Disclosure Financial Disclosureis from the 2005-2006 GCR and reports “The level offinancial disclosure required is extensive and detailed.”

Pct. Intl. GAAP Percent of Firms Following International Accounting Standards (IFRS) orU.S. GAAP is the percent of firms following those standards in the sampleof firms included in the country-level averages (as the dependent variable);U.S. firms are assumed to follow GAAP and Worldscope is the source for therest of the world.

Only Annual Earn. Ann. The fraction of firms in the sample that have only annual earnings announce-ments.

(continued)

3989


ovember 15, 2011


ownloaded from



Appendix Table A1ContinuedVariable Description

Firm Follows GAAPor IFRS

An indicator that is one if the firm follows International Accounting Standards(IFRS) or U.S. GAAP. Worldscope is the source for all foreign firms, and U.S.firms are assumed to follow GAAP.

Economic & Financial DevelopmentGDP per Capita ln(GDP per Capita) in 2000 constant dollars is from World Bank’s Financial

Structure Dataset developed byBeck, Demirguc-Kunt, and Levine(2000) andare annual observations averaged over 2003 to 2007.

Market Turnover/GDP x 100

Market Turnover/GDPin 2000 constant dollars is from World Bank’s Finan-cial Structure Dataset developed byBeck, Demirguc-Kunt, and Levine(2000)and are annual observations averaged over 2003 to 2007.

Financial MarketSophistication

The average measure ofFinancial Market Sophisticationfrom the 2003through 2007 Global Competitiveness Report and asks if “The level ofsophistication of financial markets is higher than international norms.”

Ln Firm’s PriorDec. USD MV

Natural log of the firm’s prior December market capitalization in U.S. dollars.

Regulatory EnvironmentShort Sales Legal Dummy variable set to 1 if short sales are not against the law (Charoenrook

and Daouk 2005).Short Sales Feasible Dummy variable set to 1 if short sales are actually used in the market

(Charoenrook and Daouk 2005).UK Law Dummy variable for whether the legal system in a country is based on

common law.Cost to Enforce Contracts Costs to enforce contracts as a percentage of debt. Average of the annual

measures from 2005 through 2008 from Doingbusiness.org.Governance

Investor Protection Rank Average of the annual measures from 2003 through 2008 from Doingbusi-ness.org.

Investor Protection Index Average of the annual measures from 2005 through 2008 from Doingbusi-ness.org.

Anti-Self-Dealing Index FromDjankov et al.(2008).Shareholder Lawsuits Index Average of the annual measures from 2005 through 2008 from Doingbusi-

ness.org.Director Liability Index Average of the annual measures from 2005 through 2008 from Doingbusi-

ness.org.Disclosure Index Average of the annual measures from 2005 through 2008 from Doingbusi-

ness.org.Trading Costs

LOT Trading Cost Computed followingLesmond et al.(1999). We use the prior year averageover firms included in the sample from 2003 through 2008.

Pct. Days Zero Price Chg. Alternate liquidity measure that is percent of days with zero price changes inthe prior year for the firms in the sample from 2003 through 2008.

Characteristics of Equity MarketsAverage Log Firm Size Natural log of the prior December firm size averaged over all firms in our

sample.Average Firm-Level P/E Average P/E ratio for firms in sample. P/E data are from Compustat for the

U.S. and Datastream and World scope for the rest of the world. Values arefrom the previous calendar year.

Market Model R2 Natural log of (R2/(1-R2)) whereR2 is the SST weighted average R2 ofsimplemarket model regressions including the local and the U.S. market foreach stock in our sample for each year from 2003 through 2008 (through 2007in the U.S.) and averaged over all years.

Country Risk Average over the period 2003-2008 of the country risk index published byEuromoney. Higher values indicate lowerrisk.

ReferencesBae, K., W. Bailey, and C. Mao. 2006. Stock Market Liberalization and the Information Environment.Journalof International Money and Finance25:404–28.

Bailey, W., G. Karolyi, and C. Salva. 2006. The Economic Consequences of Increased Disclosure: Evidencefrom International Cross-listings.Journal of Financial Economics81:175–213.

3990


ovember 15, 2011


ownloaded from



Bartram, S., G. Brown, and R.Stulz. 2011. Why Are U.S. Stocks More Volatile? Working Paper, Ohio StateUniversity.

Beck, T., A. Demirguc-Kunt, and R. Levine. 2000. A New Database on the Structure and Development of theFinancial Sector.World Bank Economic Review14:597–605.

Bekaert, G., C. Harvey, and C. Lundblad. 2007. Liquidity and Expected Returns: Lessons from EmergingMarkets.Review of Financial Studies20:1783–831.

Bhattacharya, U. 2006. Enforcement and Its Impact on Cost of Equity and Liquidity of the Market. InCanadaSteps Up: Report of the Task Force to Modernize Securities Regulation in Canada 6(Task Force to ModernizeSecurities Legislation in Canada, Thomas I.A. Allen, chairman).

Bhattacharya, U., and H. Daouk. 2002. The World Price of Insider Trading.Journal of Finance57:75–108.

Bhattacharya, U., H. Daouk, B. Jorgenson, and C. Kehr. 2000. When an Event Is Not an Event: The CuriousCase of an Emerging Market.Journal of Financial Economics55:69–101.

Bhattacharya, U., N. Galpin, X. Yu, and R. Ray. 2009. The Role of the Media in the Internet IPO Bubble.Journalof Financial and Quantitative Analysis44:657–82.

Bris, A., 2005. Do Insider Trading Laws Work?European Financial Management11:267–312.

Bushee, B., J. Core, W. Guay, and J. Wee. 2010. The Role of the Business Press as an Information Intermediary.Journal of Accounting Research48:1–19.

Chan, W. 2003. Stock Price Reaction to News and No-news: Drift and Reversal after Headlines.Journal ofFinancial Economics70:223–60.

Charoenrook, A., and H. Daouk. 2005. A Study of Market wide Short selling Restrictions. Working Paper,Cornell University.

Chipman, H., E. George, and R. McCulloch. 2001. Practical Implementation of Bayesian Model Selection. InModel Selection, ed. P. Lahiri. Beachwood, OH: Institute of Mathematical Statistics, Monograph Series, volume38:65–134.

Corrado, C. 1989. A Non parametric Test for Abnormal Security-price Performance in Event Studies.Journalof Financial Economics23:385–95.

Cremers, M. 2002. Stock Return Predictability: A Bayesian Model Selection Perspective.Review of FinancialStudies15:1223–49.

DeFond, M., M. Hung, and R. Trezevant. 2007. Investor Protection and the Information Content of AnnualEarnings Announcements: International Evidence.Journal of Accounting and Economics43:37–67.

Dimson, E. 1979. Risk Measurement When Shares Are Subject to Infrequent Trading.Journal of FinancialEconomics7:197–226.

Djankov, S., R. La Porta, F. Lopez-de-Silanes, and A. Shleifer. 2008. The Law and Economics of Self-dealing.Journal of Financial Economics88:430–65.

Dyck, A., N. Volchkova, and L. Zingales. 2008. The Corporate Governance Role of the Media: Evidence fromRussia.Journal of Finance63:1093–135.

Fang, L., and J. Peress. 2009. Media Coverage and the Cross-section of Stock Returns.Journal of Finance64:2023–52.

Fernandes, N., and M. Ferreira. 2009. Insider Trading Laws and Stock Price Informativeness.Review ofFinancial Studies22:1845–77.

Foster, G. 1981. Intra-industry Information Transfers Associated with Earnings Releases.Journal of Accountingand Economics3:201–32.

Freeman, R., and S. Tse. 1992. An Earnings Prediction Approach to Examining Intercompany InformationTransfers.Journal of Accounting and Economics15:509–23.

3991


ovember 15, 2011


ownloaded from



Gagnon, L., and G. Karolyi. 2009. Information, Trading Volume, and International Stock Return Comovements:Evidence from Cross-listed Stocks.Journal of Financial and Quantitative Analysis44:953–86.

George, E., and R. McCulloch. 1993. Variable Selection via Gibbs Sampling.Journal of the American StatisticalAssociation88:881–89.

Givoly, D., and D. Palmon. 1982. Timeliness of Annual Earnings Announcements: Some Empirical Evidence.Accounting Review57:486–508.

Griffin, J. 2002. Are the Fama and French Factors Global or Country-specific?Review of Financial Studies10:783–803.

Griffin, J., P. Kelly, and F. Nardari. 2010. Do Market Efficiency Measures Yield Correct Inferences?A Comparison of Developed and Emerging Markets.Review of Financial Studies23:3225–77.

Griffin, J., T. Shu, and S. Topaloglu. 2011. Examining the Dark Side of Financial Markets: Do Institutions Tradeon Information from Investment Bank Connections? Working Paper, University of Texas.

Hou, K. 2007. Industry Information Diffusion and the Lead-lag Effect in Stock Returns.Review of FinancialStudies20:1113–38.

Huberman, G., and T. Regev. 2001. Contagious Speculation and a Cure for Cancer: A Nonevent That MadeStock Prices Soar.Journal of Finance56:387–96.

Kelly, P. 2007. Information Efficiency and Firm-specific Return Variation. Working Paper, University of SouthFlorida.

Lang, M., K. Lins, and D. Miller. 2003. ADRs, Analysts, and Accuracy: Do ADRs Improve a Firm’s InformationEnvironment and Lower Its Cost of Capital?Journal of Accounting Research41:317–45.

Lesmond, D., J. Ogden, and C. Trzcinka. 1999. A New Estimate of Transaction Costs.Review of FinancialStudies12:1113–41.

Lesmond, D. 2005. Liquidity of Emerging Markets.Journal of Financial Economics77:411–52.

Lia, S., L. Ng, and B. Zhang. 2009. Informed Trading around the World. Working Paper, Singapore ManagementUniversity.

Patton, A., and M. Verardo. 2010. Does Beta Move with News? Firm-specific Information Flows and Learningabout Profitability. Working Paper, Duke University.

Roll, R. 1988.R2. Journal of Finance43:541–66.

Tetlock, P. 2007. Giving Content to Investor Sentiment: The Role of Media in the Stock Market.Journal ofFinance62:1139–68.

Tetlock, P., M. Saar-Tsechansky, and S. Macskassy. 2008. More Than Words: Quantifying Language to MeasureFirms’ Fundamentals.Journal of Finance63:1437–67.

Tetlock, P. 2010. Does Public Financial News Resolve Asymmetric Information?Review of Financial Studies23:3520–57.

———. 2011. All the News That’s Fit to Reprint: Do Investors React to Stale Information?Review of FinancialStudies24:1481–512.

3992


ovember 15, 2011


ownloaded from


How Important Is the Financial Media in Global...

Documents

Transcript of How Important Is the Financial Media in Global...