us analyst regulation and the earnings forecast bias around the world

European Financial Management, 2012doi: 10.1111/j.1468-036X.2012.00653.x

US Analyst Regulation and theEarnings Forecast Bias aroundthe World

Armen HovakimianZicklin School of Business, Baruch College, New York, NY 10010, USAE-mail: [email protected]

Ekkachai SaenyasiriSchool of Business, Providence College, Providence, RI 02918, USAE-mail: [email protected]

Abstract

We examine the spillover effects of the Global Analyst Research Settlement (orGlobal Settlement) on analysts’ earnings forecasts in 40 developed and emergingmarkets. Prior to the Global Settlement, analysts generally made overly optimisticforecasts, this bias tending to be higher in countries with less investor protection.This forecast bias declined significantly after passage of the Global Settlement,the spillover effect being stronger for countries with lower investor protection. Thespillover effect is also stronger for countries with a more significant presence ofthe analysts of the 12 banks directly involved in the Global Settlement.

Keywords: analyst regulation, investor protection, cross-country spillover, analystforecast bias, analyst conflicts of interest

JEL classification: G15, G24, G28, G29, G38

1. Introduction

We present evidence that recent changes in research analyst regulation in the USAhave led to legal spillovers affecting analyst behaviour in 40 developed and emergingmarkets around the world. We further document the role of investor protection and the12 banks directly involved in the Global Analyst Research Settlement (hereafter GlobalSettlement) in the spillover effect.

A vast literature on finance, accounting, and law documents how the extent of investorprotection and financial disclosure in a country affect its financial market development

We would like to thank John Doukas (the editor of this issue) and an anonymous refereefor valuable suggestions. We also thank Donal Byard, Terrence Martell, Oghenovo Obrimah,Joseph Weintrop, and seminar participants at Baruch College for helpful comments.

C© 2012 Blackwell Publishing Ltd

2 Armen Hovakimian and Ekkachai Saenyasiri

and the behaviour of market participants (e.g., La Porta et al., henceforth LLSV, 1998).The literature finds that in countries with legal environments that are protective of outsideinvestors’ rights, financial markets are characterised by higher analyst coverage, higherforecast accuracy, and higher firm valuations (Bushman et al., 2005; Hope, 2003; Kwag,2006; LLSV, 2002). The literature also finds that higher accounting disclosure levelslead to higher analyst forecast accuracy and lower the optimistic bias (Higgins, 1998).

Another strand of the literature examines how private transactions allow firms fromcountries with poor investor protection and less transparent accounting disclosure toovercome the shortcomings of their home markets. For example, Stulz (1999) arguesthat firms that choose to be subjected to more stringent US regulations via cross-listingcan improve their informational environment and reduce their cost of capital. Foerster andKarolyi (1999) and Miller (1999) provide evidence consistent with these arguments, andLang et al. (2003) report that cross-listings in the USA lead to greater analyst coverageand increased forecast accuracy. Similarly, cross-border mergers and acquisitions havebeen shown to result in governance and legal spillovers affecting shareholder value (e.g.,Bris et al., 2008; Goergen and Renneboog, 2004).

Despite the abundance of research, little attention has been devoted to the analysisof a related issue: how regulatory changes in one country can influence the financialmarkets and market participants in others. This paper provides evidence that fills thisgap. We specifically focus on the optimistic bias in analyst forecasts that has been linkedby earlier research to conflicts of interest. These conflicts of interest arise when sell-side analyst compensation is tied to profits generated from investment banking businessand brokerage commissions, giving analysts incentives to generate research reportsand inflated earnings forecasts that do not reflect their true opinions (e.g., Bessler andStanzel, 2009; Carleton et al., 1998; Lin and McNichols, 1998).

In late 2002 to early 2003, US regulators introduced several new rules and regulations,prosecuted analysts whose research reports were tainted by conflicts of interest, and finedbanks that failed to adequately address their research analysts’ conflicts of interest. Oneof the main regulatory developments during this period was the Global Settlement, anenforcement agreement between US regulators and 12 large investment banks (hereafterreferred to as the Big 12 banks) designed to eliminate research analysts’ conflicts ofinterest. We use the term Global Settlement to refer to this and other contemporaneousregulatory actions aimed at curtailing such conflicts of interest.1

While the main motivation for these regulatory efforts was to restore public confidencein the US capital markets, the main objective of this paper is to document their impacton the forecasts of analysts operating outside the USA.2 These US rules and regulationscan influence analyst behaviour in foreign capital markets through several mechanisms.

1 The Global Settlement was announced on 20 December, 2002. Provisions of the NationalAssociation of Securities Dealers (NASD) Rule 2711 and New York Stock Exchange (NYSE)Rule 472 went into effect in July 2002, whereas Regulation Analyst Certification was adoptedby the US Securities and Exchange Commission (SEC) in February 2003. Because theseregulatory actions were introduced over a relatively short period, it is not possible to determinetheir independent impacts. Importantly, however, all of these rules and regulations share thesame goal of reducing analysts’ conflicts of interest.2 Hovakimian and Saenyasiri (2010) examine the impact of the Global Settlement on USanalysts’ forecast bias and find that the bias practically disappears following its passage. Theauthors conclude that the Global Settlement has significantly mitigated analysts’ conflicts ofinterest in the USA.


US Analyst Regulation and the Earnings Forecast Bias around the World 3

First, public statements by the 12 multinational banks suggest that at least some of themhave decided to voluntarily apply the terms of the Global Settlement to all of their globaloperations. In addition, US regulators have mandated that banks apply certain new rules,such as NASD Rule 2711, globally. Finally, the improvements in analyst regulation inthe USA have prompted some foreign regulators to introduce similar rules.

The Global Settlement and related regulations were preceded by Regulation FairDisclosure (Reg FD), which was introduced in October 2000. One of the stated goals ofReg FD is to give market participants equal access to information by prohibiting privatecommunications between firms and analysts. This eliminates the incentive for analyststo inflate their earnings forecasts to gain privileged access to insider information (Lim,2001). Consistent with this argument, Cornett et al. (2007) find that the differencesin the reactions to the recommendations of affiliated and unaffiliated analysts havedisappeared since the passage of Reg FD. Because Reg FD explicitly excludes foreignfirms from its requirements, it is unlikely to impact the forecasts of foreign firms’earnings. Nevertheless, since Reg FD has been shown to affect analyst bias in the USA,it is accounted for in our analyses.

Consistent with prior studies, our results show that analysts’ earnings forecasts wereoverly optimistic worldwide in the period prior to Reg FD:3 The forecasts of current-year earnings show positive bias in all 40 countries and the longer-term forecasts offollowing-year earnings are even more biased. This pattern holds in each of the fortycountries in our sample, providing robust evidence of a walk-down trend in internationalearnings forecasts.4

The impact of Reg FD on forecast bias in foreign markets is insignificant, as expected.Importantly, however, we observe a statistically and economically significant decline inthe forecast bias following the Global Settlement. The mean bias in the forecasts ofcurrent-year earnings declined in 34 of 40 countries, with the overall mean droppingfrom 0.94% of the stock price prior to Reg FD to 0.47% after the Global Settlement.The mean bias in forecasts of following-year earnings declined in 32 countries, with theoverall mean dropping from 2.37% prior to Reg FD to 1.60% after the Global Settlement.When we exclude the years of the global financial crisis and the following recession(2007–2010), the mean bias declined in 36 of 40 countries for current-year earningsand in 39 of 40 countries for following-year earnings. These changes are significant notonly statistically but also economically and imply that the spillover effects of the GlobalSettlement on analyst earnings forecasts around the world are strong and pervasive.

The countries in our sample show considerable variation in their legal origins,governance structures, and accounting standards, implying high variation in the degreeof investor protection and allowing us to examine how investor protection affects analystforecast bias. Prior studies report that analyst coverage and forecast accuracy are higherin countries with better investor protection (e.g., Bushman et al., 2005; Hope, 2003).Prior studies also find that improvements in investor protection tend to have a strongerimpact in countries with lower initial investor protection.

3 In an earlier study, Basu et al. (1998) document a positive forecast bias for 10 developedcountries. Capstaff et al. (2001) document a positive forecast bias for nine European countries.4 The walk-down trend refers to the decline in forecast bias with shortening of the forecasthorizon, documented in the USA by Richardson et al. (2004). Capstaff et al. (2001) provideevidence of a walk-down trend in nine European countries.



For example, Bushman et al. (2005) find that the improved enforcement of insidertrading rules has led to increased analyst coverage, especially in countries with weakerinvestor protection. Similarly, Lang et al. (2003) find that cross-listings in the USA haveled to greater analyst coverage and increased forecast accuracy, especially for firms fromcountries with less-developed financial markets. Hail and Leuz (2009) document that themagnitude of the reduction in the cost of capital of firms cross-listed on US exchangesis higher for firms from countries with less investor protection.

Based on these prior studies and following the traditional arguments of the law andfinance literature, we hypothesise that analyst forecast bias should be higher in countrieswith weaker investor protection. Furthermore, following the Global Settlement, weexpect the forecast bias in these countries to have declined more than in countries withstronger investor protection.

Our findings support these hypotheses. We find that, before the Global Settlement,the forecast bias was higher in countries of civil law tradition, which the literatureassociates with weaker investor protection. However, these countries have experiencedmore significant reductions in analyst forecast bias since the Global Settlement. Indeed,the spillover effects of the Global Settlement have been so strong in these countriesthat the disadvantages of weaker investor protection are no longer evident in analystforecasts observed in the post-settlement period. These findings are robust since they donot change when we use alternative measures of investor protection.

We further find that the spillover effects have been stronger in countries with a moresignificant presence of Big 12 bank analysts. Specifically, although the forecasts ofanalysts working for Big 12 banks are neither more nor less overoptimistic than theforecasts of other analysts, the decline in the forecast bias since the Global Settlementhas been larger in countries with more Big 12 analyst presence. These results suggestthat the US regulatory mandates and the investment banks’ voluntary decisions to applythe new rules on a global basis are important factors in the spillover mechanism.

To summarise, the paper makes two important contributions to the literature. First, itdocuments new empirical evidence on how recent changes in analyst regulation in theUSA have affected analyst behaviour in other countries. Second, the paper highlights therole of multinational firms in transmitting the effects of national regulations far beyondnational borders.

The remainder of the paper is organised as follows. Section 2 discusses why analystsmake overly optimistic forecasts and how new regulations can affect these forecasts.Section 3 describes our sample. Section 4 examines the impact of the Global Settlementon analyst forecasts in international markets. Section 5 examines how the impact ofthe Global Settlement varies with investor protection regime. Section 6 examines therole of Big 12 bank analysts. Section 7 checks the robustness of the results. Section 8summarises our conclusions.

2. Institutional Background and Hypotheses

2.1 Analyst overoptimism

Prior studies find that analysts in the USA tend to make overly optimistic earningsforecasts (e.g., Brown, 1997; Chopra, 1998; O’Brien, 1988). The optimistic bias tendsto be larger for longer-term forecasts and to decline as forecasts are made closer to theearnings announcement date, resulting in what is referred to as the walk-down trend inanalysts’ earnings forecasts (Richardson et al., 2004).



A number of studies document that the optimistic bias is not limited to US analysts’earnings forecasts. Basu et al. (1998) show the existence of forecast bias in 10 countrieswith relatively large stock markets, whereas Capstaff et al. (2001) provide evidence offorecast bias and a walk-down trend in nine European countries.

Although several explanations have been offered for analyst overoptimism, empiricalevidence is most consistent with the view that analysts issue inflated earnings forecastsbecause of their need for insider information and conflicts of interest that arise whentheir compensation is tied to investment banking fees and brokerage commissions (e.g.,Lim, 2001; Carleton et al., 1998; Jackson, 2005; Lin and McNichols, 1998; Bessler andStanzel, 2009).

2.2 Recent US regulations and their impact on earnings forecasts for US firms

Prior to Reg FD, analysts and institutional investors often had an informationaladvantage over small investors through private communications with management andclosed conference calls where firms regularly discussed past performance and providedguidance on future prospects. To gain access to private information flow, analysts had tomaintain good relations with insiders by inflating forecasts and buy recommendations intheir research reports. To improve fairness and regain public confidence in the markets,in October of 2000, the SEC introduced Reg FD, which required US public firms tosimultaneously disclose material information to all market participants. Following RegFD, analysts covering US firms became less dependent on insider information andstarted to rely more on information disclosed via public communication channels, suchas earnings guidance (Charoenrook and Lewis, 2009). Thus, Reg FD diminished one ofthe analysts’ motives to inflate their forecasts and reduced their forecast bias (Choi et al.,2009; de Jong and Apilado, 2009; Herrmann et al., 2008; Hovakimian and Saenyasiri,2010).

However, other sources of conflicts of interest remained unaddressed by Reg FD.For instance, analysts could be pressured to make optimistic forecasts and buyrecommendations to favor investment banking clients (Bessler and Stanzel, 2009; Linand McNichols, 1998; Michaely and Womack, 1999), generate trading volume (Jackson,2005; Niehaus and Zhang, 2010), or help the performance of stocks held by affiliatedmutual funds (Guidolin and Mola, 2009). These conflicts of interest were subsequentlyaddressed by the Sarbanes–Oxley Act of 2002 (SOX); new rules issued by the SEC,NASD, and NYSE; and the Global Settlement.

Signed into law on 30 July 2002, SOX is broad legislation covering various businesspractices such as auditor independence, corporate responsibility, enhanced financialdisclosure, analysts’ conflicts of interest, and corporate and criminal fraud accountability.It amended the Securities and Exchange Act of 1934 by creating Section 15D, whichrequires the NASD and NYSE to adopt rules reasonably designed to address researchanalysts’ conflicts of interest.

To comply with SOX, the NASD released Rule 2711 (Research Analysts andResearch Report) and the NYSE amended its Rule 351 (Reporting Requirement)and Rule 472 (Communication with the Public). Most of these rules’ provisionswent into effect 9 July 2002. These rules mitigate analysts’ conflicts of interest byseparating research analysts from the influence of investment banking and brokeragebusinesses. Analysts’ compensation can no longer be tied to the performance of thesebusinesses. In addition, analysts are restricted from personal trading on the stocks theycover.



On 6 February 2003, the SEC adopted Regulation Analysts Certification (Reg AC),providing guidelines for the proper disclosure of potential conflicts of interest of sell-sideanalysts, including their association with investment banking clients and the structure oftheir compensation. This regulation took effect on 14 April 2003.

Regulatory objectives have also received support from rigorous enforcement actions.Following joint investigations by the SEC, NASD, NYSE, and the New York StateAttorney General, 10 large US and multinational investment banks agreed to pay afine totaling $1.435 billion in the Global Analyst Research Settlement for their failureto adequately address research analysts’ conflicts of interest. The terms of the GlobalSettlement were announced on 20 December 2002, and initially covered only those 10banks.5 The final agreement was announced on April 28, 2003. Two more banks reachedsettlements on August 26, 2004.6 The Global Settlement, the NASD/NYSE rules, andthe SEC regulations share the same spirit, in that their mutual objective is to eliminateanalysts’ conflicts of interest.

Prior studies of the impact of these rules and regulations on analysts’ conflicts ofinterest in the USA reveal that analysts’ stock recommendations and earnings forecastsbecame less optimistic after the Global Settlement (e.g., Barber et al., 2006; Hovakimianand Saenyasiri, 2010; Kadan et al., 2009). Overall, these results imply that the GlobalSettlement and related rules have reduced analysts’ conflicts of interest.

2.3 The international impact of recent US regulations

The impact of Reg FD on analysts’ forecasts for foreign stocks is unlikely to besignificant; the regulation prohibits the private disclosure of information by publiclytraded firms and thus regulates firms rather than analysts. Furthermore, Rule 243.101(b)of Reg FD explicitly excludes foreign issuers from its requirements, even whentheir securities, such as American Depositary Receipts, trade on the US exchanges.Nevertheless, Reg FD is a significant piece of regulation that has been shown to affectanalyst bias in the USA and, as such, is accounted for in our analyses.

In contrast, we expect the Global Settlement and related regulations to affect forecastsmade for stocks traded in foreign markets. Large multinational banks employ analystsand research groups that contribute to equity analysis in many different countries. TheNASD requires all of its members – that is, all securities firms doing business with theUS public – and members’ employees, including foreign associates, to comply with itsnew rules.7 In its Notice to Members 05–24, NASD states,

NASD has observed that members with global operations sometimes produce researchreports under a single global brand name or jointly with a research analyst employedby a non-member affiliate – i.e., a ‘mixed team’ research report. NASD and NYSE

5 The 10 investment banks are Bear Stearns, Citigroup, Credit Suisse First Boston, GoldmanSachs, J.P. Morgan, Lehman Brothers, Morgan Stanley, Merrill Lynch, UBS, and US BancorpPiper Jaffray.6 These two investment banks are Deutsche Bank and Thomas Weisel Partners.7 In NASD Rule 1100, foreign associates are defined as persons associated with an NASDmember that meet the following criteria: (1) They are not citizens, nationals, or residents ofthe USA and (2) they conduct all of their securities activities outside the USA and will notengage in any securities activities with or for any citizen, national, or resident of the USA.



have deemed such research reports to be attributable to the member and thereforesubject to the applicable requirements of Rule 2711.

Although the Global Settlement directly affects only research reports for US stocks,its impact is likely to extend to the Big 12 banks’ foreign operations. Specifically, publicstatements made by these banks suggest that they voluntarily apply the agreement totheir operations abroad. For example, in September of 2007, GoldmanSachs describedits policy for investment research on its website as follows:

The firm is subject to a ‘Global Research Settlement’ entered by a United StatesDistrict Court on October 31, 2003. Whilst not required to do so, the firm has appliedthe terms on a global basis, subject to limited variations in response to local marketpractices outside the United States.

The improvements in conflicts of interest regulation in the USA are also likely to havean indirect impact on analysts working for local banks in foreign countries since theseimprovements have prompted foreign regulators to consider introducing similar rules.In particular, the regulators in Australia, Canada, Japan, the UK, and Hong Kong haveimplemented new rules designed to curb analysts’ conflicts of interest (NASD, NYSE,2005; Bolland, 2007, p. 131). The International Organization of Securities Commissionsand the European Union Forum Group also addressed analysts’ conflicts of interest intheir Market Abuse Directive (MAD).

2.4 Cross-country implications

The degree to which conflicts of interest affect analyst behaviour is likely to vary acrosscountries with the degree of outside investor legal protection. Our primary measureof investor protection is the country’s legal origin. In addition to legal origin, we usethe anti-director rights index and accounting standards index as alternative proxies forinvestor protection that allow us to ensure the robustness of our results.8 The anti-directorrights index ranges from zero to five and measures minority shareholder rights.9 Theaccounting standards index ranges from zero to 90, with higher values implying moretransparency.10

8 The source of our data on investor protection is LLSV (1998).9 The anti-director rights index represents the number of provisions protecting minorityshareholder rights in each country. The set of considered provisions is as follows: (1) Thecountry allows shareholders to mail their proxy vote, (2) shareholders are not required todeposit their shares prior to the general shareholders’ meeting, (3) cumulative voting isallowed, (4) an oppressed minorities mechanism is in place, and (5) the minimum percentageof share capital that entitles a shareholder to call for an extraordinary shareholders’ meetingis less than or equal to 10%.10 The accounting standards index consists of the Center for International Financial Analysisand Research (CIFAR) disclosure scores used in prior studies (e.g., Hope, 2003; Khannaet al., 2004; LLSV, 1998; Rajan and Zingales, 1998). The index is based on the inclusion oromission of 90 items in the 1990 annual report of firms in each country.



The traditional view in the literature is that legal institutions in common law countriesprovide greater investor protection because, under the common law, judges have theflexibility to apply general principles such as fiduciary duty and fairness to new situations(Johnson et al., 2000). In contrast, under the civil law system, judges cannot go beyondthe laws passed by the legislatures. As a result, corporate insiders can expropriate outsideinvestors as long as the insiders’ actions are not explicitly banned by any statutes.

Based on these arguments, we expect that countries with better investor protectionshould be able to mitigate analysts’ conflicts of interest better than countries with lessinvestor protection. Consequently, greater investor protection should be associated with alower forecast bias prior to the Global Settlement. Furthermore, if the Global Settlementcan mitigate analysts’ conflicts of interest and thereby improve investor protection, weexpect its impact to be stronger in countries with lower investor protection since theseallow for more room for improvement.

3. Sample and Variables

We examine analyst forecasts in 40 countries for which LLSV (1998) provide informationon investor protection variables such as legal origin, the anti-director rights index, andthe accounting standards index and for which we can obtain analyst forecasts and grossdomestic product (GDP) growth rates.11 The list of the sample countries, along with thevalues of the corresponding investor protection variables, is presented in Table 1. A totalof 28 countries in our sample are part of the civil law tradition and 12 are part of thecommon law tradition.

Sell-side analysts’ earnings forecasts for fiscal year-end dates between 1991and 2010are rom the Detail file of the I/B/E/S database, which contains forecasts for both USand international firms.12 We use forecasts for current- and following-year earningsper share (EPS) made for the upcoming and following years’ earnings announcementdates.13

For each forecast, I/B/E/S provides the actual earnings, forecast date, forecast period(fiscal year) end, analyst identity code, and broker identity code. We use the BrokerTranslation File from I/B/E/S to convert broker codes into brokers’ names, which arethen used to identify analysts who work for the Big 12 banks. Stock prices are from theI/B/E/S Summary file.14 The GDP growth rates are from Trading Economics.

The focus of our analysis is on country-level mean forecast bias, calculated for eachcountry i in each forecast month m of each fiscal year t across all firms j in the country

11 Data on 49 countries are from LLSV (1998). We drop US forecasts since our focus is theimpact of US regulation on other countries. Four countries (Kenya, Zimbabwe, Ecuador, andUruguay) have no analyst forecasts in the Institutional Brokers’ Estimate System (I/B/E/S).Four more countries (Nigeria, Pakistan, Sri Lanka, and Jordan) have too few forecasts.12 We exclude forecasts contained in I/B/E/S’ excluded estimates file and forecasts for whichactual earnings figures are missing.13 Forecasts for current-year EPS are the I/B/E/S forecasts with code FPI1. Forecasts forfollowing-year EPS are the I/B/E/S forecasts with code FPI2.14 The I/B/E/S’ summary files contain snapshots of consensus-level data and correspondingstock prices taken on a monthly basis. The snapshots are as of the Thursday before the thirdFriday of every month. The reported stock price in this file is the last available price prior tothat Thursday.



Table 1

Investor protection in each country.

This table presents investor protection in each country. The common law indicator equals one forcommon law countries and zero for civil law countries. The anti-director rights index ranges from zeroto five, with higher numbers reflecting better protection of minority shareholder rights. The accountingstandards index varies between zero and 90, with higher values implying more transparency.

Accounting Number of country-Common Anti-director standards month

Country Law index index observations

Civil law countriesArgentina 0 4 45 293Austria 0 2 54 462Belgium 0 0 61 474Brazil 0 3 54 358Chile 0 5 52 375Colombia 0 3 50 142Denmark 0 2 62 509Egypt 0 2 24 173Finland 0 3 77 409France 0 3 69 508Germany 0 1 62 475Greece 0 2 55 331Indonesia 0 2 446Italy 0 1 62 413Japan 0 4 65 479Korea 0 2 62 430Mexico 0 1 60 340Netherlands 0 2 64 523Norway 0 4 74 464Peru 0 3 38 307Philippines 0 3 65 437Portugal 0 3 36 348Spain 0 4 64 496Sweden 0 3 83 449Switzerland 0 2 68 449Taiwan 0 3 65 398Turkey 0 2 51 302Venezuela 0 1 40 96

Common law countriesAustralia 1 4 75 520Canada 1 5 74 499Hong Kong 1 5 69 507India 1 5 57 451Ireland 1 4 473Is rael 1 3 64 307Malaysia 1 4 76 516New Zealand 1 4 70 499Singapore 1 4 78 520South Africa 1 5 70 452Thailand 1 2 64 488United Kingdom 1 5 78 519

Total 16,637



as follows:15

BIASi,t,m = 1

Ji

∑

j

100∗(Fi, j,t,m − Ai, j,t )/Pi, j,t−1, (1)

where

Fi, j,t,m = 1

N j

∑

n

Fi, j,t,m,n (2)

and Ai,j,t is the annual EPS of firm j from country i realized in fiscal year t, Pi,j,t−1 is thestock price of firm j one year before the end of fiscal year t, Fi,j,t,m is the firm-level meanearnings forecast based on Nj analysts making forecasts for firm j in month m relativeto the end of fiscal year t, and Fi,j,t,m,n is the mean of earnings forecasts made in monthm by analyst n for fiscal year t of firm j.16 Our calculations according to Equations(1) and (2) use only new forecasts made in month m. Stale forecasts in earlier months(m – 1, e.g.) are not carried over into month m.

Brown (2001) documents that forecasting is more difficult when firms report a lossor a decline in earnings. As a result, forecast bias may be due to a rapid decline in actualearnings, especially during a recession. This type of unusually high forecast bias is not aresult of conflicts of interest. We therefore drop forecasts during periods in which annualGDP growth rates for the fiscal year are negative.

To minimise the influence of outliers and misreported data, our analysis excludes theforecasts in the top and bottom 1% of the distribution tails. Observations with missingvalues of any relevant variable are dropped from the sample. For each country andfiscal year, the sample contains up to 25 monthly observations of forecast bias, yielding16,637 country–month observations representing 40 countries and covering the fiscalyears between 1991and 2010.

4. The Global Settlement and Forecast Bias

Table 2 summarises the analyst forecast bias at the country level before Reg FD, betweenReg FD and the Global Settlement, and after the Global Settlement. The forecasts madeon or after 23 October 2000, are considered to be made after Reg FD. The forecasts madeon or after 20 December 2002, are considered to be made after the Global Settlement.

Because the prior literature indicates that the analyst forecast bias tends to be largerfor longer-term forecasts and declines as the forecast horizon becomes shorter, we reportthe median forecast bias separately for forecasts made in months –11 through +1 relativeto the fiscal year-end and for forecasts made in months −23 through −12.

The last row in Table 2 shows that, prior to Reg FD, the mean forecast bias in oursample is 0.94% of the stock price for forecasts made in months –11 through +1 (short-term forecasts), and 2.37% for forecasts made in months −23 through −12 (long-termforecasts). Indeed, the forecasts are positively biased in each of the 40 countries in oursample and in each country the bias is higher for longer-term forecasts. Thus, both theoveroptimism in analyst earnings forecasts and the pattern of its increase with forecast

15 We also present the results using the median forecast bias, which is defined similarly.16 It is common to use stock price to normalize the forecast bias (e.g., Lim, 2001; Richardsonet al., 2004).



Table 2

Forecast bias by country and regulatory period.

This table presents the forecast bias by country and regulatory period. The mean forecast bias is definedas the mean difference between the forecast and realised EPS, scaled by stock price and multipliedby 100. Here Before Reg FD indicates forecasts made before 23 October 2000; Between Reg FD andGS indicates forecasts made between 23 October 2000, and 20 December 2002; After GS indicatesforecasts made after December 20, 2002. The sample covers forecasts made in 1989–2010 for the fiscalyears ending in 1991–2010.

Mean forecast bias Mean forecast biasShort-term forecast: Long-term forecasts:Month −11 to +1 Month −23 to −12

Between BetweenBefore Reg FD After Before Reg FD AfterReg FD and GS GS Change Reg FD and GS GS Change

(1) (2) (3) (3) – (1) (1) (2) (3) (3) – (1)

Civil law countriesArgentina 2.31 – 0.87 −1.44 4.57 −1.92 2.74 −1.83Austria 1.02 1.23 0.34 −0.68 2.76 3.88 −0.09 −2.84Belgium 0.75 2.02 −0.09 −0.85 2.90 3.05 −0.11 −3.01Brazil 2.19 4.85 0.94 −1.25 4.56 6.23 1.95 −2.62Chile 1.12 2.29 0.17 −0.95 2.72 4.35 0.42 −2.30Colombia 2.66 5.38 −0.64 −3.30 4.52 3.21 −0.88 −5.40Denmark 1.46 3.60 0.06 −1.40 2.50 2.59 0.40 −2.10Egypt 0.41 2.44 −0.04 −0.45 2.12 9.86 0.87 −1.24Finland 1.52 1.83 0.34 −1.18 3.83 5.00 0.88 −2.95France 2.02 1.76 −0.06 −2.08 3.91 4.72 0.15 −3.76Germany 0.73 2.03 0.51 −0.22 1.86 7.11 0.83 −1.03Greece 0.21 0.97 0.43 0.23 0.55 2.65 0.82 0.27Indonesia 0.79 4.31 0.98 0.19 1.98 7.52 1.14 −0.84Italy 1.24 3.05 0.22 −1.02 2.79 3.48 0.12 −2.67Japan 0.55 0.98 0.24 −0.31 1.35 2.23 1.37 0.02Korea 2.57 8.83 0.96 −1.61 3.67 15.84 1.56 −2.11Mexico 2.99 4.89 0.51 −2.48 3.42 6.91 −0.51 −3.94Netherlands 0.99 2.84 0.19 −0.80 2.77 6.20 0.51 −2.26Norway 1.45 2.37 1.99 0.54 3.23 5.34 4.46 1.23Peru 2.98 2.55 −0.72 −3.70 6.30 4.55 −0.31 −6.61Philippines 1.25 0.52 −0.06 −1.31 3.37 −0.08 0.52 −2.85Portugal 1.07 1.32 −0.21 −1.28 2.87 2.10 −0.18 −3.06Spain 1.85 0.95 −0.08 −1.93 3.00 1.89 −0.52 −3.52Sweden 0.68 1.81 0.27 −0.40 1.97 5.57 0.58 −1.39Switzerland 0.19 1.80 0.25 0.06 0.95 4.67 1.59 0.63Taiwan 0.56 1.30 0.10 −0.47 1.46 3.92 1.44 −0.02Turkey 3.45 2.43 1.36 −2.09 4.26 4.54 2.13 −2.13Venezuela 4.00 1.72 −0.70 −4.70 7.33 1.44 −0.19 −7.52

Common law countriesAustralia 0.87 2.00 0.79 −0.08 2.28 2.17 2.44 0.16Canada 1.37 1.95 1.55 0.19 3.44 4.01 2.50 −0.94Hong Kong 1.02 1.87 0.33 −0.69 2.25 3.79 1.48 −0.77India 0.60 0.63 −0.17 −0.77 3.25 1.32 0.74 −2.51Ireland 0.49 0.02 −0.08 −0.57 1.91 1.22 −0.13 −2.04Israel 0.90 1.61 0.51 −0.39 1.65 6.93 2.62 0.98Malaysia 0.28 0.54 0.55 0.27 1.06 1.71 2.36 1.30



Table 2

Continued.



(1) (2) (3) (3) – (1) (1) (2) (3) (3) – (1)

New Zealand 1.15 2.41 0.98 −0.17 2.81 3.76 2.25 −0.56Singapore 0.78 1.31 0.22 −0.56 1.37 3.05 1.73 0.36South Africa 0.70 1.76 0.66 −0.04 2.36 3.36 1.39 −0.97Thailand 2.41 2.30 0.19 −2.22 4.06 1.08 1.40 −2.66United Kingdom 0.68 0.98 0.07 −0.61 1.96 2.25 0.45 −1.51

All countries 0.94 1.45 0.47 −0.47 2.37 2.89 1.60 −0.76

horizon (the walk-down trend) are not limited to the USA and most developed countriesbut are evident worldwide.

Since Reg FD, forecast bias appears to have increased. The mean short-term forecastbias in the period between Reg FD and the Global Settlement is 1.45% whereas themean long-term forecast bias is 2.89%. Both of these numbers are higher than thecorresponding numbers for the pre-Reg FD period. Hovakimian and Saenyasiri (2010)report, however, that a similar increase in the forecast bias in the USA was driven byunusually poor macroeconomic conditions during that period. The same could be truefor international forecasts as well. We control for macroeconomic conditions when weexamine the forecast bias in the multiple regression framework in Section 5.

The forecast bias has declined sharply since the Global Settlement, with the meanforecast bias at 0.47% of the stock price for forecasts made in months –11 through +1and at 1.60% for forecasts made in months −23 through −12. The levels of forecast biassince the Global Settlement are lower than those before Reg FD in 32 of 40 countries forlong-term forecasts and in 34 of 40 countries for short-term forecasts. When comparedto the period between Reg FD and the Global Settlement, the post-settlement bias islower in all but one country for short-term forecasts, and in all but five countries forlong-term forecasts.

Overall, the pattern of forecast bias in Figure 2 confirms that analysts around theworld generally made overly optimistic forecasts in almost every year prior to the GlobalSettlement. After the Global Settlement, the forecast bias has been consistently low, bothin and outside the USA, except for the spike in 2008–2009, when a drop in the realizedEPS due to the recession was much more dramatic than the drop in the forecasts.

To summarise, the impact of the Global Settlement on analyst earnings forecastsaround the world has been strong and pervasive.

5. The Global Settlement and the Forecast Bias: the Impact of Investor Protection

5.1 Univariate analysis

Panel A of Table 3 presents the mean forecast bias for countries categorized into highand low investor protection groups based on legal origin. Consistent with the notion that



Earningsannouncement date (EAD)

Fiscal year end (t) Fiscal year end (t-1)

orForecast period

end date

Year in which therealized EPS is calculated

Stock price (Pt-1)

Fiscal year end (t-2)

Earnings forecasts can be made at any day before EAD

1+htnoM0htnoM11-htnoM32-htnoM

Fig. 1. Timeline of analyst forecasts.

This figure illustrates the timeline of analyst forecasts. The earliest analyst forecasts for a specificfiscal year-end EPS are made 24 months before the fiscal year-end date (in forecast month −23). Foreach EPS, analysts can make multiple forecasts over the course of the next 24 months. Some analystsmay continue to make forecasts after the forecast fiscal year ends, since firms announce their annualearnings with a delay of several months. Since the length of the EPS announcement delay can beaffected by how high or low the realised EPS is relative to the consensus, we retain only forecasts madeno more than one month after the forecast fiscal year-end (in forecast month +1).

higher investor protection mitigates analysts’ conflicts of interest, the results show that,prior to the Global Settlement (both before and after Reg FD), countries of common laworigin had a lower forecast bias compared to countries of civil law origin. The differencesare statistically significant at the 1% level for both long-term and short-term forecasts.

Panel A also shows that the forecast bias increased somewhat following Reg FD butdeclined sharply after the Global Settlement. The mean change in the forecast bias fromthe pre-Reg FD period to the post-Global Settlement period is negative for both civillaw and common law countries, but more negative for civil law countries, consistentwith our expectations. These differences are statistically significant at 1% for both long-term and short-term forecasts. Furthermore, since the Global Settlement, the differencebetween the levels of short-term forecasts in common and civil law countries has becomestatistically insignificant, whereas for long-term forecasts the bias in civil law countrieshas become significantly lower.

As a robustness check, in Panel B of Table 3, we examine the median forecast bias,which is less susceptible to outlier effects. Although the median bias tends to be smallerin magnitude, the general patterns for the median country bias are similar to those for themean bias shown in Panel A of Table 3. Similar to the findings of Abarbanell and Lehavy(2003) for their US sample, the forecast bias distribution in the foreign markets inferredfrom Table 3 is right skewed. However, the significant reductions in both the mean andmedian forecast bias since the Global Settlement imply that these changes are mainlydue to a shift of the whole forecast bias distribution, and not just the disappearance ofextremely biased forecasts.

It is possible that the results in Table 3 are driven by analysts’ failure to anticipaterobust world economic growth between 2003 and 2007, and not a reduction in theirconflicts of interest. This may be why we observe a negative median of forecast bias



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Fig. 2. Time series of the mean forecast bias, forecast level and realised earnings.

This figure presents the time series of the mean forecast bias, forecast level, and realised earnings. Themean forecast bias is defined as the difference between the mean of the forecasts made in months –11through +1 relative to the fiscal year-end and the realised EPS, scaled by stock price and multipliedby 100. The sample covers forecasts made in 1990–2010 for the fiscal years ending in 1991–2010.

after the Global Settlement. To address this issue, our subsequent analysis uses theGDP growth rate to control for analysts’ inability to accurately forecast earnings if theeconomy changes substantially.

To summarise, the spillover effects of the Global Settlement are significantly strongerin countries with lower investor protection. Indeed, they are so much stronger that thedisadvantages of weaker investor protection are no longer evident in forecasts madesince the Global Settlement. This result is also evident in Figure 3, which presents thetime series of the mean bias by legal origin.

5.2 Regression analysis

As discussed earlier, the differences in forecast bias across the three sub-periods may bedue to variations in macroeconomic conditions. This section examines the impact of USanalyst regulation on forecast bias in foreign countries while controlling for differencesin macroeconomic conditions and investor protection using the following regressionmodel:

BIASi,m,t = α0 + α1 F Dm,t + α2GSm,t + α3Protectioni + α4(Protectioni × F Dm,t )

+ α5(Protectioni × GSm,t ) + α6GDPi,t + α7Monthm + εi,m,t + νi . (3)



Table 3

Forecast bias by legal origin and regulatory period.

This table presents the forecast bias by legal origin and regulatory period. The mean (median) forecastbias is defined as the mean (median) difference between the forecast and realised EPS, scaled bystock price and multiplied by 100. Here Before Reg FD indicates forecasts made before 23 October,2000; Between Reg FD and GS indicates forecasts made between 23 October 2000, and 20 December2002; and After GS indicates forecasts made after December 20, 2002. The common law row presentsforecasts in common law countries. The civil law row presents forecasts in civil law countries. Thesample covers forecasts made in 1989–2006 for the fiscal years ending in 1991–2010.

Panel A: Mean forecast bias


Between BetweenBefore Reg FD Before Reg FDReg FD and GS After GS Change Reg FD and GS After GS Change

(1) (2) (3) (3)–(1) (1) (2) (3) (3)–(1)

Civil law 1.40∗∗ 2.48∗∗ 0.35∗∗ −1.05∗∗ 2.90∗∗ 4.67∗∗ 0.84∗∗ −2.06∗∗Common law 0.94∗∗ 1.43∗∗ 0.46∗∗ −0.48∗∗ 2.37∗∗ 2.77∗∗ 1.65∗∗ −0.72∗∗Difference −0.46∗∗ −1.05∗∗ 0.12 0.57∗∗ −0.53∗∗ −1.91∗∗ 0.81∗∗ 1.34∗∗

(Common–Civil)

Panel B: Median forecast bias

Median forecast bias Median forecast biasShort-term forecast: Long-term forecasts :Month −11 to +1 Month −23 to −12

Between BetweenBefore Reg FD Before Reg FDReg FD and GS After GS Change Reg FD and GS After GS Change

(1) (2) (3) (3)–(1) (1) (2) (3) (3)–(1)

Civil law 0.16∗∗ 0.64∗∗ −0.07∗∗ −0.23∗∗ 0.80∗∗ 2.07∗∗ −0.17∗ −0.97∗∗Common law 0.11∗∗ 0.22∗∗ −0.03∗∗ −0.14∗∗ 0.74∗∗ 0.88∗∗ 0.13∗∗ −0.87∗∗Difference −0.04∗∗ −0.42∗∗ 0.04∗ −0.07∗∗ −1.20∗∗ 0.29∗∗

(Common–Civil)∗∗Significantly different from zero at the 1% level.∗Significantly different from zero at the 5% level.

In Equation (3), BIASi,m,t is the mean forecast bias based on all forecasts made m monthsbefore the end of fiscal year t for firms in country i; FD is an indicator variable setto one for all forecasts made between Reg FD and the Global Settlement, and zerootherwise; and GS is an indicator variable set to one for all forecasts made since theGlobal Settlement, and zero otherwise. A negative coefficient estimate for FD wouldimply that the forecast bias declined after Reg FD. Similarly, a negative coefficientestimate for GS would imply that the forecast bias since the Global Settlement has beenlower than prior to Reg FD.

The variable Protection measures investor protection. As discussed earlier, we usea civil versus common law indicator variable, the anti-director rights index, and theaccounting standards index to identify the investor protection regime. A negativecoefficient estimate for Protection would imply that greater investor protection leads



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Civil Law Countries Common Law Countries

% of stock price

The Global Settlement

Year-month in which earnings forecasts were made

Fig. 3. Time series of the mean forecast bias by legal origin.

This figure presents the time series of the mean bias by legal origin. The mean forecast bias is definedas the difference between the mean of the forecasts made in months –11 through +1 relative to thefiscal year-end and the realised EPS, scaled by stock price and multiplied by 100. The sample coversforecasts made in 1990–2010 for the fiscal years ending in 1991–2010.

to lower forecast bias. Finally, a positive coefficient estimate for (Protection × GS)would imply that the reduction in forecast bias due to the Global Settlement is larger incountries with lower investor protection. The variable GDPi,t is the annual GDP growthrate for fiscal year t in country i, and Monthm is the forecast month relative to the end ofthe fiscal year.

Table 4 presents three sets of results using three different proxies for investorprotection. As in LLSV (2002), the regressions are estimated with random countryeffects. The robust t-statistics reported for these and all the remaining regressions arecalculated based on standard errors adjusted for heteroskedasticity and clustering bycountry.

The coefficient estimates of FD are significantly positive, except when the accountingstandards index is used as a proxy for investor protection, in which case the estimate is notstatistically significant. The coefficient estimates of the interaction of FD with Protectionare not statistically significant, regardless of the definition of investor protection used.These results imply that Reg FD did not reduce the forecast bias outside the USA. Thisis not surprising, given that Reg FD does not directly regulate analyst behaviour andexplicitly excludes foreign issuers from its requirements. Table 4 also reports negativecoefficient estimates for investor protection in all three regressions. However, only thecoefficient of accounting standards is statistically different from zero, implying thathigher accounting standards lead to lower forecast bias prior to Reg FD.

The coefficient estimates of GS are negative and statistically significant in all threeregressions, implying that, on average, across the 40 countries, the analyst forecast bias



Table 4

The Global Settlement and investor protection regression results.

This table reports the results of random country effect regressions. The dependent variable is theearnings forecast bias, defined as the mean difference between the forecast and realised EPS, scaledby stock price and multiplied by 100. The FD indicator equals one when forecasts are made between23 October 2000, and 20 December 2002. The GS indicator equals one when forecasts are madeafter 20 December 2002. Protection is investor protection as measured by a common law indicator,the anti-director index, or the accounting transparency index. The common law indicator equals onefor common law countries and zero for civil law countries. The variable Month is the forecast monthrelative to the end of the fiscal year. The sample covers forecasts made in 1989–2010 for the fiscalyears ending in 1991–2010. The robust t-statistics reported for these regressions are calculated basedon standard errors adjusted for heteroskedasticity and clustering by country.

Anti-director AccountingCommon law rights index standards index

Coef. t -stat Coef. t -stat Coef. t -stat

FD 1.123∗∗ 2.7 1.947∗ 2.4 −0.629 −0.6GS −1.603∗∗ −6.8 −2.215∗∗ −5.4 −5.190∗∗ −6.2Protection −0.262 −0.8 −0.100 −0.7 −0.054∗∗ −3.8Protection × FD −0.924 −1.9 −0.375 −1.9 0.021 1.4Protection × GS 0.859∗∗ 2.6 0.291∗∗ 2.6 0.060∗∗ 4.7GDP Growth Rate −0.295∗∗ −8.4 −0.299∗∗ −8.3 −0.305∗∗ −9.1Month 0.100∗∗ 16.7 0.100∗∗ 16.6 0.101∗∗ 16.3Constant 2.334∗∗ 7.8 2.568∗∗ 4.6 5.737∗∗ 5.7R2 0.12 0.12 0.12Number of 16,637 16,637 15,718

Country-MonthNumber of 40 40 38

Countries∗∗Significant at the 1% level.∗Significant at the 5% level.

declined following the Global Settlement. We also find positive coefficient estimates forthe interaction term between Global Settlement and investor protection, Protection ×GS, all statistically significant at 1%. These results imply that the Global Settlement andother US regulations addressing analysts’ conflicts of interest have had a significantlystronger impact on analyst forecasts in countries with lower investor protection.

Other results in Table 4 are as follows. On average, a 1 percentage point drop in therealised GDP growth rate raises the forecast bias by 0.29% of the stock price, suggestingthat analysts are unable to accurately predict GDP growth rates or their impact oncorporate earnings. Forecast bias is high for long-term forecasts and becomes lower asforecasts are made closer to the fiscal year-end. On average, forecast bias increases by0.10 percentage points per month with the forecast horizon, indicating the presence ofthe walk-down trend in these foreign markets.

6. The Role of Big 12 Bank Analysts

This section examines the role of the Big 12 banks in the mechanism by which theeffects of the Global Settlement spill over into foreign markets. As discussed earlier,public statements by some Big 12 banks suggest that they have chosen to apply theC© 2012 Blackwell Publishing Ltd


terms of the Global Settlement to their foreign operations. In addition, NASD Rule2711 applies to NASD members’ foreign employees and affiliates. It is possible that thereduction in forecast bias in foreign markets is observed because, following the GlobalSettlement, Big 12 bank analysts’ forecasts of earnings of firms in these markets havebecome less biased. If this is a significant factor driving our international results, then weshould expect the decline in forecast bias to be larger in countries with more significantBig 12 bank presence.

For this spillover mechanism to work, the Big 12 banks should have significantpresence in terms of analyst coverage in most of the countries in our sample. In addition,to test this hypothesis, we need substantial variation in Big 12 bank analyst presenceacross the sample countries.

Panel A of Table 5 presents the fractions of the forecasts made by Big 12analysts in each country prior to Reg FD, between Reg FD and the Global Settlement,and after the Global Settlement. Panel B presents the same results aggregated bylegal origin. The results show that, on average, the forecasts from Big 12 analystsaccount for about one-third of all forecasts. We also observe substantial variation inthis fraction across countries, both within the legal origin groups and across them.

To test the hypothesis that the Big 12 banks play an important role in the mechanismby which US regulation affects analyst forecasts in other countries, we extend regressionmodel (3) to include additional terms as follows:

BIASi,m,t = α0 + α1FDm,t + α2GSm,t

= α3Protectioni + α4(Protectioni × FDi,m + α5(ProtectioniGSm,t )

+ α6Big12i,m + α7(Big12i,m × FDm,t ) + α8(Big12i,m × GSm,t )

+ α9GDPi,t + α10Monthm + εi,m,t + vi .(4)

In (4), Big12i,m measures the presence of analysts who work for the Big 12 banks incountry i as the proportion of all forecasts in forecast month m that are made by analystsworking for the Big 12 banks.17 If the presence of the Big 12 banks helps reduce forecastbias after the Global Settlement, we should observe a negative coefficient estimate forBig12 × GS.

Table 6 presents three sets of results for regression (4) using different measures ofinvestor protection. All three sets of results show that the presence of Big 12 banksdoes not influence the level of the forecast bias prior to the Global Settlement, sincethe impact of the Big12 indicator and its interaction with FD indicator on forecast biasare insignificant. The coefficient estimates of interacted terms Big12 × GS are negativein all three regressions, suggesting that, following the Global Settlement, the forecastbias has declined more in countries with more Big 12 bank presence. The impact ofthese interacted terms is statistically significant at 1% when the common law indicatoror anti-director rights index is used for investor protection, but statistically insignificantwhen the accounting standards index is used.18

17 As an alternative specification, we defined Big12 as the fraction of Big 12 bank analystsrather than the fraction of forecasts made by these analysts. The results were qualitativelysimilar to those reported in this paper.18 The coefficients estimates of the interacted terms Big12 × GS are statistically significantlynegative in all three regressions when the years of the financial crisis and the followingrecession (2007–2010) are excluded.



Table 5

Big 12 bank presence.

This table reports the Big 12 bank presence by country in Panel A and by legal origin in Panel B.Here% of forecasts from Big 12 is the mean proportion of forecasts made by analysts who work for theBig 12 banks. Here Before Reg FD indicates forecasts made before 23 October, 2000; Between RegFD and GS indicates forecasts made between 23 October, 2000, and 20 December, 2002; and After GSindicates forecasts made after 20 December, 2002. The sample covers forecasts made in 1989–2010for the fiscal years ending in 1991–2010.

Panel A: Big 12 bank presence by country and regulatory period

% of forecast from Big 12 % of forecast from Big 12Short-term forecast: Long-term forecasts:Month −11 to +1 Month −23 to −12

Between BetweenBefore Reg FD After Before Reg FD AfterReg FD and GS GS Reg FD and GS GS

(1) (2) (3) (1) (2) (3)

Civil law countriesArgentina 0.16 – 0.49 0.15 0.67 0.70Austria 0.27 0.37 0.35 0.35 0.44 0.38Belgium 0.22 0.11 0.20 0.28 0.15 0.19Brazil 0.30 0.59 0.51 0.40 0.60 0.55Chile 0.31 0.56 0.72 0.35 0.57 0.79Colombia 0.36 0.53 0.59 0.31 0.55 0.76Denmark 0.11 0.15 0.14 0.13 0.14 0.13Egypt 0.00 0.55 0.37 0.40 0.21 0.41Finland 0.19 0.13 0.14 0.17 0.14 0.15France 0.23 0.21 0.20 0.26 0.21 0.22Germany 0.27 0.22 0.22 0.30 0.22 0.22Greece 0.17 0.25 0.25 0.29 0.31 0.33Indonesia 0.29 0.18 0.32 0.27 0.18 0.34Italy 0.30 0.29 0.21 0.35 0.29 0.23Japan 0.43 0.37 0.42 0.42 0.35 0.41Korea 0.37 0.16 0.15 0.37 0.13 0.16Mexico 0.26 0.48 0.69 0.31 0.52 0.75Netherlands 0.24 0.25 0.24 0.28 0.24 0.24Norway 0.18 0.10 0.10 0.21 0.12 0.09Peru 0.42 0.56 0.68 0.49 0.49 0.78Philippines 0.20 0.33 0.50 0.22 0.35 0.46Portugal 0.27 0.38 0.26 0.30 0.44 0.28Spain 0.32 0.30 0.26 0.36 0.31 0.27Sweden 0.18 0.18 0.14 0.21 0.18 0.14Switzerland 0.31 0.27 0.31 0.34 0.27 0.32Taiwan 0.59 0.55 0.47 0.58 0.54 0.48Turkey 0.09 0.09 0.22 0.37 0.35 0.34Venezuela 0.50 0.67 1.00 0.37 0.50 1.00

Common law countriesAustralia 0.56 0.65 0.62 0.55 0.66 0.63Canada 0.19 0.19 0.13 0.19 0.21 0.14Hong Kong 0.28 0.33 0.40 0.27 0.34 0.40India 0.54 0.43 0.35 0.54 0.45 0.37Ireland 0.22 0.26 0.28 0.23 0.28 0.28Israel 0.35 0.40 0.32 0.39 0.39 0.30Malaysia 0.28 0.25 0.21 0.27 0.24 0.23



Table 5

Continued.

Panel A: Big 12 bank presence by country and regulatory period


Between BetweenBefore Reg FD After Before Reg FD AfterReg FD and GS GS Reg FD and GS GS

(1) (2) (3) (1) (2) (3)

New Zealand 0.60 0.50 0.46 0.62 0.49 0.46Singapore 0.28 0.33 0.36 0.28 0.32 0.37South Africa 0.34 0.37 0.43 0.38 0.37 0.41Thailand 0.38 0.25 0.23 0.32 0.28 0.24United Kingdom 0.44 0.40 0.37 0.46 0.41 0.39

All countries 0.30 0.32 0.34 0.33 0.33 0.36

Panel B: Big 12 bank presence by legal origin and regulatory period



(1) (2) (3) (3)–(1) (1) (2) (3) (3)–(1)

Civil law 0.27∗∗ 0.29∗∗ 0.33∗∗ 0.06∗∗ 0.30∗∗ 0.31∗∗ 0.36∗∗ 0.05∗∗Common law 0.37∗∗ 0.36∗∗ 0.35∗∗ −0.02∗ 0.37∗∗ 0.37∗∗ 0.36∗∗ −0.01Difference 0.10∗∗ 0.07∗∗ 0.02∗ −0.08∗∗ 0.07∗∗ 0.07∗∗ 0.00 −0.06∗∗

(Common -Civil)

∗∗Significantly different from zero at the 1% level.∗Significantly different from zero at the 5% level.

The results in Table 6 show that, following the Global Settlement, the forecast biasdeclines more in countries with more significant Big 12 analyst presence. The questionwe ask next is whether the observed decline in forecast bias is mechanically driven bythe decline in the bias of Big 12 analysts or whether the Global Settlement has affectedthe forecasts of other analysts as well. To answer this question, Table 7 compares theforecasts of analysts working for Big 12 banks to those of other analysts.19

Comparing the mean forecast bias of Big 12 and other analysts, we observe that thedifferences are statistically insignificant and economically trivial. The results imply thatthe forecasts by Big 12 analysts are neither more nor less optimistic than those by otheranalysts. This pattern holds in all three sub-periods for both long-term and short-termforecasts. These results imply that, prior to the Global Settlement, analyst conflictsof interest were widespread and not limited to Big 12 analysts. Following the Global

19 To be included in the sample for Table 7, the covered firms must have at least one forecastfrom each of the two analyst groups within the same month. This sample contains 15,316country–month observations.



Table 6

The impact of the Global Settlement on forecast bias: The role of Big 12 banks.

This table reports the results of random country effects regressions. The dependent variable is theearnings forecast bias, defined as the mean difference between the forecast and realised EPS, scaled bystock price and multiplied by 100. The FD indicator equals one when forecasts are made between23 October, 2000, and 20 December, 2002, and zero otherwise. The GS indicator equals onewhen forecasts are made after 20 December, 2002, and zero otherwise. The variable Protection isinvestor protection as measured by a common law indicator, the anti-director index, or the accountingtransparency index. The common law indicator equals one for common law countries and zero for civillaw countries. Here Big 12 is the proportion of forecasts in the forecast month made by analysts whowork for the Big 12 banks. The variable Month is the forecast month relative to the end of the fiscalyear. The sample covers forecasts made in 1989–2010 for the fiscal years ending in 1991–2010. Therobust t-statistics reported for these regressions are calculated based on standard errors adjusted forheteroskedasticity and clustering by country.

Common lawAnti-directorrights index

Accounting standards index


FD 2.007∗ 2.4 2.542∗ 2.4 1.591 0.9GS −1.167∗∗ −3.7 −1.845∗∗ −4.5 −4.574∗∗ −5.0Protection −0.284 −0.9 −0.100 −0.7 −0.055∗∗ −3.8Protection × FD −0.730 −1.5 −0.282 −1.6 0.004 0.2Protection × GS 0.868∗∗ 2.6 0.336∗∗ 3.3 0.054∗∗ 4.2Big 12 −0.310 −0.6 −0.353 −0.7 −0.486 −0.9Big 12 × FD −2.929 −1.5 −2.709 −1.4 −3.358 −1.6Big 12 × GS −1.280∗ −2.1 −1.488∗ −2.4 −0.607 −1.0GDP Growth Rate −0.292∗∗ −8.6 −0.296∗∗ −8.6 −0.303∗∗ −9.1Month 0.102∗∗ 16.2 0.102∗∗ 16.1 0.103∗∗ 15.9Constant 2.432∗∗ 7.3 2.675∗∗ 4.5 5.895∗∗ 5.6R2 0.12 0.12 0.12Number of Country-Month 16,637 16,637 15,718Number of Countries 40 40 38∗∗Significant at the 1% level.∗Significant at the 5% level.

Settlement, however, both short-term and long-term forecast bias declined significantlyfor both Big 12 and other analysts. These results are also consistent with existing studiesthat document that analysts exhibit herding behaviour when making earnings forecasts(De Bondt and Forbes, 1999; Trueman, 1994; Welch, 2000).20

To summarise, the reduction in the forecast bias since the Global Settlement has beenlarger in countries with a more significant presence of Big 12 bank analysts. This resultis due not only to a decline in the forecast bias of Big 12 analysts, but also to a matchingdecline in the forecast bias of other analysts. Our results imply that the changes in analystregulation instituted in the USA in late 2002 through early 2003 have had a spillovereffect on analysts abroad and that the mechanism of this spillover is, at least partially,

20 See the literature review on herding behaviour in Hirshleifer and Teoh (2003).



Table 7

Forecast bias by regulatory period for Big 12 and other analysts.

This table reports the forecast bias by regulatory period for Big 12 and other analysts. The forecastbias is defined as the mean difference between the forecast and realised EPS, scaled by stock price andmultiplied by 100. Here Before Reg FD indicates forecasts made before 23 October 2000; BetweenReg FD and GS indicates forecasts made between 23 October 2000 and 20 December 2002; and AfterGS indicates forecasts made after 20 December 2002. The numbers in the Big 12 row are based onforecasts made only by analysts who work for the Big 12 banks. The numbers in the Other row arebased on forecasts made by analysts who work for the other banks only. The sample covers forecastsmade in 1989–2010 for the fiscal years ending in 1991–2010.

Mean bias Mean biasShort-term forecast: Long-term forecasts:Month −11 to +1 Month −23 to −12


(1) (2) (3) (3)–(1) (1) (2) (3) (3)–(1)

Other 0.92∗∗ 1.45∗∗ 0.06∗ −0.86∗∗ 2.22∗∗ 3.11∗∗ 0.53∗∗ −1.69∗∗Big 12 0.88∗∗ 1.32∗∗ 0.06∗ −0.82∗∗ 2.18∗∗ 3.01∗∗ 0.49∗∗ −1.69∗∗Difference −0.04 −0.13 0.00 0.04∗∗ −0.04 −0.10 −0.04 0.00

(Big 12–Other)∗∗Significantly different from zero at the 1% level.∗Significantly different from zero at the 5% level.

related to the presence of the Big 12 banks in each country and their decision to applythe terms of US regulation to their foreign operations.

These results are similar to those of Lin and McNichols (1998), who find no cross-sectional difference in their US sample between the earnings forecasts of unaffiliatedanalysts and those of affiliated analysts whose compensation is partly tied to theunderwriting business. One interpretation of this finding is that both affiliated andunaffiliated analyst forecasts were biased, in which case it is not surprising that bothhave declined since the Global Settlement.

7. Regulatory Reform in Europe

Following the changes in US analyst regulation, European Community (EC) regulatorsintroduced the MAD, designed to curb analysts’ conflicts of interest. To determinewhether its introduction has helped reduce the forecast bias since the Global Settlement,we add an indicator variable in regression (3). The term MADi,m,t is an indicator setto one for all forecasts made since the introduction of the MAD in country i, and zerootherwise. As noted by Dubois et al. (2011), EC member states adopted the MAD atdifferent times, starting with Germany in July of 2004 and ending with Portugal in Marchof 2006.21

21 The MAD adoption dates are from Dubois et al., (2011). The 13 EC countries that adoptedthe MAD are Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, theNetherlands, Portugal, Spain, Sweden, and the UK.



Table 8

The impact of the MAD on forecast bias.

This table reports the results of random country effect regressions. The dependent variable is theearnings forecast bias, defined as the mean difference between the forecast and realised EPS, scaledby stock price and multiplied by 100. The FD indicator equals one when forecasts are made between23 October 2000, and 20 December 2002, and zero otherwise. The GS indicator equals one whenforecasts are made after 20 December 2002, and zero otherwise. The MAD indicator equals one whenforecasts were made after the MAD adoption dates, and zero otherwise. The variable Protection isinvestor protection as measured by a common law indicator, the anti-director index, or the accountingtransparency index. The common law indicator equals one for common law countries and zero for civillaw countries. The variable Month is the forecast month relative to the end of the fiscal year. The samplecovers forecasts made in 1989–2010 for the fiscal years ending in 1991–2010. The robust t-statisticsreported for these regressions are calculated based on standard errors adjusted for heteroskedasticityand clustering by country.

Common lawAnti-director rights

indexAccounting

standards index


FD 1.123∗∗ 2.7 1.946∗ 2.4 −0.624 −0.6GS −1.646∗∗ −5.9 −2.288∗∗ −5.0 −5.183∗∗ −6.2MAD 0.166 0.7 0.173 0.7 −0.111 −0.5Protection −0.266 −0.8 −0.100 −0.7 −0.054∗∗ −3.8Protection × FD −0.924 −1.9 −0.374 −1.9 0.021 1.4Protection × GS 0.890∗ 2.6 0.303∗ 2.6 0.060∗∗ 4.8GDP Growth Rate −0.294∗∗ −8.4 −0.298∗∗ −8.3 −0.305∗∗ −9.1Month 0.100∗∗ 16.7 0.100∗∗ 16.5 0.101∗∗ 16.3Cons tant 2.333∗∗ 7.8 2.569∗∗ 4.6 5.726∗∗ 5.7R2 0.12 0.12 0.12Number of 16,637 16,637 15,718

Country-MonthNumber of 40 40 38

Countries∗∗Significant at the 1% level.∗Significant at the 5% level.

The coefficient estimates of the MAD indicator presented in Table 8 are statisticallyinsignificant in all three regressions, implying that, on average, forecast bias has notchanged since MAD adoption. Although our result suggests that the MAD does not helpreduce forecast bias, we should keep in mind that the worldwide recession that followedthe financial crisis of 2007–2008 adversely affected realised earnings and thereforeelevated the level of forecast bias after MAD adoption.

We should also note that Dubois et al. (2011) examine the impact of the MAD on stockrecommendations issued by affiliated analysts and document that the relative optimismof affiliated analysts has declined since the MAD but that other analysts have not changedtheir behaviour.22 In the authors’ sample, recommendations issued by affiliated analysts

22 Dubois et al., (2011) define affiliated analysts as those who worked for brokers acting asunderwriters or advisers over the last year. They define relative optimism as the differencebetween an analyst’s recommendation and the consensus recommendation.



Table 9

Robustness Checks.

This table reports the results of random country effect regressions. The dependent variable is theearnings forecast bias, defined as the mean difference between the forecast and realised EPS, scaled bystock price and multiplied by 100. In specification (1), YEAR, ranges from zero to 19, correspondingto the fiscal year-ends from 1991 to 2010. In specification (2), we scale the forecast bias by absoluterealised earnings. The robust t-statistics reported for these regressions are calculated based on standarderrors adjusted for heteroskedasticity and clustering by country.

(1) (2)

Coef t -stat Coef t -stat

FD 1.508 1.8 12.347∗ 2.3GS −2.033∗∗ −4.9 −19.110∗∗ −3.1Common Law −0.290 −0.9 −6.874 −1.7Common Law × FD −0.747 −1.5 −6.533 −1.3Common Law × GS 0.875∗∗ 2.6 14.404∗∗ 3.3Big 12 −0.248 −0.5 −4.868 −0.7Big 12 × FD −2.926 −1.5 −19.743 −1.5Big 12 × GS −1.389∗ −2.3 −9.376 −0.9GDP Growth Rate −0.288∗∗ −9.0 −3.577∗∗ −7.5Month 0.097∗∗ 15.8 1.483∗∗ 14.1Year 0.090∗∗ 3.5Constant 1.955∗∗ 5.7 38.642∗∗ 9.9

R2 0.13 0.13Number of Country-Month 16,637 16,447Number of Countries 40 40∗∗Significant at the 1% level.∗Significant at the 5% level.

account for about 3% of the total sample. Because our study examines the forecast biasof all analysts at the country level, it is unlikely to detect any change due to the MAD ifonly 3% of the sample analysts change their behaviour.

8. Robustness Checks

Richardson et al. (2004) find that forecast bias has declined gradually since the early1990s. To address the concern that our results may be driven by this trend, we add theYEAR variable, ranging from zero to 19, corresponding to the fiscal year-ends from1991 to 2010, in regression (4). The results reported in specification (1) of Table 9 arequalitatively similar to those in Table 6. The coefficient estimates of the GS indicator andthe interacted term Big12 × GS are negative and statistically significant. Meanwhile, thecoefficient estimates of Common Law × GS remain positive and statistically significant.

In specification (2), we use absolute realised earnings to normalize forecastbias.23 The results confirm that the forecast bias has declined since the Global

23 Using absolute realised earnings as a scaling variable generates some observations withextremely large forecast bias. To mitigate concerns about the impact of these extreme outliers,we drop observations with bias values in excess of ±500% of realised earnings.



Settlement and that the reduction is more evident in countries with weaker investorprotection.

In recent years, many countries have adopted International Financial ReportingStandards (IFRS), the revised version of International Accounting Standards (IAS).For example, as of January 1, 2005, all publicly listed EU companies have been requiredto prepare financial statements in accordance with IFRS. Some firms may have alsovoluntarily adopted IFRS earlier than the deadline set by their corresponding regulators.

One concern with our interpretation of the results in this paper is that the IFRSadoptions may have helped improve the quality of the accounting disclosures of theadopters, and consequently helped reduce analyst forecast bias. However, whereasforecast bias in our sample declined significantly in 2003, the IFRS adoptions werenot clustered at that time.24 In addition, the reductions in forecast bias are observed forboth countries that did and did not adopt IFRS. Therefore, IFRS adoptions cannot fullyexplain the observed reduction in forecast bias in our sample.

9. Conclusions

Prior to the Global Settlement, analysts tended to generate inflated earnings forecastsdue to conflicts of interest. Such conflicts of interest were evident worldwide and werenot limited to the 12 banks involved in the Global Analyst Research Settlement. Theforecast bias is observed more prominently in countries with civil law, low minorityshareholder rights, and low accounting standards scores, consistent with the view thatsuch countries provide investors with a lesser degree of protection against expropriationby insiders.

Following the Global Settlement, the analyst forecast bias declined significantlyworldwide. The reduction in the forecast bias has been more apparent in countries thatprovide less protection to outside investors. Furthermore, the forecast bias has declinedmore in countries with a more significant presence by analysts who work for the Big 12banks.

Our findings demonstrate that changes in analyst regulation in the USA have hadspillover effects that have influenced the behaviour of market participants in othercountries. Our findings also show that the decision to apply the terms of the USregulation to Big 12 banks’ foreign operations has played an important role in thespillover mechanism.

References

Abarbanell, J. and Lehavy, R., 2003, ‘Biased forecasts or biased earnings? The role of reportedearnings, in explaining apparent bias and over/underreaction in analysts’ earnings forecasts, Journalof Accounting and Economics, Vol. 36, pp. 105–46.

Barber, B.M., Lehavy, R., McNichols, M. and Trueman, B., 2006, ‘Buys, holds, and sells: thedistribution of investment banks’ stock ratings and the implications for the profitability of analysts’recommendations,’ Journal of Accounting and Economics, Vol. 41, pp. 87–117.

Basu, S., Hwang, L.-S. and Jan, C.-L., 1998, ‘International variation in accounting measurement rulesand analysts’ earnings forecast errors’, Journal of Business Finance and Accounting, Vol. 25, pp.1207–47.

24 See Daske et al. (2008) for data on IFRS adoption and non-IFRS adoption countries as ofDecember 2005.



Bessler, W. and Stanzel, M., 2009, ‘Conflicts of interest and research quality of affiliated analystsin the German universal banking system: evidence from IPO underwriting’, European FinancialManagement, Vol. 15, pp. 757–86.

Bolland, J., 2007, ‘Writing Securities Research: a Best Practice Guide, (John Wiley, Singapore).Bris, A., Brisley, N. and Cabolis, C., 2008, ‘Adopting better corporate governance: evidence from

cross-border mergers’, Journal of Corporate Finance, Vol. 14, pp. 224–40.Brown, L.D., 1997, ‘Analyst forecasting errors: additional evidence’, Financial Analysts Journal,

Vol. 53, pp. 81–88.Brown, L.D., 2001, ‘A temporal analysis of earnings surprises: profits versus losses’, Journal of

Accounting Research, Vol. 39, pp. 221–41.Bushman, R.M., Piotroski, J.D. and Smith, A.J., 2005, ‘Insider trading restrictions and analysts’

incentives to follow firms’, Journal of Finance, Vol. 60, pp. 35–66.Capstaff, J., Paudyal, K. and Rees, W., 2001, ‘A comparative analysis of earnings forecasts in Europe’,

Journal of Business Finance and Accounting, Vol. 28, pp. 531–62.Carleton, W.T., Chen, C.R. and Steiner, T.L., 1998, ‘Optimism biases among brokerage and non-

brokerage firms’ equity recommendations: agency costs in the investment industry’, FinancialManagement, Vol. 27, pp. 17–30.

Choi, H.S., Clarke, J., Ferris, S. and Jayaraman, N., 2009, ‘The effects of regulation on industry structureand trade generation in the US securities industry’, Journal of Banking and Finance, Vol. 33, pp.1434–45.

Chopra, V.K., 1998, ‘Why so much error in analysts’ earnings forecasts?’ Financial Analysts Journal,Vol. 54, pp. 30–37.

Charoenrook, A. and Lewis, C.M., 2009, ‘Information, selective disclosure, and analyst behavior’,Financial Management, Vol. 38, pp. 39–57.

Cornett, M.M., Tehranian, H. and Yalcin, A., 2007, ‘Regulation Fair Disclosure and the market’sreaction to analyst investment recommendation changes’, Journal of Banking and Finance, Vol. 31,pp. 567–88.

Daske, H., Hail, L., Leuz, C. and Verdi, R., 2008, ‘Mandatory IFRS reporting around the world: earlyevidence on the economic consequences’, Journal of Accounting Research, Vol. 46, pp. 1085–1142.

De Bondt, W.F.M. and Forbes, W.P., 1999, ‘Herding in analyst earnings forecasts: evidence from theUnited Kingdom’, European Financial Management, Vol. 5, pp. 143–63.

De Jong, P.J. and Apilado, V.P., 2009, ‘The changing relationship between earnings expectations andearnings for value and growth stocks during Reg FD’, Journal of Banking and Finance, Vol. 33, pp.435–42.

Dubois, M., Dumontier, P. and Fresard, L., 2011, ‘Regulating conflicts of interests: the effect ofsanctions and enforcement’, Working paper (University of Neuchatel).

Foerster, S.R. and Karolyi, G.A., 1999, ‘The effects of market segmentation and investor recognitionon asset prices: evidence from foreign stock listing in the U.S.’, Journal of Finance, Vol. 54, pp.981–1014.

Goergen, M. and Renneboog, L., 2004, ‘Shareholder wealth effects of European domestic and cross-border takeover bids’, European Financial Management, Vol. 10, pp. 9–45.

Goldman Sachs, 2007, ‘Regulatory disclosures’, Date last accessed: September 2007,http://www2.goldmansachs.com/client_services/global_investment_research/ukpolicy.html

Guidolin, M. and Mola, S., 2009, ‘Affiliated mutual funds and analyst optimism’, Journal of FinancialEconomics, Vol. 93, pp. 108–37.

Hail, L. and Leuz, C., 2009, ‘Cost of capital effects and changes in growth expectations around U.S.cross-listings’, Journal of Financial Economics, Vol. 93, pp. 428–54.

Herrmann, D., Hope, O.-K. and Thomas, W.B., 2008, ‘International diversification and forecastoptimism: the effects of Reg FD’, Accounting Horizons, Vol. 22, pp. 179–97.

Higgins, H.N., 1998, ‘Analyst forecasting performance in seven countries’, Financial Analysts Journal,Vol. 54, pp. 58–62.

Hirshleifer, D. and Teoh, S.H., 2003, ‘Herd behaviour and cascading in capital markets: a review andsynthesis’, European Financial Management, Vol. 9, pp. 25–66.



Hope, O.-K., 2003, ‘Disclosure practices, enforcement of accounting standards, and analysts’ forecastaccuracy: an international study’, Journal of Accounting Research, Vol. 41, pp. 235–72.

Hovakimian, A. and Saenyasiri, E., 2010, ‘Conflicts of interest and analyst behavior: evidence fromrecent changes in regulation’, Financial Analysts Journal, Vol. 66, pp. 96–107.

Jackson, A.R., 2005, ‘Trade generation, reputation, and sell-side analysts’, Journal of Finance,Vol. 60, pp. 673–717.

Johnson, S., La Porta, R., Lopez-de-Silanes, F. and Shleifer, A., 2000, ‘Tunneling’, American EconomicReview Papers and Proceedings, Vol. 90, pp. 22–27.

Kadan, O., Madureira, L., Wang, R. and Zach, T., 2009, ‘Conflicts of interest and stock recommen-dations: the effects of the Global Settlement and related regulations’, Review of Financial Studies,Vol. 22, pp. 4189–4217.

Khanna, T., Palepu, K. and Srinivasan, S., 2004, ‘Disclosure practices of foreign companies interactingwith U.S. markets’, Journal of Accounting Research, Vol. 42, pp. 475–508.

Kwag, S.-W., 2006, ‘Shareholder protection and forecast bias’, Journal of Applied Business Research,Vol. 22, pp. 39–48.

La Porta, R., Lopez-de-Silanes, F., Shleifer, A. and Vishny, R.W., 1998, ‘Law and finance’, Journal ofPolitical Economy, Vol. 6, pp. 1113–55.

La Porta, R., Lopez-de-Silanes, F., Shleifer, A. and Vishny, R.W., 2002, ‘Investor protection andcorporate valuation’, Journal of Finance, Vol. 57, pp. 1147–70.

Lang, M.H., Lins, K.V. and Miller, D.P., 2003, ‘ADRs, analysts, and accuracy: does cross listing inthe United States improve a firm’s information environment and increase market value?’ Journal ofAccounting Research, Vol. 41, pp. 317–45.

Lim, T., 2001, ‘Rationality and analysts’ forecast bias’, Journal of Finance, Vol. 56, pp. 369–85.Lin, H.-W. and McNichols, M.F., 1998, ‘Underwriting relationships, analysts’ earnings forecasts and

investment recommendations’, Journal of Accounting and Economics, Vol. 25, pp. 101–27.Michaely, R. and Womack, K.L., 1999, ‘Conflict of interest and the credibility of underwriter analyst

recommendations’, Review of Financial Studies, Vol. 12, pp. 653–86.Miller, D.P., 1999, ‘The market reaction to international cross-listing: evidence from depositary

receipts’, Journal of Financial Economics, Vol. 51, pp. 103–23.NASD and NYSE, 2005, ‘Joint report on the operation and effectiveness of the research analyst conflict

of interest rules’, date last accessed: September 2007, http://www.nyse.com/pdfs/rajointreport.pdf.Niehaus, G. and Zhang, D., 2010, ‘The impact of sell-side analyst research coverage on an affiliated

broker’s market share of trading volume’, Journal of Banking and Finance, Vol. 34, pp. 776–87.O’Brien, P.C., 1988, ‘Analysts’ forecasts as earnings expectations’, Journal of Accounting and

Economics, Vol. 10, pp. 53–83.Rajan, R. and Zingales, L., 1998, ‘Financial dependence and growth’, American Economic Review,

Vol. 88, pp. 559–86.Richardson, S.A., Teoh, S.H. and Wysocki, P.D., 2004, ‘The walk-down to beatable analyst forecasts:

the role of equity issuance and insider trading incentives’, Contemporary Accounting Research,Vol. 21, pp. 885–924.

Stulz, R.M., 1999, ‘Globalization, corporate finance, and the cost of capital’, Journal of AppliedCorporate Finance, Vol. 12, pp. 8–25.

Trueman, B., 1994, ‘Analyst forecasts and herding behavior’, Review of Financial Studies, Vol. 7, pp.97–124.

Welch, I., 2000, ‘Herding among security analysts’, Journal of Financial Economics, Vol. 58, pp.369–96.


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