managers’ forecast guidance of analysts: international evidence

Journal of Accounting and Public Policy 24 (2005) 280–299

www.elsevier.com/locate/jaccpubpol

Journal of Accounting and Public Policy 24 (2005) 280–299

www.elsevier.com/locate/jaccpubpol

Managers� forecast guidance ofanalysts: International evidence

Lawrence D. Brown a,*, Huong N. Higgins b

a J. Mack Robinson College of Business, School of Accountancy,

Georgia State University, Atlanta, GA 30302-4050, United Statesb Department of Management, Worcester Polytechnic Institute, Worcester, MA 01609,

United States

Abstract

We consider forecast guidance as a mechanism that managers use to avoid negativeearnings surprises. Modeling forecast guidance using methods by Matsumoto,[Accounting Review 77 (3) (2002) 483–514] and Bartov et al. [Journal of Accountingand Economics 33 (2) (2002) 173–204], we show that managers in strong-investor-pro-tection countries are more likely to utilize forecast guidance to avoid negative earningssurprises than managers in weak-investor-protection countries. We also show that USmanagers are more prone to use forecast guidance to avoid negative earnings surprisesthan managers in other countries. Our results provide insight into the information dis-semination process and how managers behave in response to weak regulation of infor-mal disclosures in different investor protection environments.� 2005 Elsevier Inc. All rights reserved.

Keywords: Forecast guidance; Analysts; Earnings; International; Investor protection

0278-4254/$ - see front matter � 2005 Elsevier Inc. All rights reserved.doi:10.1016/j.jaccpubpol.2005.05.001

* Corresponding author. Tel.: +1 404 651 0545; fax: +1 404 651 1033.E-mail address: [email protected] (L.D. Brown).

mailto:[email protected]

L.D. Brown, H.N. Higgins / Journal of Accounting and Public Policy 24 (2005) 280–299 281

1. Introduction

Avoiding negative earnings surprises is entrenched in today�s corporate cul-ture (Bartov et al., 2002). Managers seek to avoid negative earnings surprises toavoid litigation, maximize the value of their compensation, and boost theircredibility (Brown and Higgins, 2001; Bartov et al., 2002). There are two waysthat managers can avoid negative earnings surprises: (1) manage reported earn-ings upward and/or (2) guide analysts� earnings expectations downward.1 Theacademic literature generally focuses on managing reported earnings upwardas a way to avoid negative earnings surprises (Healy and Wahlen, 1999), butthe business press considers downward forecast guidance as crucial to the earn-ings surprise game (Bleakley, 1997; Ip, 1997; McGee, 1997; Talley and Craig,2002; Vickers, 1999).

We focus on downward forecast guidance as a way managers avoid negativeearnings surprises in an international setting. Managers make disclosures toinvestors and analysts through a variety of channels using both formal andinformal disclosures (Rao and Sivakumar, 1999). Accounting research has fo-cused primarily on formal disclosures, such as financial accounting reports,rather than on informal disclosures, such as analyst forecast guidance. Formaldisclosures represent only a portion of the disclosure process, are often not theprimary vehicle for apprising shareholders of important developments, and areout-of-date when they are distributed. On the other hand, informal disclosureshave increasingly become the main vehicle for informing the market on atimely basis. Many companies disclose information through informal means,such as press releases, promotional materials, speeches, and conversations withanalysts (Brown, 2005). Given the role of informal disclosures, research onforecast guidance is important because it improves our understanding ofhow and why firms disseminate informal disclosures.

Research on forecast guidance is also important because it has implicationsfor public policies addressing earnings surprise games. While public policiesregulating formal disclosures exist in most countries, policies regulating infor-mal disclosures are virtually absent. Informal communications such as press re-leases and communications to shareholders remain virtually free of expressregulation. The US has almost no direct regulation regarding informal disclo-sure (Brown, Chapter 2.06, p. 4, 2005). Rather, informal communications areregulated under the antifraud provisions of the Exchange Act, mainly Rule10b-5, which places a blanket prohibition on firms� fraudulent misstatements.The antifraud provisions, particularly the ubiquitous Rule 10b-5, impose

1 Similar to Bartov et al. (2002) and Matsumoto (2002), we focus on negative surprise avoidance.Consistent with the extant literature, we use the terms negative surprise avoidance and reportedearnings that meet or beat analyst earnings estimates synonymously.

282 L.D. Brown, H.N. Higgins / Journal of Accounting and Public Policy 24 (2005) 280–299

obligations of accuracy and completeness, and sometimes dictate the contentand timing of corporate communications. US antifraud provisions are at bestan imperfect regulation mechanism due to a lack of systematic rules, and nocountry has better regulation mechanisms of informal disclosures than theUS. For example, in Continental Europe, EC Listing and Reporting Directivesregulating disclosures adhere to the principles of materiality, clear disclosure,current information, standard format, cautionary statements in forward-look-ing statements, and equal treatment of investors, but the content and timelinessof informal disclosures are not subjected to regulatory supervision (Baums,2002).

We contribute to the public policy literature by examining managerialbehavior in a specific context, managing earnings surprises in response toweak regulation of informal disclosures in different investor protection envi-ronments. In strong-investor-protection environments, characterized bycommon-law and market orientation (La Porta et al., 1998), managers seekto avoid negative earnings surprises because strong-investor-protection envi-ronments place high emphasis on stock returns (Brown and Higgins, 2001).There are two ways to avoid negative earnings surprises: manage reportedearnings upward and/or guide analyst forecasts downward (Matsumoto,2002). Given the relative difficulty of managing earnings upward in strong-investor-protection environments, managers are relatively more likely to useforecast guidance in these environments because the regulation of forecastguidance is far less rigorous than that of managing earnings. On the contrary,in weak-investor-protection environments, which are characterized by code-law and credit orientation, there is less emphasis on stock price performanceso managers are less pressured to avoid negative surprises (Brown and Higgins,2001).

When there is less pressure to meet or beat analyst estimates, managers arerelatively less likely to use forecast guidance. Further, because regulation of re-ported earnings is not stringent in weak-investor-protection countries, manag-ers wishing to avoid negative surprises in these environments are more likely touse earnings management, reducing the role of forecast guidance. Drawing onthe above, we hypothesize and show that managers in strong-investor-protec-tion environments are relatively more likely to use forecast guidance to avoidnegative earnings surprises vis a vis managers in weak-investor-protection envi-ronments. We also show that US managers are more likely to use forecast guid-ance than non-US managers, consistent with prior findings that US managersmanage earnings surprises more (Brown and Higgins, 2001) but manage re-ported earnings less (Bhattacharya et al., 2003; Leuz et al., 2003) than theirinternational peers.

We proceed as follows. Section 2 develops our hypothesis. Section 3 des-cribes our sample and methodology. Section 4 discusses results of preliminaryanalyses. Section 5 presents results of our main analyses. Section 6 concludes.


2. Hypothesis development

2.1. Weak regulation of informal disclosures

Public policies regulating formal accounting reports exist inmost countries. Incontrast, public policies regulating informal communication channels betweenmanagers and analysts are less developed in all countries. With the exceptionof Regulation FD, there is no direct regulation in the US regarding informal dis-closures (Brown, Chapter 2.06, p. 4, 2005). Instead, such informal communica-tions are regulated under the antifraud provisions of the securities laws, mainlyRule 10b-5, which constitutes a blanket prohibition on fraudulent misstatementsby any company. The antifraud provisions have a pronounced impact on theregulation of informal disclosures, because they mandate to some degree theaccuracy, completeness, and timeliness of disclosure, and impose a duty of con-tinued accuracy once disclosure has occurred. The only direct regulation of infor-mal disclosures in the US is Regulation FD, which aims at eliminating thepractice of selective disclosure of material information, particularly to analysts.

While the US antifraud provisions are the vehicles for informal disclosureregulation, companies are often unaware of their implications in the absenceof a systematic set of rules and regulations. The provisions have regulatedinformal communications in a haphazard fashion. With the recent explosionin shareholder class actions, courts have shown increasing dissatisfaction withthe natural development of the law under Rule 10b-5, resulting in some courtsbeing willing to grant motions to dismiss despite facts to the contrary. Regula-tion of informal disclosures in the US is imperfect at best.

Many non-US countries adhere to disclosure principles similar to those inthe US. For example, in Continental Europe, EC Listing and Reporting Direc-tives regulating disclosures adhere to the principles of materiality, clear disclo-sure, current information, standard format, cautionary statements in forward-looking statements, and equal treatment of investors (Baums, 2002). However,in practice, informal disclosures are not subjected to regulatory supervisionwith respect to their content and timeliness in Continental Europe (Baums,2002). Overall, general disclosure principles are similar across countries, butthere is little direct regulation of informal disclosures other than genericanti-tort provisions in respective countries� laws, and there is little (if any)empirical research on cross-country variation in regulation of informal disclo-sures. Regulation of informal disclosures is weaker, or not stronger, than reg-ulation of formal disclosures in all countries.

2.2. Forecast guidance to avoid negative earnings surprises

We draw upon the investor protection and earnings surprise managementliteratures to hypothesize how managers� responses to weak regulation of


informal disclosures differ across investor protection environments. In strong-investor-protection environments, due to their market orientation and highemphasis on stock price performance, managers have incentives to avoid neg-ative earnings surprises to safeguard stock valuation (Brown and Higgins,2001). For example, there are three sources of managers� incentives to avoidnegative surprises to safeguard stock price performance: (1) the presence ofindependent board directors who stress price performance, (2) the market forcorporate control that removes managers of undervalued firms, and (3) equi-ty-based executive compensation contracts (Brown and Higgins, 2001).

There are two ways to avoid negative earnings surprises: manage reportedearnings upward and/or manage analyst forecasts downward (Matsumoto,2002). In strong-investor-protection environments, managers are less likely tomanage earnings upward because strong investor protection limits managers�ability to acquire private control benefits, reducing their incentives to maskfirm performance (Leuz et al., 2003). Strong-investor-protection environmentsare characterized by rules of law that allow minority shareholders to challengedirectors� decisions in court to protect themselves against expropriation by con-trolling managers, and by court systems that effectively enforce investors� rightsor substitute for weak investor-protection laws. For example, shareholders�right to vote by mail when they cannot travel to shareholders� meetingsempowers investors against abuse by managers.

Given the difficulty of managing reported earnings upward in strong-inves-tor-protection environments, and the lack of rigorous regulations regardingforecast guidance, managers in strong-investor-protection environments aremore likely to use downward forecast guidance to avoid negative earnings sur-prises. In contrast, in weak-investor-protection environments, managers do nothave strong incentives to avoid negative surprises (Brown and Higgins, 2001),so they have less need to use downward forecast guidance. Furthermore, man-agers in weak-investor-protection environments wishing to avoid negativeearnings surprises are better able to manage earnings upward due to weak reg-ulation of reported earnings, so they have less need to use downward forecastguidance. Based on the above, our hypothesis (in alternative form) is:

H1: Managers in strong-investor-protection countries are more likely to usedownward forecast guidance to avoid negative earnings surprises than manag-ers in weak-investor-protection countries.

3. Sample and methodology

3.1. Measures of downward forecast guidance (dependent variable)

To enhance our study�s validity, we use two measures of downward forecastguidance, one based on Matsumoto (2002), and the other based on Bartovet al. (2002).


3.1.1. Measure based on Matsumoto (2002)

Similar to Matsumoto (2002), we measure forecast guidance conditional onmeeting or beating analyst earnings estimates using a binary variable (MGUI).Specifically, for each firm i, in industry j, in country k, in year t, guidance is theunexpected portion of the earnings forecast (UEF), measured as the differencebetween the consensus analyst earnings forecast (CF) and the expected analystearnings forecast (E[F]) for the period:

UEFijkt ¼ CFijkt � E½F ijkt�. ð1ÞThe expected analyst forecast (E[F]) in Eq. (1) is modeled using a random walkmodel (EPS from the previous period) with drift (E[D]):

E½F ijkt� ¼ EPSijkt�1 þ E½Dijkt�. ð2ÞExpected drift (E[D]) in Eq. (2) is estimated based on prior earnings changes,where drift is the earnings change from the previous year (Eq. (3)). Expecteddrift uses actual yearly drift (D) and ending price (PRICE) for all firms inindustry j, country k and year t (Eq. (4))

Dijkt ¼ EPSijkt � EPSijkt�1 ð3Þand

E½Dijkt� ¼ ajkt þ bjkt � ðDijkt�1=PRICEijkt�2Þ � PRICEijkt�1. ð4Þ

In Eq. (4), we use the values of ajkt and bjkt only when they can be estimatedusing at least ten firms in the same country-industry-year (two-digit SIC codefor industry). All measurements are based on US dollars using the exchangerate prevailing on the first day of the month after the fiscal year end.2 Ourmodel and restriction to ten or more firms is similar to Matsumoto (2002), ex-cept we examine international firms, use annual data,3 account for currencydifferences, and exclude the portion of the expected forecast reflected in cumu-lative daily excess returns after the previous earnings announcement.4 DOWN

2 Translation to a single currency for all firms within a country-industry-year is necessary forestimating regression equation (4). We use exchange rates at fiscal year end to mitigate the effects ofcurrency movements. We translate all firms to US dollars to facilitate cross-sectional tests.3 Matsumoto (2002) uses quarterly data and US firms. We use annual data because most

countries do not require quarterly reporting.4 We make this exclusion because of limited availability of international stock returns data. If

new information is not impounded in stock prices quickly, this exclusion results in no differencebetween our metric and Matsumoto�s. New information is not impounded in stock prices quicklyfor many non-US firms traded in relatively poorly developed markets (Ball et al., 2000). When newinformation is impounded in stock prices quickly for a firm performing well, this exclusion resultsin a more negative drift, a more negative expected forecast, and a more positive guidance measurecompared to Matsumoto (2002). Because new information is impounded in stock prices morequickly for firms in well-developed markets with strong-investor-protection, and because stockprices generally rose during our sample period, this exclusion makes our tests more conservative.


is coded 1 if UEF from Eq. (1) is negative and 0 otherwise. We define down-ward forecast guidance as occurring (MGUI = 1) when DOWN = 1 and re-ported earnings meet or beat analyst expectations (i.e., the last consensusestimate before the earnings report date). Otherwise, we set MGUI = 0.

3.1.2. Measure based on Bartov et al. (2002)We also measure downward forecast guidance similar to Bartov et al.

(2002), denoted with a binary variable (BGUI). Specifically, a firm year is char-acterized as having downward forecast guidance (i.e., BGUI = 1) when: (1)current earnings are less than the first consensus forecast, (2) the last consensusforecast is less than the first consensus forecast, and (3) current earnings aregreater than or equal to the last consensus forecast. Otherwise, BGUI is 0.

3.2. Independent variable—investor protection

We use country-level factors to reflect the extent of country investor protec-tion. The country-level factors are discussed by La Porta et al. (1997, 1998),who distinguish between laws protecting outside investors and the quality oflaw enforcement. Laws protecting outside investors consist of legal rules con-cerning investors� rights such as voting power, ease of participation in corpo-rate voting, and legal protection against expropriation by management.Enforcement relates to important intermediary functions such as an effectivejudicial system to empower courts in law enforcement, and an effectiveaccounting system to render company disclosures interpretable and verifiable.Consistent with La Porta et al. (1998) and Leuz et al. (2003), we use the anti-director rights index5 to proxy for investor protection laws, the enforcementindex6 to measure investor protection enforcement, and the index for account-ing quality7 to capture the strength of financial contracting enforcement,

5 La Porta et al. (1998) construct an anti-director rights index to capture how strongly the legalsystem favors minority shareholders against managers or dominant shareholders in corporatedecision-making processes. The index is computed based on six anti-director rights, namely theright to: (1) vote by mail, (2) sell shares around the date of shareholders� meeting, (3) havecumulative voting for directors and/or proportional representation on the board, (4) challengedirectors� decisions in court and/or to force the company to repurchase shares of minorityshareholders who object to certain management decisions, (5) have preemptive rights to buy newissues of stock, and (6) call an extraordinary shareholders� meeting.6 Leuz et al. (2003) use the mean score across three enforcement variables by La Porta et al.

(1998) to capture how strongly a system of legal enforcement substitutes for weak laws, forexample, by allowing active and well-functioning courts to step in and rescue investors abused bymanagers. The three enforcement variables are: (1) judicial system efficiency, (2) assessment of ruleof law, and (3) corruption index.7 La Porta et al. (1998) rely on a privately constructed index based on examination of company

reports from different countries to estimate quality of accounting standards.


especially when investor rights are weak. We use principal factor analysis toobtain a single factor score to condense these three indices into one investorprotection factor (IPFAC). The factor score receives the highest loadings fromInvestor Protection Laws (0.92352), followed by Investor Protection Enforce-ment (0.7561), and Quality of Accounting Standards (0.8604), indicating thatfactor analysis is appropriate for data condensation in this case.8

3.3. Control variables

We use a binary variable US (1 for a US firm, 0 otherwise) as a control vari-able because our sample consists of a majority of US observations and we wishto ensure that US data do not drive our results. US and IPFAC may be corre-lated so we test for but do not find harmful multicollinearity. In a robustnesscheck, we replicate our analysis without US observations and obtain consistentresults.

We cannot separate analyst downward bias from forecast guidance withprecision because both result from interaction between analysts and manag-ers. To mitigate this problem, we control for potential confounding effectsof earnings and forecast properties using five factors: (1) earnings growth,(2) earnings variability, (3) time-series properties of earnings (time-series bias),(4) analyst forecast bias, and (5) earnings skewness. We use the I/B/E/S def-inition of earnings growth (GROWTH), i.e., average annualized earnings pershare growth over the prior five years. We also use the I/B/E/S definition ofearnings variability (VAR), i.e., mean absolute difference between reportedearnings per share and a five-year historical EPS growth trend line fromthe current year, expressed as a percentage of trend line earnings per share.We measure time-series forecast (TSBIAS) bias as the difference betweenthe current year�s earnings and the previous year�s earnings, scaled by theabsolute value of current earnings. We measure analyst forecast bias (AN-BIAS) as the difference between current earnings and the last analyst consen-sus forecast before the earnings announcement, scaled by the absolute valueof current earnings. We measure earnings skewness (SKEW) as the statisticalskewness of the distribution of the firm�s earnings to price ratio over the sam-ple period.

We include market capitalization (LCAP), GDP growth (GDP), and year(YEAR) to control for firm size, economic cycle, and time period, respectively.LCAP is measured as the log of firm�s market capitalization in millions of USdollars in the current year. GDP is the annual growth rate in the current year ofa country�s real seasonally adjusted GDP. YEAR is coded as 1 to 10, for therespective calendar years 1991–2000.

8 Our results are robust to using each of the measures underlying IPFAC individually.


3.4. Data sources and sample selection

We obtain our data from I/B/E/S and DataStream, products of ThomsonFinancial Inc. We use the I/B/E/S International Summary File for analyst con-sensus forecasts, actual earnings and stock prices, and Datastream for ex-change rates and GDP growth rates. We compute DOWN for firms withthree or more years of data using the last annual consensus (mean) forecastbefore the annual earnings announcements. Following Matsumoto (2002),we restrict our sample to firms with at least ten observations in a given coun-try-industry-year to calculate expected drift. To allow for reasonable samplesizes, we exclude countries with less than 100 firm-year observations of com-putable forecast guidance in the ten-year period, 1991–2000.

Table 1 shows the distribution of firms and observations by country. Thereare 43,360 forecasts of 10,659 firms from the US and 20 other countries (inalphabetical order): Australia, Canada, France, Germany, Greece, HongKong, India, Italy, Japan, Korea, Malaysia, Norway, South Africa, Spain,Sweden, Switzerland, Taiwan, Thailand, Turkey, and the UK. The US hasboth the most firms (5283) and observations (21,799). Seven non-US countrieshave at least 200 firms each: Japan (1646), the UK (1244), Korea (584), Taiwan(259), Thailand (245), Germany (216), and Canada (200), and eight non-UScountries have at least 500 observations: the UK (6556), Japan (6140), Korea(2429), Taiwan (927), Thailand (818), Canada (727), Germany (643), and Aus-tralia (576). Due to data availability for many control variables, our final sam-ple is reduced to 31,864.

4. Preliminary analyses

Similar to Matsumoto (2002), we conduct several analyses to assess thevalidity of DOWN. Our first four analyses show that DOWN adheres to theexpected patterns of earnings surprise management as documented in priorstudies. Our fifth analysis compares our two measures of forecast guidance,MGUI and BGUI. Our sixth analysis replicates our first five analyses omittingUS forecasts.

4.1. Ex-post pattern in meet or beat estimates

Matsumoto (2002) examines whether forecast guidance facilitates meetingor beating analyst consensus earnings estimates. She finds that DOWN iscoded 1 in 54.12% of her sample when US firms report quarterly earnings thatmeet or beat analyst estimates, but in only 49.23% of her sample when they re-port quarterly earnings falling short of analyst estimates. Her chi-square statis-tic of 33.48 is significant at a p-value <0.0001. We conduct similar analyses and

Table 1Distribution of forecast guidance in international firms

Country No. offirms

No. ofobservations

No. of observations aspercent of total sample (%)

Percent of observationswith MGUI = 1 (%)

Percent of observationswith BGUI = 1 (%)

Investor protectionranking

Australia 161 576 1.33 32.29 5.73 16Canada 200 727 1.68 26.82 8.39 19France 120 381 0.88 20.74 5.25 11Germany 216 643 1.48 24.88 6.84 7Greece 81 179 0.41 25.70 3.35 2Hong Kong 73 291 0.67 19.24 1.38 15India 90 198 0.46 21.72 3.03 6Italy 47 177 0.41 19.21 6.22 5Japan 1646 6140 14.16 24.02 13.57 13Korea 584 2429 5.60 14.66 8.69 4Malaysia 53 206 0.48 21.36 2.91 14Norway 39 104 0.24 29.81 11.54 18South Africa 80 296 0.68 25.68 4.39 10Spain 34 147 0.34 31.97 0.68 9Sweden 66 155 0.36 37.42 9.03 20Switzerland 62 299 0.69 28.76 4.01 12Taiwan 259 927 2.14 21.04 7.55 8Thailand 245 818 1.89 23.59 8.92 3Turkey 76 312 0.72 10.58 5.45 1UK 1244 6556 15.12 36.96 8.27 21US 5283 21,799 50.27 37.91 25.00 17

Total 10,659 43,360 100 32.5 22.23

Notes: MGUI and BGUI are binary variables denoting forecast guidance based on methods adapted from Matsumoto (2002) and Bartov et al. (2002),respectively. MGUI is coded 1 when current earnings meet or beat analyst estimates, and DOWN is 1 and 0 otherwise. DOWN is coded 1 if the modeledmeasure of unexpected earnings forecast (UEF) is negative and 0 otherwise. BGUI is coded 1 when: (1) current earnings are less than the first consensusforecast, (2) the last consensus forecast is less than the first consensus forecast, and (3) current earnings are greater than or equal to the last consensusforecast, and 0 otherwise. Investor Protection Ranking is the rank ordering of the IPFAC scores, where IPFAC is the factor score of three investorprotection indices: Investor Protection Laws, Investor Protection Enforcement, and Quality of Accounting Standards as in La Porta et al. (1998) and Leuzet al. (2003). The highest (lowest) score represents the most (least) investor protection.

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Table 2Contingency analyses of DOWN

Guidance (DOWN= 1)

Panel A: Contingency analyses of DOWN partitioned by sign of earnings surprise

Sign of earnings surprise No YesPositive 10,083 (41.71%) 14,092 (58.29%)Negative 9687 (50.49%) 9481 (49.51%)

v2 = 332.73p < 0.0001

Panel B: Contingency analyses of DOWN partitioned by sign of earnings

EarningsProfit 15,945 (44.50%) 19,885 (55.50%)Loss 3766 (50.72%) 3659 (49.28%)

v2 = 95.89p < 0.0001

Panel C: Contingency analysis of DOWN for positive profit surprises partitioned by size of surprise

Profit surpriseSmall positive 3380 (38.94%) 5301 (61.06%)Other positive surprises 4623 (43.66%) 5966 (56.34%)

v2 = 43.82p < 0.0001

Panel D: Contingency analysis of DOWN for negative loss surprises partitioned by size of surprise

Loss surpriseExtreme negative 1144 (61.01%) 731 (38.99%)Other negative surprises 1760 (49.77%) 1776 (50.23%)

v2 = 62.25p < 0.0001

Notes: DOWN is coded 1 if the modeled measure of unexpected earnings forecast (UEF) is negativeand 0 otherwise. Earnings surprise is defined as actual minus predicted earnings, divided by theabsolute value of actual earnings. Loss is defined as negative earnings. Profit is defined as non-negative earnings. Positive earnings surprises within 5% of reported profits are termed smallpositive surprises. Negative earnings surprises less than (more negative than) 100% of reportedlosses are termed extreme negative surprises.


report our results in Panel A of Table 2. When international managers reportannual earnings meeting or beating analyst estimates, DOWN = 1 in 58.29% ofour sample. When international managers report annual earnings falling shortof analyst estimates, DOWN = 1 in 49.51% of our sample. The chi-square sta-tistic of 332.73 is significant at a p-value of <0.0001, consistent with guidancebeing associated with surprise management in an international context.

4.2. Profits versus losses

The impact of earnings announcements on valuation is more pronouncedfor profitable firms (Hayn, 1995), providing managers with greater incentives


to manage earnings surprises (Degeorge et al., 1999; Brown, 2001). If interna-tional managers use forecast guidance to manage earnings surprises, guidanceshould be more evident for profitable firms. Panel B, a contingency analysis ofDOWN partitioned into firms reporting profits versus losses, shows thatDOWN = 1 in 55.50% and 49.28% of profit and loss observations, respec-tively. The chi-square statistic of 95.89, significant at a p-value of <0.0001, isconsistent with forecast guidance occurring relatively more often for profitablefirms.

4.3. Profit firms: small positive versus other positive surprises

The patterns of surprise management differ between profit and loss firms(Degeorge et al., 1999); managers seek to create small positive surprises forprofit firms and avoid extreme negative surprises for loss firms (Brown,2001). We focus on small positive surprises for profit firms in this subsectionand extreme negative surprises for loss firms in the next subsection. If interna-tional managers use forecast guidance to manage profit surprises, guidanceshould pertain more to small positive profit surprises than to other positiveprofit surprises.

Panel C provides results of a contingency analysis of DOWN for firmspartitioned into small versus other positive profit surprises. Similar to Brownand Higgins (2001), we define earnings surprises as actual minus predictedearnings, divided by the absolute value of actual earnings, and small positivesurprises as those within 5% of reported profits. DOWN = 1 in 61.06% ofsmall positive versus 56.34% of other positive profit surprises. The chi-squarestatistic of 43.82 is significant at a p-value of <0.0001, consistent with guid-ance pertaining more to firms reporting small versus other positive profitsurprises.

4.4. Loss firms: extreme negative versus other negative surprises

If managers use forecast guidance to manage loss surprises, guidance will beless evident when managers report extreme negative versus other negative losssurprises. Panel D presents findings of a contingency analysis of DOWN forloss firms partitioned into extreme versus other negative surprises. Similar toBrown and Higgins (2001), we consider surprises more negative than 100%of reported losses to be extreme negative surprises. DOWN = 1 in 38.99% ofextreme negative versus 50.23% of other negative loss surprises. The chi-squarestatistic of 62.25 is significant at a p-value of <0.0001, showing less forecastguidance when firms report extreme negative loss surprises than when firms re-port other negative loss surprises. Overall, our Table 2 results suggest thatDOWN, as measured by MGUI, captures forecast guidance as a mechanismfor earnings surprise management.

Table 3Contingency analyses of forecast guidance measures MGUI and BGUI

Downward forecastguidance measured by MGUI

Downward forecast guidance measured by BGUI

No guidance Guidance Total

No guidance 24,754 (84.58%) 4514 (15.42%) 29,268 (67.50%)Guidance 8969 (63.65%) 5123 (36.35%) 14,092 (32.50%)

Total 33,723 (77.77%) 9637 (22.23%) 43,360 (100%)v2 = 2410.83p < .0001

Notes: MGUI and BGUI are binary variables denoting forecast guidance based on methodsadapted from Matsumoto (2002) and Bartov et al. (2002), respectively. MGUI is coded 1 whencurrent earnings meet or beat analyst estimates and DOWN is 1. DOWN is coded 1 if the modeledmeasure of unexpected earnings forecast (UEF) is negative and 0 otherwise. BGUI is coded 1 when:(1) current earnings are less than the first consensus forecast, (2) the last consensus forecast is lessthan the first consensus forecast, and (3) current earnings are greater than or equal to the lastconsensus forecast, and 0 otherwise.


4.5. Comparing our two measures of forecast guidance

We examine differences and similarities between MGUI and BGUI, basedon Matsumoto (2002) and Bartov et al. (2002), respectively, by showing a con-tingency analysis of MGUI versus BGUI in Table 3. Based on MGUI, 32.50%of the observations are characterized as forecast guidance, while based onBGUI, 22.23% of the observations are characterized as forecast guidance.9

This finding reveals that the underlying measurement methods differ, specifi-cally the Bartov et al. (2002) method is more restrictive, classifying fewer obser-vations as guidance than the Matsumoto (2002) method.10 Despite thisdifference, Table 3 shows that BGUI and MGUI essentially are consistent witheach other, i.e., they agree or capture the same concept. Specifically, whenMGUI = 0, only 15.42% of observations have BGUI = 1, but 84.58% of obser-vations have BGUI = 0. Similarly, when BGUI = 0, only 26.60% of observa-tions have MGUI = 1, but 73.40% of observations have MGUI = 0.11 Wereplicated the preliminary analyses reported in panels A through C of Table2 based on BGUI instead of DOWN.12 For simplicity, we do not tabulate these

9 MGUI equals 1 for 14,092 of 43,360 observations (or 32.50%). BGUI equals 1 for 9637 of43,360 observations (or 22.23%).10 Our results are comparable with those reported by Bartov et al. (2002) and Matsumoto (2002).Bartov et al.�s (Matsumoto�s) total population and number of guidance cases are 64,872 (15,848)and 10,977 (8324), respectively. The ratio of number of guidance cases over the total population is16.92% for Bartov et al. and 52.52% for Matsumoto.11 These percentages are based on the respective ratios, 8969/33,723 and 24,754/33,723.12 A Panel-D-type analysis is not relevant because it compares extreme negative loss surprises withother negative loss surprises and, under Bartov et al. (2002) all guidance observations are defined tohave positive surprises.


results but we find similar patterns of earnings surprise management, consistentwith BGUI reliably capturing forecast guidance.

4.6. Examining non-US firms separately

The above analyses may be driven by the fact that about half of our sampleconsists of US firms so we replicate all five analyses after omitting US firms.For simplicity, we do not tabulate these results, but they are statistically signifi-cant and consistent with those reported in Sections 4.1–4.5. Specifically, we findthat downward forecast guidance is more evident when firms report earningsthat meet or beat versus fall short of analysts� forecasts, profits versus losses,and small profit surprises versus other positive profit surprises. Moreover,downward forecast guidance is less evident when firms report extreme versusother negative loss surprises using either the Bartov et al. (2002) or Matsumoto(2002) method.

5. Main results

5.1. Summary data

Column 5 of Table 1 shows mean MGUI percentages by country. The UShas the largest percent (37.91%), followed by Sweden (37.42%), the UK(36.96%), and Australia (32.29%). Turkey has the smallest percent (10.58%),preceded by Korea (14.66%), Italy (19.21%), and Hong Kong (19.24%). Col-umn 6 shows the mean BGUI percentages by country. The US has the largestpercent (25%), followed by Japan (13.57%) and Norway (11.54%). Spain hasthe smallest percent (0.68%), preceded by Hong Kong (1.38%) and Malaysia(2.91%). The summary data highlight the highest forecast guidance measuresin the US, consistent with US managers using forecast guidance more thantheir international counterparts. Column 7 shows the ranking order of IPFAC,the factor score of the three investor protection indices, Investor ProtectionLaws, Investor Protection Enforcement, and Quality of Accounting Standardsas measured in La Porta et al. (1998) and Leuz et al. (2003). A high (low) rank-ing represents more (less) investor protection. It is evident that our proxy forinvestor protection is positively related to MGUI and BGUI. More specifically,the Pearson correlations between IPFAC ranking and MGUI and BGUI are0.70 and 0.32, respectively.

5.2. Logistic results

While it is evident that there is a positive relation between guidance andinvestor protection in a univariate context, we test our hypothesis using logistic


regressions of forecast guidance on proxies for investor protection and controlvariables. Table 4 contains multivariate results of the logistic regressions foreach firm i, in country k, in year t, in the following form:

MGUIiktðor BGUIiktÞ¼ a0 þ a1IPFACk þ a2USi þ a3GROWTHit þ a4VARit

þ a5TSBIASit þ a6ANBIASit þ a7SKEWi þ a8LCAPit

þ a9GDPkt þ a10YEARt þ e; ð5Þ

Table 4Logistic regressions of forecast guidance—all observations:MGUIikt(or BGUIikt) = a0 + a1IPFACk + a2USi + a3GROWTHit + a4VARit + a5TSBIASit

+ a6ANBIASit + a7SKEWi + a8LCAPit + a9GDPkt + a10YEARt + e

Independentvariable

Predictedrelation

Panel A: MGUI Panel B: BGUI

Estimatedcoefficient

Pr > Chi-square Estimatedcoefficient

Pr > Chi-square

Intercept �1.6078 <0.0001 �1.0051 <0.0001IPFAC + 0.3911 <0.0001 0.1677 <0.0001US + 0.2366 <0.0001 0.3119 <0.0001GROWTH ? 0.0056 <0.0001 �0.0095 <0.0001VAR ? �0.0047 <0.0001 �0.0015 0.0009TSBIAS ? 0.0199 0.0395 �0.2884 <0.0001ANBIAS ? 2.9975 <0.0001 2.8864 <0.0001SKEW ? 0.0216 0.0544 �0.0109 0.3817LCAP ? 0.0592 <0.0001 �0.0304 <0.0001GDP ? 14.1246 <0.0001 �3.5419 <0.0001YEAR ? �0.0027 0.6305 �0.0044 0.4733

N 31,864 31,864R-square 26.03% 20.31%Chi-square 6642.49 4522.23p-value <0.0001 <0.0001

Notes:We indicate the predicted relation only for the variables representing our hypothesis. MGUIand BGUI are defined in the notes to Tables 2 and 3. IPFAC is the factor score of three investorprotection indices: Investor Protection Laws, Investor Protection Enforcement, and Quality ofAccounting Standards as in La Porta et al. (1998) and Leuz et al. (2003). US is coded 1 if the firm�scountry is the US and 0 otherwise. GROWTH is I/B/E/S earnings growth index, measured as theaverage annualized earnings per share growth over the past five years. VAR is I/B/E/S earningsvariability index, measured as the mean absolute difference between actual reported earnings pershare and a five-year historical EPS growth trend line, expressed as a percent of the earnings pershare trend. TSBIAS is time-series forecast bias, measured as the difference between current yearearnings and its lag scaled by the absolute value of current earnings. ANBIAS is analyst forecastbias, measured as the difference between current earnings and the analyst consensus forecast, scaledby the absolute value of current earnings. SKEW is earnings skewness, the statistical skewness ofthe distribution of the earnings to price ratio. LCAP is the log of market capitalization measured inmillions of US dollars. GDP is the country�s seasonally adjusted GDP growth rate. YEAR is 1–10for calendar years 1991–2000, respectively.


where MGUI and BGUI are forecast guidance measures based on Matsumoto(2002) and Bartov et al. (2002), respectively.

IPFAC is the factor score of the three investor protection indices, InvestorProtection Laws, Investor Protection Enforcement, and Quality of AccountingStandards as in La Porta et al. (1998) and Leuz et al. (2003). The expected signof IPFAC is positive according to our hypothesis that managers in strong-investor-protection countries use forecast guidance relatively more than man-agers in weak-investor-protection countries.

US is a binary variable denoting US (1), or non-US (0). We expect US to bepositive because: (1) Brown and Higgins (2001) show that there is more earn-ings surprise avoidance in the US than in any other country while (2) Bhattach-arya et al. (2003) and Leuz et al. (2003) show that there is less earningsmanagement in the US than in any other country. When combined, thesetwo findings suggest that there is more forecast guidance in the US than inany other country.

GROWTH is earnings growth. VAR is earnings variability. TSBIAS is time-series forecast bias. ANBIAS is analyst forecast bias. SKEW is earnings skew-ness. LCAP is the log of market capitalization. GDP is GDP growth rate.YEAR is calendar year, 1991–2000. All variable measurements are discussedin Section 3. All continuous data are winzorized at the 1st and 99th percentiles.We have no expectation regarding signs of the control variables for eitherMGUI or BGUI, and they may differ for MGUI and BGUI as they representdifferent proxies for the likelihood of forecast guidance.

Table 4 shows that IPFAC is positive in both the MGUI and BGUI models,after controlling for other factors. IPFAC is 0.3911 in the MGUI model and0.1677 in the BGUI model (both significant at a p-value of <0.0001). Ourresults are consistent with our hypothesis that managers in strong-investor-protection countries use forecast guidance more than managers in weak-inves-tor-protection countries. Also as expected, US is positive in both the MGUIand BGUI models, after controlling for other factors. More precisely, US is0.2366 and 0.3119 (significant at a p-value of <0.0001) in the MGUI and BGUImodels, respectively. The VIF factors of regression models similar to those inTable 4 are no larger than 1.98, indicating that multicollinearity is not a prob-lem. In addition, the Table 4 results appear economically significant. TheMGUI model suggests that a one point increase in IPFAC increases the logodds of forecast guidance by 0.3911, or an increase by 47.86% in the odds ratioof forecast guidance.13

Table 5 replicates Table 4 using only non-US observations to see if our re-sults are driven by the large number of US observations. IPFAC is positive and

13 From solving the log odds equation: lny2–lny1 = 0.3911, y1 and y2 being the odds of forecastguidance for two identical observations except observation 2 has a higher IPFAC score by 1 unit.

Table 5Logistic regressions of forecast guidance—Non-US observations:MGUIikt(or BGUIikt) = a0 + a1IPFACk + a2GROWTHit + a3VARit + a4TSBIASit + a5ANBIASit

+ a6SKEWi + a7LCAPit + a8GDPkt + a9YEARt + e

Independentvariable

Predictedrelation

Panel A: MGUI Panel B: BGUI

Estimatedcoefficient

Pr > Chi-square Estimatedcoefficient

Pr > Chi-square

Intercept �1.7756 <0.0001 �0.8072 <0.0001IPFAC + 0.3747 <0.0001 0.1333 <0.0001GROWTH ? 0.0027 <0.0001 �0.0068 <0.0001VAR ? �0.0011 <0.0637 �0.0019 0.0049TSBIAS ? �0.0281 0.0315 �0.2810 <0.0001ANBIAS ? 2.7717 <0.0001 2.4178 <0.0001SKEW ? 0.0216 0.1740 �0.0421 0.0151LCAP ? 0.0711 <0.0001 �0.0220 <0.0512GDP ? 14.1426 <0.0001 �3.9226 <0.0001YEAR ? 0.0012 0.8955 �0.0367 0.0003

N 15,866 15,866R-square 31.46% 21.41%Chi-square 3930.8987 2265.6148p-value <0.0001 <0.0001

Notes:We indicate the predicted relation only for the variables representing our hypothesis. MGUIand BGUI are defined in the notes to Tables 2 and 3. IPFAC is the factor score of three investorprotection indices: Investor Protection Laws, Investor Protection Enforcement, and Quality ofAccounting Standards as in La Porta et al. (1998) and Leuz et al. (2003). GROWTH is I/B/E/Searnings growth index, measured as the average annualized earnings per share growth over the pastfive years. VAR is I/B/E/S earnings variability index, measured as the mean absolute differencebetween actual reported earnings per share and a five-year historical EPS growth trend line,expressed as a percent of the earnings per share trend. TSBIAS is time-series forecast bias, mea-sured as the difference between current year earnings and its lag scaled by the absolute value ofcurrent earnings. ANBIAS is analyst forecast bias, measured as the difference between currentearnings and the analyst consensus forecast, scaled by the absolute value of current earnings.SKEW is earnings skewness, the statistical skewness of the distribution of the earnings to priceratio. LCAP is the log of market capitalization measured in millions of US dollars. GDP is thecountry�s seasonally adjusted GDP growth rate. YEAR is 1–10 for calendar years 1991–2000,respectively.


at a p-value of <0.0001 in both the MGUI and BGUI models. This result sug-gests that our findings that managers in strong-investor-protection countriesuse forecast guidance more than managers in weak-investor-protection coun-tries is not driven by the US observations in our sample.

Our sample includes data for firms over multiple years so our results are vul-nerable to dependency among firm observations, resulting in under-estimatedstandard errors and overstating the statistical significance of our coefficientestimates. To mitigate this concern, we assume a lag -1 autoregressive structureacross years, and replicate our logistic analysis using robust standard errors.


Un-tabulated results show that IPFAC is significantly positive (p-value of<0.0001), consistent with our hypothesis that managers in strong-investor-pro-tection countries resort to forecast guidance more than managers in weak-investor-protection countries.

As further analysis, we aggregate our data at the country level to observe thecorrelation between countries� mean forecast guidance measures with individ-ual countries� investor protection measures after controlling for countries� aver-age GDP. Due to our use of only 21 observations, our test results are lessreliable. Nevertheless, the correlation between MGUI and IPFAC is still signif-icantly positive (p-value = 0.023), consistent with our firm-level results.

6. Conclusion

Managing reported earnings upward and guiding analyst earnings forecastsdownward are the two ways managers can use to avoid negative earnings sur-prises (Matsumoto, 2002). Most prior research has focused on upwards earn-ings management as a mechanism to avoid negative earnings surprises. Weexamine downward forecast guidance in an international context, an issuenot addressed by the prior literature. Examining forecast guidance in an inter-national context is important for three reasons. First, it enhances our under-standing of corporate disclosure processes via informal channels. Second, itincreases our knowledge of the implications of public policies governing infor-mal disclosures. Third, it has the potential to reconcile some seemingly contra-dictory results in the literature.

We employ common proxies for investor protection as in La Porta et al.(1998) and Leuz et al. (2003). We measure forecast guidance using techniquessimilar to those developed by Matsumoto (2002) and Bartov et al. (2002), andwe perform extensive tests to show their validity. Based on data from 21 coun-tries, we find that managers in strong-investor-protection countries use forecastguidance more than managers in weak-investor-protection countries. Our re-sults shed light on managerial strategies for avoiding negative earnings sur-prises by using forecast guidance in response to weak regulations of informaldisclosures. Our finding that US managers use forecast guidance more thannon-US managers helps to reconcile the seemingly-contradictory results ofBrown and Higgins (2001) and Leuz et al. (2003), who, respectively, show that,relative to managers in other countries, US managers are more likely to avoidnegative earnings surprises but they are less likely to manage reported earnings.

While we model forecast guidance based on prior research and performextensive validity checks, our measures proxy for the likelihood of the existenceof forecast guidance and do not provide direct evidence of actual forecast guid-ance from management to analysts for a particular firm or country. Unfortu-nately, we cannot separate analyst downward bias from forecast guidance with


precision since both result from interactions between analysts and managers. Inspite of the fact that we use many controls, it is conceivable that our guidancemeasures are driven by forecast attributes we ignore.

Acknowledgments

We have benefited from the comments of Sudipta Basu, George Benston,David Burgstahler, Marcus Caylor, Jennifer Francis, Don Herrmann, Indra-rini Laksmana, Christian Leuz, Tom Lopez, Liyu Luo, Gary Meek, EmadMohammad, Siva Nathan, Joseph Petruccelli, Arianna Pinello, Grace Pownall,Wayne Thomas, participants of the 2002 Southeastern Accounting SummerResearch Colloquium, 2002 Conference on Financial Economics and Account-ing, and workshops at Georgia State University and Oklahoma State Univer-sity. We thank Thomson Financial I/B/E/S for providing earnings per shareforecast data, which is part of its broad academic program to encourage earn-ings expectations research.

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managers’ forecast guidance of analysts: international evidence

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