Managers’ forecast guidance of analysts: International evidence

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

www.elsevier.com/locate/jaccpubpol

ManagersÕ forecast guidance of analysts: International evidence Lawrence D. Brown

a,*

, Huong N. Higgins

b

a

b

J. Mack Robinson College of Business, School of Accountancy, Georgia State University, Atlanta, GA 30302-4050, United States Department of Management, Worcester Polytechnic Institute, Worcester, MA 01609, United States

Abstract We consider forecast guidance as a mechanism that managers use to avoid negative earnings surprises. Modeling forecast guidance using methods by Matsumoto, [Accounting Review 77 (3) (2002) 483–514] and Bartov et al. [Journal of Accounting and Economics 33 (2) (2002) 173–204], we show that managers in strong-investor-protection countries are more likely to utilize forecast guidance to avoid negative earnings surprises than managers in weak-investor-protection countries. We also show that US managers are more prone to use forecast guidance to avoid negative earnings surprises than managers in other countries. Our results provide insight into the information dissemination process and how managers behave in response to weak regulation of informal disclosures in different investor protection environments. Ó 2005 Elsevier Inc. All rights reserved. Keywords: Forecast guidance; Analysts; Earnings; International; Investor protection

*

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

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

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 todayÕs corporate culture (Bartov et al., 2002). Managers seek to avoid negative earnings surprises to avoid litigation, maximize the value of their compensation, and boost their credibility (Brown and Higgins, 2001; Bartov et al., 2002). There are two ways that managers can avoid negative earnings surprises: (1) manage reported earnings upward and/or (2) guide analystsÕ earnings expectations downward.1 The academic literature generally focuses on managing reported earnings upward as a way to avoid negative earnings surprises (Healy and Wahlen, 1999), but the business press considers downward forecast guidance as crucial to the earnings 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 negative earnings surprises in an international setting. Managers make disclosures to investors and analysts through a variety of channels using both formal and informal disclosures (Rao and Sivakumar, 1999). Accounting research has focused primarily on formal disclosures, such as financial accounting reports, rather than on informal disclosures, such as analyst forecast guidance. Formal disclosures represent only a portion of the disclosure process, are often not the primary vehicle for apprising shareholders of important developments, and are out-of-date when they are distributed. On the other hand, informal disclosures have increasingly become the main vehicle for informing the market on a timely basis. Many companies disclose information through informal means, such as press releases, promotional materials, speeches, and conversations with analysts (Brown, 2005). Given the role of informal disclosures, research on forecast guidance is important because it improves our understanding of how and why firms disseminate informal disclosures. Research on forecast guidance is also important because it has implications for public policies addressing earnings surprise games. While public policies regulating formal disclosures exist in most countries, policies regulating informal disclosures are virtually absent. Informal communications such as press releases and communications to shareholders remain virtually free of express regulation. The US has almost no direct regulation regarding informal disclosure (Brown, Chapter 2.06, p. 4, 2005). Rather, informal communications are regulated under the antifraud provisions of the Exchange Act, mainly Rule 10b-5, which places a blanket prohibition on firmsÕ 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 reported earnings 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 content and timing of corporate communications. US antifraud provisions are at best an imperfect regulation mechanism due to a lack of systematic rules, and no country has better regulation mechanisms of informal disclosures than the US. For example, in Continental Europe, EC Listing and Reporting Directives regulating disclosures adhere to the principles of materiality, clear disclosure, current information, standard format, cautionary statements in forward-looking statements, and equal treatment of investors, but the content and timeliness of informal disclosures are not subjected to regulatory supervision (Baums, 2002). We contribute to the public policy literature by examining managerial behavior in a specific context, managing earnings surprises in response to weak regulation of informal disclosures in different investor protection environments. In strong-investor-protection environments, characterized by common-law and market orientation (La Porta et al., 1998), managers seek to avoid negative earnings surprises because strong-investor-protection environments place high emphasis on stock returns (Brown and Higgins, 2001). There are two ways to avoid negative earnings surprises: manage reported earnings upward and/or guide analyst forecasts downward (Matsumoto, 2002). Given the relative difficulty of managing earnings upward in stronginvestor-protection environments, managers are relatively more likely to use forecast guidance in these environments because the regulation of forecast guidance is far less rigorous than that of managing earnings. On the contrary, in weak-investor-protection environments, which are characterized by codelaw and credit orientation, there is less emphasis on stock price performance so managers are less pressured to avoid negative surprises (Brown and Higgins, 2001). When there is less pressure to meet or beat analyst estimates, managers are relatively less likely to use forecast guidance. Further, because regulation of reported earnings is not stringent in weak-investor-protection countries, managers wishing to avoid negative surprises in these environments are more likely to use earnings management, reducing the role of forecast guidance. Drawing on the above, we hypothesize and show that managers in strong-investor-protection environments are relatively more likely to use forecast guidance to avoid negative earnings surprises vis a vis managers in weak-investor-protection environments. We also show that US managers are more likely to use forecast guidance than non-US managers, consistent with prior findings that US managers manage earnings surprises more (Brown and Higgins, 2001) but manage reported earnings less (Bhattacharya et al., 2003; Leuz et al., 2003) than their international peers. We proceed as follows. Section 2 develops our hypothesis. Section 3 describes our sample and methodology. Section 4 discusses results of preliminary analyses. Section 5 presents results of our main analyses. Section 6 concludes.

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

283

2. Hypothesis development 2.1. Weak regulation of informal disclosures Public policies regulating formal accounting reports exist in most countries. In contrast, public policies regulating informal communication channels between managers and analysts are less developed in all countries. With the exception of Regulation FD, there is no direct regulation in the US regarding informal disclosures (Brown, Chapter 2.06, p. 4, 2005). Instead, such informal communications are regulated under the antifraud provisions of the securities laws, mainly Rule 10b-5, which constitutes a blanket prohibition on fraudulent misstatements by any company. The antifraud provisions have a pronounced impact on the regulation of informal disclosures, because they mandate to some degree the accuracy, completeness, and timeliness of disclosure, and impose a duty of continued accuracy once disclosure has occurred. The only direct regulation of informal disclosures in the US is Regulation FD, which aims at eliminating the practice of selective disclosure of material information, particularly to analysts. While the US antifraud provisions are the vehicles for informal disclosure regulation, companies are often unaware of their implications in the absence of a systematic set of rules and regulations. The provisions have regulated informal communications in a haphazard fashion. With the recent explosion in shareholder class actions, courts have shown increasing dissatisfaction with the natural development of the law under Rule 10b-5, resulting in some courts being willing to grant motions to dismiss despite facts to the contrary. Regulation of informal disclosures in the US is imperfect at best. Many non-US countries adhere to disclosure principles similar to those in the US. For example, in Continental Europe, EC Listing and Reporting Directives regulating disclosures adhere to the principles of materiality, clear disclosure, current information, standard format, cautionary statements in forwardlooking statements, and equal treatment of investors (Baums, 2002). However, in practice, informal disclosures are not subjected to regulatory supervision with respect to their content and timeliness in Continental Europe (Baums, 2002). Overall, general disclosure principles are similar across countries, but there is little direct regulation of informal disclosures other than generic anti-tort provisions in respective countriesÕ laws, and there is little (if any) empirical research on cross-country variation in regulation of informal disclosures. Regulation of informal disclosures is weaker, or not stronger, than regulation of formal disclosures in all countries. 2.2. Forecast guidance to avoid negative earnings surprises We draw upon the investor protection and earnings surprise management literatures to hypothesize how managersÕ responses to weak regulation of

284

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

informal disclosures differ across investor protection environments. In stronginvestor-protection environments, due to their market orientation and high emphasis on stock price performance, managers have incentives to avoid negative earnings surprises to safeguard stock valuation (Brown and Higgins, 2001). For example, there are three sources of managersÕ incentives to avoid negative surprises to safeguard stock price performance: (1) the presence of independent board directors who stress price performance, (2) the market for corporate control that removes managers of undervalued firms, and (3) equity-based executive compensation contracts (Brown and Higgins, 2001). There are two ways to avoid negative earnings surprises: manage reported earnings upward and/or manage analyst forecasts downward (Matsumoto, 2002). In strong-investor-protection environments, managers are less likely to manage earnings upward because strong investor protection limits managersÕ ability to acquire private control benefits, reducing their incentives to mask firm performance (Leuz et al., 2003). Strong-investor-protection environments are characterized by rules of law that allow minority shareholders to challenge directorsÕ decisions in court to protect themselves against expropriation by controlling managers, and by court systems that effectively enforce investorsÕ rights or substitute for weak investor-protection laws. For example, shareholdersÕ right to vote by mail when they cannot travel to shareholdersÕ meetings empowers investors against abuse by managers. Given the difficulty of managing reported earnings upward in strong-investor-protection environments, and the lack of rigorous regulations regarding forecast guidance, managers in strong-investor-protection environments are more likely to use downward forecast guidance to avoid negative earnings surprises. In contrast, in weak-investor-protection environments, managers do not have strong incentives to avoid negative surprises (Brown and Higgins, 2001), so they have less need to use downward forecast guidance. Furthermore, managers in weak-investor-protection environments wishing to avoid negative earnings surprises are better able to manage earnings upward due to weak regulation of reported earnings, so they have less need to use downward forecast guidance. Based on the above, our hypothesis (in alternative form) is: H1: Managers in strong-investor-protection countries are more likely to use downward forecast guidance to avoid negative earnings surprises than managers in weak-investor-protection countries. 3. Sample and methodology 3.1. Measures of downward forecast guidance (dependent variable) To enhance our studyÕs validity, we use two measures of downward forecast guidance, one based on Matsumoto (2002), and the other based on Bartov et al. (2002).

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

285

3.1.1. Measure based on Matsumoto (2002) Similar to Matsumoto (2002), we measure forecast guidance conditional on meeting 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 the unexpected portion of the earnings forecast (UEF), measured as the difference between the consensus analyst earnings forecast (CF) and the expected analyst earnings forecast (E[F]) for the period: UEFijkt ¼ CFijkt
E½F ijkt .

ð1Þ

The expected analyst forecast (E[F]) in Eq. (1) is modeled using a random walk model (EPS from the previous period) with drift (E[D]): E½F ijkt  ¼ EPSijkt
1 þ E½Dijkt .

ð2Þ

Expected 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)). Expected drift uses actual yearly drift (D) and ending price (PRICE) for all firms in industry j, country k and year t (Eq. (4)) Dijkt ¼ EPSijkt
EPSijkt
1

ð3Þ

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

ð4Þ

and

In Eq. (4), we use the values of ajkt and bjkt only when they can be estimated using at least ten firms in the same country-industry-year (two-digit SIC code for industry). All measurements are based on US dollars using the exchange rate prevailing on the first day of the month after the fiscal year end.2 Our model and restriction to ten or more firms is similar to Matsumoto (2002), except we examine international firms, use annual data,3 account for currency differences, and exclude the portion of the expected forecast reflected in cumulative 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 for estimating regression equation (4). We use exchange rates at fiscal year end to mitigate the effects of currency 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 difference between our metric and MatsumotoÕs. New information is not impounded in stock prices quickly for many non-US firms traded in relatively poorly developed markets (Ball et al., 2000). When new information is impounded in stock prices quickly for a firm performing well, this exclusion results in a more negative drift, a more negative expected forecast, and a more positive guidance measure compared to Matsumoto (2002). Because new information is impounded in stock prices more quickly for firms in well-developed markets with strong-investor-protection, and because stock prices generally rose during our sample period, this exclusion makes our tests more conservative.

286

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

is coded 1 if UEF from Eq. (1) is negative and 0 otherwise. We define downward forecast guidance as occurring (MGUI = 1) when DOWN = 1 and reported earnings meet or beat analyst expectations (i.e., the last consensus estimate 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 characterized as having downward forecast guidance (i.e., BGUI = 1) when: (1) current earnings are less than the first consensus forecast, (2) the last consensus forecast is less than the first consensus forecast, and (3) current earnings are greater 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 protection. The country-level factors are discussed by La Porta et al. (1997, 1998), who distinguish between laws protecting outside investors and the quality of law enforcement. Laws protecting outside investors consist of legal rules concerning investorsÕ rights such as voting power, ease of participation in corporate voting, and legal protection against expropriation by management. Enforcement relates to important intermediary functions such as an effective judicial system to empower courts in law enforcement, and an effective accounting system to render company disclosures interpretable and verifiable. Consistent with La Porta et al. (1998) and Leuz et al. (2003), we use the antidirector rights index5 to proxy for investor protection laws, the enforcement index6 to measure investor protection enforcement, and the index for accounting 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 legal system favors minority shareholders against managers or dominant shareholders in corporate decision-making processes. The index is computed based on six anti-director rights, namely the right to: (1) vote by mail, (2) sell shares around the date of shareholdersÕ meeting, (3) have cumulative voting for directors and/or proportional representation on the board, (4) challenge directorsÕ decisions in court and/or to force the company to repurchase shares of minority shareholders who object to certain management decisions, (5) have preemptive rights to buy new issues of stock, and (6) call an extraordinary shareholdersÕ 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, for example, by allowing active and well-functioning courts to step in and rescue investors abused by managers. The three enforcement variables are: (1) judicial system efficiency, (2) assessment of rule of 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.

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

287

especially when investor rights are weak. We use principal factor analysis to obtain a single factor score to condense these three indices into one investor protection factor (IPFAC). The factor score receives the highest loadings from Investor Protection Laws (0.92352), followed by Investor Protection Enforcement (0.7561), and Quality of Accounting Standards (0.8604), indicating that factor 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 variable because our sample consists of a majority of US observations and we wish to ensure that US data do not drive our results. US and IPFAC may be correlated so we test for but do not find harmful multicollinearity. In a robustness check, we replicate our analysis without US observations and obtain consistent results. We cannot separate analyst downward bias from forecast guidance with precision because both result from interaction between analysts and managers. To mitigate this problem, we control for potential confounding effects of 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 definition of earnings growth (GROWTH), i.e., average annualized earnings per share growth over the prior five years. We also use the I/B/E/S definition of earnings variability (VAR), i.e., mean absolute difference between reported earnings per share and a five-year historical EPS growth trend line from the current year, expressed as a percentage of trend line earnings per share. We measure time-series forecast (TSBIAS) bias as the difference between the current yearÕs earnings and the previous yearÕs earnings, scaled by the absolute value of current earnings. We measure analyst forecast bias (ANBIAS) as the difference between current earnings and the last analyst consensus forecast before the earnings announcement, scaled by the absolute value of current earnings. We measure earnings skewness (SKEW) as the statistical skewness of the distribution of the firmÕs earnings to price ratio over the sample 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 firmÕs market capitalization in millions of US dollars in the current year. GDP is the annual growth rate in the current year of a countryÕs real seasonally adjusted GDP. YEAR is coded as 1 to 10, for the respective calendar years 1991–2000.

8

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

288

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

3.4. Data sources and sample selection We obtain our data from I/B/E/S and DataStream, products of Thomson Financial Inc. We use the I/B/E/S International Summary File for analyst consensus forecasts, actual earnings and stock prices, and Datastream for exchange rates and GDP growth rates. We compute DOWN for firms with three or more years of data using the last annual consensus (mean) forecast before the annual earnings announcements. Following Matsumoto (2002), we restrict our sample to firms with at least ten observations in a given country-industry-year to calculate expected drift. To allow for reasonable sample sizes, we exclude countries with less than 100 firm-year observations of computable forecast guidance in the ten-year period, 1991–2000. Table 1 shows the distribution of firms and observations by country. There are 43,360 forecasts of 10,659 firms from the US and 20 other countries (in alphabetical order): Australia, Canada, France, Germany, Greece, Hong Kong, India, Italy, Japan, Korea, Malaysia, Norway, South Africa, Spain, Sweden, Switzerland, Taiwan, Thailand, Turkey, and the UK. The US has both the most firms (5283) and observations (21,799). Seven non-US countries have at least 200 firms each: Japan (1646), the UK (1244), Korea (584), Taiwan (259), Thailand (245), Germany (216), and Canada (200), and eight non-US countries have at least 500 observations: the UK (6556), Japan (6140), Korea (2429), Taiwan (927), Thailand (818), Canada (727), Germany (643), and Australia (576). Due to data availability for many control variables, our final sample is reduced to 31,864.

4. Preliminary analyses Similar to Matsumoto (2002), we conduct several analyses to assess the validity of DOWN. Our first four analyses show that DOWN adheres to the expected patterns of earnings surprise management as documented in prior studies. Our fifth analysis compares our two measures of forecast guidance, MGUI and BGUI. Our sixth analysis replicates our first five analyses omitting US forecasts. 4.1. Ex-post pattern in meet or beat estimates Matsumoto (2002) examines whether forecast guidance facilitates meeting or beating analyst consensus earnings estimates. She finds that DOWN is coded 1 in 54.12% of her sample when US firms report quarterly earnings that meet or beat analyst estimates, but in only 49.23% of her sample when they report quarterly earnings falling short of analyst estimates. Her chi-square statistic of 33.48 is significant at a p-value <0.0001. We conduct similar analyses and

Table 1 Distribution of forecast guidance in international firms

Australia Canada France Germany Greece Hong Kong India Italy Japan Korea Malaysia Norway South Africa Spain Sweden Switzerland Taiwan Thailand Turkey UK US Total

No. of firms

No. of observations

161 200 120 216 81 73 90 47 1646 584 53 39 80 34 66 62 259 245 76 1244 5283

576 727 381 643 179 291 198 177 6140 2429 206 104 296 147 155 299 927 818 312 6556 21,799

10,659

43,360

No. of observations as percent of total sample (%) 1.33 1.68 0.88 1.48 0.41 0.67 0.46 0.41 14.16 5.60 0.48 0.24 0.68 0.34 0.36 0.69 2.14 1.89 0.72 15.12 50.27 100

Percent of observations with MGUI = 1 (%)

Percent of observations with BGUI = 1 (%)

Investor protection ranking

32.29 26.82 20.74 24.88 25.70 19.24 21.72 19.21 24.02 14.66 21.36 29.81 25.68 31.97 37.42 28.76 21.04 23.59 10.58 36.96 37.91

5.73 8.39 5.25 6.84 3.35 1.38 3.03 6.22 13.57 8.69 2.91 11.54 4.39 0.68 9.03 4.01 7.55 8.92 5.45 8.27 25.00

16 19 11 7 2 15 6 5 13 4 14 18 10 9 20 12 8 3 1 21 17

32.5

22.23

289

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 modeled measure 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 less than the first consensus forecast, and (3) current earnings are greater than or equal to the last consensus forecast, and 0 otherwise. Investor Protection Ranking is the rank ordering of the IPFAC scores, where IPFAC is the factor score of three investor protection indices: Investor Protection Laws, Investor Protection Enforcement, and Quality of Accounting Standards as in La Porta et al. (1998) and Leuz et al. (2003). The highest (lowest) score represents the most (least) investor protection.

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

Country

290

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

Table 2 Contingency analyses of DOWN Guidance (DOWN = 1) Panel A: Contingency analyses of DOWN partitioned by sign of earnings surprise Sign of earnings surprise No Yes Positive 10,083 (41.71%) 14,092 (58.29%) Negative 9687 (50.49%) 9481 (49.51%) v2 = 332.73 p < 0.0001 Panel B: Contingency analyses of DOWN partitioned by sign of earnings Earnings Profit 15,945 (44.50%) Loss 3766 (50.72%) v2 = 95.89 p < 0.0001

19,885 (55.50%) 3659 (49.28%)

Panel C: Contingency analysis of DOWN for positive profit surprises partitioned by size of surprise Profit surprise Small positive 3380 (38.94%) 5301 (61.06%) Other positive surprises 4623 (43.66%) 5966 (56.34%) v2 = 43.82 p < 0.0001 Panel D: Contingency analysis of DOWN for negative loss surprises partitioned by size of surprise Loss surprise Extreme negative 1144 (61.01%) 731 (38.99%) Other negative surprises 1760 (49.77%) 1776 (50.23%) v2 = 62.25 p < 0.0001 Notes: DOWN is coded 1 if the modeled measure of unexpected earnings forecast (UEF) is negative and 0 otherwise. Earnings surprise is defined as actual minus predicted earnings, divided by the absolute value of actual earnings. Loss is defined as negative earnings. Profit is defined as nonnegative earnings. Positive earnings surprises within 5% of reported profits are termed small positive surprises. Negative earnings surprises less than (more negative than) 100% of reported losses are termed extreme negative surprises.

report our results in Panel A of Table 2. When international managers report annual earnings meeting or beating analyst estimates, DOWN = 1 in 58.29% of our sample. When international managers report annual earnings falling short of analyst estimates, DOWN = 1 in 49.51% of our sample. The chi-square statistic of 332.73 is significant at a p-value of <0.0001, consistent with guidance being associated with surprise management in an international context. 4.2. Profits versus losses The impact of earnings announcements on valuation is more pronounced for profitable firms (Hayn, 1995), providing managers with greater incentives

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

291

to manage earnings surprises (Degeorge et al., 1999; Brown, 2001). If international managers use forecast guidance to manage earnings surprises, guidance should be more evident for profitable firms. Panel B, a contingency analysis of DOWN partitioned into firms reporting profits versus losses, shows that DOWN = 1 in 55.50% and 49.28% of profit and loss observations, respectively. The chi-square statistic of 95.89, significant at a p-value of <0.0001, is consistent with forecast guidance occurring relatively more often for profitable firms. 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 for profit firms and avoid extreme negative surprises for loss firms (Brown, 2001). We focus on small positive surprises for profit firms in this subsection and extreme negative surprises for loss firms in the next subsection. If international managers use forecast guidance to manage profit surprises, guidance should pertain more to small positive profit surprises than to other positive profit surprises. Panel C provides results of a contingency analysis of DOWN for firms partitioned into small versus other positive profit surprises. Similar to Brown and Higgins (2001), we define earnings surprises as actual minus predicted earnings, divided by the absolute value of actual earnings, and small positive surprises as those within 5% of reported profits. DOWN = 1 in 61.06% of small positive versus 56.34% of other positive profit surprises. The chi-square statistic of 43.82 is significant at a p-value of <0.0001, consistent with guidance pertaining more to firms reporting small versus other positive profit surprises. 4.4. Loss firms: extreme negative versus other negative surprises If managers use forecast guidance to manage loss surprises, guidance will be less evident when managers report extreme negative versus other negative loss surprises. Panel D presents findings of a contingency analysis of DOWN for loss firms partitioned into extreme versus other negative surprises. Similar to Brown and Higgins (2001), we consider surprises more negative than 100% of reported losses to be extreme negative surprises. DOWN = 1 in 38.99% of extreme negative versus 50.23% of other negative loss surprises. The chi-square statistic of 62.25 is significant at a p-value of <0.0001, showing less forecast guidance when firms report extreme negative loss surprises than when firms report other negative loss surprises. Overall, our Table 2 results suggest that DOWN, as measured by MGUI, captures forecast guidance as a mechanism for earnings surprise management.

292

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

Table 3 Contingency analyses of forecast guidance measures MGUI and BGUI Downward forecast guidance measured by MGUI

Downward forecast guidance measured by BGUI No guidance

Guidance

Total

No guidance Guidance

24,754 (84.58%) 8969 (63.65%)

4514 (15.42%) 5123 (36.35%)

29,268 (67.50%) 14,092 (32.50%)

Total

33,723 (77.77%) v2 = 2410.83 p < .0001

9637 (22.23%)

43,360 (100%)

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. DOWN is coded 1 if the modeled measure 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 less than the first consensus forecast, and (3) current earnings are greater than or equal to the last consensus forecast, and 0 otherwise.

4.5. Comparing our two measures of forecast guidance We examine differences and similarities between MGUI and BGUI, based on Matsumoto (2002) and Bartov et al. (2002), respectively, by showing a contingency analysis of MGUI versus BGUI in Table 3. Based on MGUI, 32.50% of the observations are characterized as forecast guidance, while based on BGUI, 22.23% of the observations are characterized as forecast guidance.9 This finding reveals that the underlying measurement methods differ, specifically the Bartov et al. (2002) method is more restrictive, classifying fewer observations as guidance than the Matsumoto (2002) method.10 Despite this difference, Table 3 shows that BGUI and MGUI essentially are consistent with each other, i.e., they agree or capture the same concept. Specifically, when MGUI = 0, only 15.42% of observations have BGUI = 1, but 84.58% of observations have BGUI = 0. Similarly, when BGUI = 0, only 26.60% of observations have MGUI = 1, but 73.40% of observations have MGUI = 0.11 We replicated the preliminary analyses reported in panels A through C of Table 2 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 of 43,360 observations (or 22.23%). 10 Our results are comparable with those reported by Bartov et al. (2002) and Matsumoto (2002). Bartov et al.Õs (MatsumotoÕs) 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 is 16.92% 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 with other negative loss surprises and, under Bartov et al. (2002) all guidance observations are defined to have positive surprises.

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

293

results but we find similar patterns of earnings surprise management, consistent with 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 sample consists 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 significant and consistent with those reported in Sections 4.1–4.5. Specifically, we find that downward forecast guidance is more evident when firms report earnings that meet or beat versus fall short of analystsÕ forecasts, profits versus losses, and small profit surprises versus other positive profit surprises. Moreover, downward forecast guidance is less evident when firms report extreme versus other 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 US has 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%). Column 6 shows the mean BGUI percentages by country. The US has the largest percent (25%), followed by Japan (13.57%) and Norway (11.54%). Spain has the smallest percent (0.68%), preceded by Hong Kong (1.38%) and Malaysia (2.91%). The summary data highlight the highest forecast guidance measures in the US, consistent with US managers using forecast guidance more than their international counterparts. Column 7 shows the ranking order of IPFAC, the factor score of the three investor protection indices, Investor Protection Laws, Investor Protection Enforcement, and Quality of Accounting Standards as measured in La Porta et al. (1998) and Leuz et al. (2003). A high (low) ranking represents more (less) investor protection. It is evident that our proxy for investor protection is positively related to MGUI and BGUI. More specifically, the Pearson correlations between IPFAC ranking and MGUI and BGUI are 0.70 and 0.32, respectively. 5.2. Logistic results While it is evident that there is a positive relation between guidance and investor protection in a univariate context, we test our hypothesis using logistic

294

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

regressions of forecast guidance on proxies for investor protection and control variables. Table 4 contains multivariate results of the logistic regressions for each firm i, in country k, in year t, in the following form: MGUIikt ðor BGUIikt Þ ¼ a0 þ a1 IPFACk þ a2 USi þ a3 GROWTHit þ a4 VARit þ a5 TSBIASit þ a6 ANBIASit þ a7 SKEWi þ a8 LCAPit þ a9 GDPkt þ a10 YEARt þ e;

ð5Þ

Table 4 Logistic regressions of forecast guidance—all observations: MGUIikt(or BGUIikt) = a0 + a1IPFACk + a2USi + a3GROWTHit + a4VARit + a5TSBIASit + a6ANBIASit + a7SKEWi + a8LCAPit + a9GDPkt + a10YEARt + e Independent variable

Predicted relation

Intercept IPFAC US GROWTH VAR TSBIAS ANBIAS SKEW LCAP GDP YEAR

+ + ? ? ? ? ? ? ? ?

N R-square Chi-square p-value

Panel A: MGUI

Panel B: BGUI

Estimated coefficient

Pr > Chi-square

Estimated coefficient

Pr > Chi-square


1.6078 0.3911 0.2366 0.0056
0.0047 0.0199 2.9975 0.0216 0.0592 14.1246
0.0027

<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0395 <0.0001 0.0544 <0.0001 <0.0001 0.6305


1.0051 0.1677 0.3119
0.0095
0.0015
0.2884 2.8864
0.0109
0.0304
3.5419
0.0044

<0.0001 <0.0001 <0.0001 <0.0001 0.0009 <0.0001 <0.0001 0.3817 <0.0001 <0.0001 0.4733

31,864 26.03% 6642.49 <0.0001

31,864 20.31% 4522.23 <0.0001

Notes: We indicate the predicted relation only for the variables representing our hypothesis. MGUI and BGUI are defined in the notes to Tables 2 and 3. IPFAC is the factor score of three investor protection indices: Investor Protection Laws, Investor Protection Enforcement, and Quality of Accounting Standards as in La Porta et al. (1998) and Leuz et al. (2003). US is coded 1 if the firmÕs country is the US and 0 otherwise. GROWTH is I/B/E/S earnings growth index, measured as the average annualized earnings per share growth over the past five years. VAR is I/B/E/S earnings variability index, measured as the mean absolute difference between 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, measured as the difference between current year earnings and its lag scaled by the absolute value of current earnings. ANBIAS is analyst forecast bias, measured as the difference between current earnings 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 price ratio. LCAP is the log of market capitalization measured in millions of US dollars. GDP is the countryÕs seasonally adjusted GDP growth rate. YEAR is 1–10 for calendar years 1991–2000, respectively.

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

295

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, Investor Protection Laws, Investor Protection Enforcement, and Quality of Accounting Standards as in La Porta et al. (1998) and Leuz et al. (2003). The expected sign of IPFAC is positive according to our hypothesis that managers in stronginvestor-protection countries use forecast guidance relatively more than managers in weak-investor-protection countries. US is a binary variable denoting US (1), or non-US (0). We expect US to be positive because: (1) Brown and Higgins (2001) show that there is more earnings surprise avoidance in the US than in any other country while (2) Bhattacharya et al. (2003) and Leuz et al. (2003) show that there is less earnings management in the US than in any other country. When combined, these two findings suggest that there is more forecast guidance in the US than in any other country. GROWTH is earnings growth. VAR is earnings variability. TSBIAS is timeseries forecast bias. ANBIAS is analyst forecast bias. SKEW is earnings skewness. LCAP is the log of market capitalization. GDP is GDP growth rate. YEAR is calendar year, 1991–2000. All variable measurements are discussed in Section 3. All continuous data are winzorized at the 1st and 99th percentiles. We have no expectation regarding signs of the control variables for either MGUI or BGUI, and they may differ for MGUI and BGUI as they represent different 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 and 0.1677 in the BGUI model (both significant at a p-value of <0.0001). Our results are consistent with our hypothesis that managers in strong-investorprotection countries use forecast guidance more than managers in weak-investor-protection countries. Also as expected, US is positive in both the MGUI and BGUI models, after controlling for other factors. More precisely, US is 0.2366 and 0.3119 (significant at a p-value of <0.0001) in the MGUI and BGUI models, respectively. The VIF factors of regression models similar to those in Table 4 are no larger than 1.98, indicating that multicollinearity is not a problem. In addition, the Table 4 results appear economically significant. The MGUI model suggests that a one point increase in IPFAC increases the log odds of forecast guidance by 0.3911, or an increase by 47.86% in the odds ratio of forecast guidance.13 Table 5 replicates Table 4 using only non-US observations to see if our results are driven by the large number of US observations. IPFAC is positive and

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

296

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

Table 5 Logistic regressions of forecast guidance—Non-US observations: MGUIikt(or BGUIikt) = a0 + a1IPFACk + a2GROWTHit + a3VARit + a4TSBIASit + a5ANBIASit + a6SKEWi + a7LCAPit + a8GDPkt + a9YEARt + e Independent variable

Predicted relation

Intercept IPFAC GROWTH VAR TSBIAS ANBIAS SKEW LCAP GDP YEAR

+ ? ? ? ? ? ? ? ?

N R-square Chi-square p-value

Panel A: MGUI

Panel B: BGUI

Estimated coefficient

Pr > Chi-square

Estimated coefficient

Pr > Chi-square


1.7756 0.3747 0.0027
0.0011
0.0281 2.7717 0.0216 0.0711 14.1426 0.0012

<0.0001 <0.0001 <0.0001 <0.0637 0.0315 <0.0001 0.1740 <0.0001 <0.0001 0.8955


0.8072 0.1333
0.0068
0.0019
0.2810 2.4178
0.0421
0.0220
3.9226
0.0367

<0.0001 <0.0001 <0.0001 0.0049 <0.0001 <0.0001 0.0151 <0.0512 <0.0001 0.0003

15,866 31.46% 3930.8987 <0.0001

15,866 21.41% 2265.6148 <0.0001

Notes: We indicate the predicted relation only for the variables representing our hypothesis. MGUI and BGUI are defined in the notes to Tables 2 and 3. IPFAC is the factor score of three investor protection indices: Investor Protection Laws, Investor Protection Enforcement, and Quality of Accounting Standards as in La Porta et al. (1998) and Leuz et al. (2003). GROWTH is I/B/E/S earnings growth index, measured as the average annualized earnings per share growth over the past five years. VAR is I/B/E/S earnings variability index, measured as the mean absolute difference between 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, measured as the difference between current year earnings and its lag scaled by the absolute value of current earnings. ANBIAS is analyst forecast bias, measured as the difference between current earnings 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 price ratio. LCAP is the log of market capitalization measured in millions of US dollars. GDP is the countryÕs 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 suggests that our findings that managers in strong-investor-protection countries use forecast guidance more than managers in weak-investor-protection countries is not driven by the US observations in our sample. Our sample includes data for firms over multiple years so our results are vulnerable to dependency among firm observations, resulting in under-estimated standard errors and overstating the statistical significance of our coefficient estimates. To mitigate this concern, we assume a lag -1 autoregressive structure across years, and replicate our logistic analysis using robust standard errors.

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

297

Un-tabulated results show that IPFAC is significantly positive (p-value of <0.0001), consistent with our hypothesis that managers in strong-investor-protection countries resort to forecast guidance more than managers in weakinvestor-protection countries. As further analysis, we aggregate our data at the country level to observe the correlation between countriesÕ mean forecast guidance measures with individual countriesÕ investor protection measures after controlling for countriesÕ average GDP. Due to our use of only 21 observations, our test results are less reliable. Nevertheless, the correlation between MGUI and IPFAC is still significantly positive (p-value = 0.023), consistent with our firm-level results.

6. Conclusion Managing reported earnings upward and guiding analyst earnings forecasts downward are the two ways managers can use to avoid negative earnings surprises (Matsumoto, 2002). Most prior research has focused on upwards earnings management as a mechanism to avoid negative earnings surprises. We examine downward forecast guidance in an international context, an issue not addressed by the prior literature. Examining forecast guidance in an international context is important for three reasons. First, it enhances our understanding of corporate disclosure processes via informal channels. Second, it increases our knowledge of the implications of public policies governing informal disclosures. Third, it has the potential to reconcile some seemingly contradictory 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 techniques similar to those developed by Matsumoto (2002) and Bartov et al. (2002), and we perform extensive tests to show their validity. Based on data from 21 countries, we find that managers in strong-investor-protection countries use forecast guidance more than managers in weak-investor-protection countries. Our results shed light on managerial strategies for avoiding negative earnings surprises by using forecast guidance in response to weak regulations of informal disclosures. Our finding that US managers use forecast guidance more than non-US managers helps to reconcile the seemingly-contradictory results of Brown and Higgins (2001) and Leuz et al. (2003), who, respectively, show that, relative to managers in other countries, US managers are more likely to avoid negative earnings surprises but they are less likely to manage reported earnings. While we model forecast guidance based on prior research and perform extensive validity checks, our measures proxy for the likelihood of the existence of forecast guidance and do not provide direct evidence of actual forecast guidance from management to analysts for a particular firm or country. Unfortunately, we cannot separate analyst downward bias from forecast guidance with

298

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

precision since both result from interactions between analysts and managers. In spite of the fact that we use many controls, it is conceivable that our guidance measures 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, Indrarini Laksmana, Christian Leuz, Tom Lopez, Liyu Luo, Gary Meek, Emad Mohammad, Siva Nathan, Joseph Petruccelli, Arianna Pinello, Grace Pownall, Wayne Thomas, participants of the 2002 Southeastern Accounting Summer Research Colloquium, 2002 Conference on Financial Economics and Accounting, and workshops at Georgia State University and Oklahoma State University. We thank Thomson Financial I/B/E/S for providing earnings per share forecast data, which is part of its broad academic program to encourage earnings expectations research. References Ball, R., Kothari, S.P., Robin, A., 2000. The effect of international institutional factors on properties of accounting earnings. Journal of Accounting and Economics 29 (1), 1–51. Bartov, E., Givoly, D., Hayn, C., 2002. The rewards to meeting or beating earnings expectations. Journal of Accounting and Economics 33 (2), 173–204. Baums, T., 2002. Changing patterns of corporate disclosure in Continental Europe: The example of Germany. Law Working Paper No 04/2002, Johann Wolfgang Goethe University and European Corporate Governance Institute (ECGI). Bhattacharya, U., Daouk, H., Welker, M., 2003. The world price of earnings opacity. The Accounting Review 78 (3), 641–678. Bleakley, F.R., 1997. Again looks like a ÔgangbusterÕ quarter: Despite odds, strong earnings expected in 2nd period. The Wall Street Journal 229 (126), A2. Brown, J.R., 2005. The Regulation of Corporate Disclosure, third ed. Aspen Publishers, New York, NY. Brown, L.D., 2001. A temporal analysis of earnings surprises: Profits versus losses. Journal of Accounting Research 39 (2), 221–241. Brown, L.D., Higgins, H., 2001. Managing earnings surprises in the US versus 12 other countries. Journal of Accounting and Public Policy 20 (4–5), 373–398. Degeorge, F., Patel, J., Zeckhauser, R., 1999. Earnings management to exceed thresholds. Journal of Business 72 (1), 1–33. Hayn, C., 1995. The information content of losses. Journal of Accounting and Economics 20 (2), 125–153. Healy, P.M., Wahlen, J.M., 1999. A review of the earnings management literature and its implications for standard setting. Accountings Horizons 13 (4), 365–383. Ip, G., 1997. Traders laugh off the official estimate on earnings, act on whispered numbers. The Wall Street Journal 229 (11), C1–C3. La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R., 1997. Legal determinants of external finance. Journal of Finance 52 (3), 1131–1150.

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

299

La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R., 1998. Law and finance. Journal of Political Economy 106 (6), 1113–1155. Leuz, C., Nanda, D., Wysocki, P.D., 2003. Earnings management and investor protection: An international comparison. Journal of Financial Economics 69 (3), 505–527. Matsumoto, D.A., 2002. ManagementÕs incentives to avoid negative earnings surprises. Accounting Review 77 (3), 483–514. McGee, S., 1997. As stock market surges ahead, ‘‘predictable’’ profits are driving it. The Wall Street Journal 229 (87), C1–C3. Rao, H., Sivakumar, K., 1999. Institutional sources of boundary-spanning structures: The establishment of investor relations departments in the fortune 500 industrials. Organization Science 10 (1), 27–42. Talley, K., Craig, S., 2002. Merrill lynch directs analysts to look deeper than pro forma. The Wall Street Journal 239 (45), C1. Vickers, M., 1999. Ho-hum, another earnings surprise. Business Week (3630), 83–84.