ajinkya 2005

DOI: 10.1111/j.1475-679X.2005.00174.xJournal of Accounting Research

Vol. 43 No. 3 June 2005Printed in U.S.A.

The Association between OutsideDirectors, Institutional Investors

and the Properties of ManagementEarnings Forecasts

B I P I N A J I N K Y A , ∗ S A N J E E V B H O J R A J †,A N D P A R T H A S E N G U P T A ‡

Received 27 May 2003; accepted 20 September 2004

ABSTRACT

We investigate the relation of the board of directors and institutional own-ership with the properties of management earnings forecasts. We find thatfirms with more outside directors and greater institutional ownership are morelikely to issue a forecast and are inclined to forecast more frequently. In addi-tion, these forecasts tend to be more specific, accurate and less optimisticallybiased. These results are robust to changes specification, Granger causalitytests, and simultaneous equation analyses. The results are similar in the pre–and post–Regulation Fair Disclosure (Reg FD) eras. Additional analysis sug-gests that concentrated institutional ownership is negatively associated withforecast properties. This association is less negative in the post–Reg FD en-vironment, which is consistent with Reg FD reducing the ability of firms toprivately communicate information to select audiences.

∗University of Florida; †Cornell University; ‡University of Maryland. We thank Kate Camp-bell, Charles Lee, Carol Marquardt, James Peters, and workshop participants at AmericanUniversity, University of Florida, Georgia State University, Texas Christian University, and theAmerican Accounting Association’s 2003 annual meeting at Hawaii and the 14th annual Finan-cial Economics and Accounting Conference at Indiana University. We also thank ThompsonFinancial for generously providing us First Call analyst forecast and management forecast datathrough their Academic program.

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Copyright C©, University of Chicago on behalf of the Institute of Professional Accounting, 2005

344 B. AJINKYA, S. BHOJRAJ, AND P. SENGUPTA

1. Introduction

The U.S. corporate world is dominated by publicly traded firms with widelydispersed ownership. Typically, shareholders designate firm managers torun the company with the goal of maximizing shareholder wealth. Becauseshareholders do not participate in day-to-day corporate activities, implicitor explicit governance mechanisms assist in monitoring management ac-tions and protecting shareholder interests.1 In this article we examine theassociation between governance mechanisms and the extent and quality ofvoluntary disclosure. Specifically, we examine the association of governancemechanisms (i.e., outside directors and institutional investors) with man-agement earnings forecast occurrence, as well as with the specificity (i.e.,precision), accuracy, and optimism of the issued forecasts.

Prior work provides some evidence that the presence of outside directorsreduces the likelihood of financial fraud as well as earnings management(Dechow, Sloan, and Sweeney [1996], Beasley [1996], Klein [2002]).2 In ad-dition to monitoring the quality of financial information, outside directorscan play a role in determining and monitoring a firm’s voluntary disclosurepolicy. Owing to their fiduciary duty toward shareholders, directors in gen-eral have a responsibility to ensure greater transparency when it is in theshareholders’ interests. Consistent with this responsibility, Osterland [2004]reported that several companies, including Burlington and Comcast, haveestablished formal board-level committees to monitor corporate disclosure.Outside directors, by virtue of their position and presumed independence,are likely to possess greater incentives to ensure transparency when it is inthe shareholders’ interests, as compared with other directors. To the extentthat outside directors monitor disclosure policy and foster an environmentof greater transparency, we expect to find that firms with a larger propor-tion of these directors have a greater propensity to issue forecasts. We alsoexpect the forecasts to be more specific, more accurate, and less optimisti-cally biased. An alternative view is that outside directors may be ineffective,either because they have allegiance to company managers or because offear of litigation (Mace [1986], Jensen [1993], National Investor RelationsInstitute (NIRI) [2002]). This view works against our finding a positive asso-ciation between outside directors and forecast properties. Using a sample ofmanagement’s earnings forecasts issued from 1997 to 2002, we find resultsthat are consistent with the governance role of outside directors. We findthat the probability of occurrence of management earnings forecasts andthe frequency of such forecasts are positively associated with the percentage

1 See Shleifer and Vishny [1997] and Bushman and Smith [2001] for surveys of the corporategovernance literature in finance and accounting.

2 Prior work examining the role of outside directors finds that outside directors also playan active role in monitoring corporate activities, such as replacing poorly performing CEOs(Weisbach [1988]) and protecting shareholder interests during takeover fights (Shivdasani[1993]).

OUTSIDE DIRECTORS, INSTITUTIONAL INVESTORS, AND EARNINGS FORECASTS 345

of the board that consists of outsiders. The results also suggest that com-panies with a greater percentage of outside directors make more accurateand less optimistically biased earnings forecasts. However, we do not find asignificant association between outside directors and the specificity of theforecasts issued, which could be attributable to the directors’ fear of greaterlitigation exposure that might result from more specific forecasts.

Prior work examining the link between institutional investors and a firm’sinformation disclosure policy focuses on the association between analysts’disclosure ratings and institutional ownership.3 Healy, Hutton, and Palepu[1999] find that firms with sustained increases in disclosure (as proxied byanalyst ratings) experience an increase in institutional ownership. Busheeand Noe [2000] find an association between analysts’ disclosure ratingsand institutional ownership using both levels and changes analysis. In thisstudy we extend the prior research on institutional ownership by examiningthe association between institutional ownership and various properties ofmanagement forecasts. We focus on management earnings forecasts as apurer measure of voluntary disclosure of private information than analysts’scores. Focusing on management forecasts also allows us to examine spe-cific aspects of disclosure such as specificity and bias. Additional benefits offocusing on management forecasts are a larger sample size and the abilityto examine a more recent period, including the post–Regulation Fair Dis-closure (Reg FD) environment (analyst disclosure scores were discontinuedin 1996). Given that corporate disclosures, especially earnings forecasts, areclosely watched by institutions in addition to the constant investor probingof companies for their earnings outlook, we expect institutional ownershipto be positively associated with a firm’s propensity to issue forecasts as wellas the specificity and accuracy of forecasts issued, and negatively associatedwith managerial optimism. Consistent with our hypotheses, we find thatfirms with higher institutional ownership are more likely to issue a man-agement forecast. Over the six-year sample period, these firms also issueforecasts more frequently and the forecasts issued tend to be more specific(or precise). Using a subsample of point forecasts, we also find that forecastaccuracy (absolute forecast error) is positively (negatively) associated withinstitutional ownership, whereas forecast optimism bias is negatively asso-ciated with institutional ownership; that is, managers of firms with higherinstitutional ownership are more conservative in their forecasts.

Prior research suggests that institutions are not a homogenous group andthat their incentives are likely to be determined by ownership concentration

3 Other work examining the role of institutional investors focuses on whether these share-holders help protect investor interests in various contexts, including mergers and takeoversand management turnovers, and whether they help enhance firm performance and value (e.g.,Denis and Serrano [1996], Jarrell and Poulsen [1987], Kang and Shivdasani [1995], Shivdasani[1993], Weisbach [1988]). Other research explores the effects of governance on CEO com-pensation (e.g., Core, Holthausen, and Larcker [1999]) and effects of governance on the costof debt (Bhojraj and Sengupta [2003]).


(e.g., percentage of company’s common stock held by the five largest insti-tutional owners of the firm), which affects their ability to generate privateinformation and benefits (e.g., see Agrawal and Mandelker [1990], Porter[1992]). Testing the effect of such concentration (or blockholding), wefind that firms with concentrated institutional ownership, with their inher-ent ability to generate private information and benefits, are less likely topromote voluntary disclosure. In this case we find that firms are less likelyto issue earnings forecasts, and the forecasts issued tend to be less specific.Hence, concentration of institutional ownership has an adverse effect ondisclosure properties.

We examine several alternative explanations for our findings. For exam-ple, prior work exploring the effect of disclosure on institutional ownership(Healy, Hutton, and Palepu [1999], Bushee and Noe [2000]) suggests thatinstitutions prefer to buy stock in firms that have superior disclosure or haveexperienced sustained disclosure increases. However, it seems reasonablethat once institutions invest in a particular company they are likely to haveadded incentives to encourage further improvements in disclosure. This sug-gests that the link between institutional ownership and disclosure is likelyto be endogenous. We carry out several tests examining this issue, includingusing changes specification and lead-lag (Granger causality) analysis, andcontrolling for endogeneity between the variables. The link between insti-tutional ownership and forecast properties could also be driven by analystfollowing, which is shown in prior work to be related to both disclosure andinstitutional holding (see, for example, Lang and Lundholm [1996] andO’Brien and Bhushan [1990]). Similarly, the results on board compositioncould be driven by underlying firm characteristics that are associated withboth disclosure and board composition. We carry out several robustness teststo reduce the possibility that these potential alternative explanations are thesource of our results. Although it is impossible to eliminate these explana-tions, the additional tests indicate an association between outside directors,institutional ownership, and forecast properties, thereby providing greaterconfidence in interpreting the results.

Our sample includes annual earnings forecasts issued from 1997 to 2002.In October 2000 a structural change occurred in the voluntary disclosureregulation, with the introduction of Reg FD, which prohibits firms fromprivately disclosing information to select audiences. Although this regula-tion has increased the number of forecasts issued (Heflin, Subramanyam,and Zhang [2003], Bailey et al. [2003]), the question is whether outsidedirectors and institutional ownership can explain cross-sectional variationin forecast issuances and their properties after Reg FD. We examine thisissue as well as any effect that the structural shift might have had on theincentives of outside directors and institutional investors through a sub-period analysis. The results suggest no significant differences in the as-sociation of the two governance proxies with forecast properties in thepre– and post–Reg FD eras. However, the association between concen-trated institutional ownership and the probability of forecast occurrence


is less negative in the post–Reg FD era, which is consistent with Reg FDreducing the ability of firms to privately communicate information to selectaudiences.

Our results, linking institutional ownership and outside directors to vol-untary disclosure, are interesting given the current scrutiny of corporategovernance mechanisms and the state of the financial reporting system.Governance mechanisms and the financial reporting system have come un-der siege in the wake of a series of financial scandals including Enron andWorldCom. These scandals have led to a greater focus on the need forstronger governance and more transparent disclosure. Our results suggestthat the two are linked—stronger governance appears to be associated withmore transparent disclosure.

In a recent working paper, Karamanou and Vafeas [2004] also address thelink between corporate governance and disclosure decisions. As in our study,they examine the effects of governance on the properties of managementforecasts. They use a much smaller sample consisting of management fore-casts issued by 275 Fortune 500 firms from 1995 to 1999 but they examinea broader set of governance variables including number of board meetingsand measures of audit committee composition. Their findings relating tothe effect of institutional ownership and outside directors on the propertiesof management forecasts are generally similar to those we report here.4

The rest of the paper is organized as follows. Section 2 develops the mainhypotheses, and section 3 outlines the method we adopt to test our hypothe-ses. The results are provided in section 4. Section 5 details the additionalanalyses and robustness checks, and section 6 summarizes and concludesthe paper.

2. Outside Directors, Institutional Owners,and Earnings Forecast Properties

2.1 OUTSIDE DIRECTORS AND EARNINGS FORECAST PROPERTIES

Several prior studies document the favorable impact of outside directorson firm decisions aimed at enhancing shareholder wealth.5 Consistent with

4 The other governance variables Karamanou and Vafeas [2004] use generally turn out tobe statistically insignificant in their regressions except for audit committee size, which seemsto be negatively associated with the probability of issuing a forecast and the precision of theforecast. They also partition the forecasts into good news and bad news, and find that theeffect of governance is stronger for bad news disclosures and that precision decreases withgovernance only when bad news is conveyed. We, on the other hand, provide simultaneousanalyses of the endogeneity between the characteristics of management forecasts and selectedgovernance variables. We also analyze the effect of concentrated institutions on disclosure anduse this variable as interacting with sample periods pre– and post–Reg FD to examine the effectof FD on disclosure (see our later discussion).

5 Research shows that firms with outsider-dominated boards are more likely to participatein major restructuring events such as mergers, takeovers, and tender offers (Lin [1996]) andare more likely to remove poorly performing CEOs (Weisbach [1988]) and nominate outside


these studies, Dechow, Sloan, and Sweeney [1996] and Beasley [1996] finda negative association between outside directors and likelihood of finan-cial fraud. Similarly, Klein [2002] documents a negative relation betweenoutside directors and earnings management. Finally, Sengupta [2004] docu-ments that firms with more outside directors are more likely to release theirquarterly earnings figures early. These studies suggest that the monitoringrole of outside directors extends to the financial reporting process.

Prior work studying earnings forecasts suggests that managers actingin the best interests of the firm should enhance transparency by issuingmore frequent, specific, and accurate forecasts (Skinner [1994], Kasznikand Lev [1995], Kim and Verrecchia [1991], Baginski, Conrad, andHassell [1993], Williams [1996]).6 However, managers acting in theirown self-interest could decide to disclose less than what is optimal forvarious reasons, including insider trading opportunities and reputationalrisks of erroneous forecasts. Outside directors can mitigate managerialself-interest and influence the issuance and properties of earnings forecastsby directly reviewing the disclosure policy and earnings releases as wellas by fostering an environment that encourages greater transparency.The NIRI [2002], in discussing issues related to earnings guidanceand directors’ role in evaluating the guidance, finds that although theSarbanes-Oxley Act does not expressly require the board to review earningsreleases, several prominent securities lawyers say they advise their clientsto do so. Along similar lines, Corporate Board Member magazine [2004]identifies the evaluation of investor communications including quarterlyteleconferences and press releases as a key role of directors. In addition,a New York Stock Exchange (NYSE) listing requirement is that the auditcommittee of the board discuss with management information that ispresented in press releases, including earnings guidance. As an illustration,one of the key responsibilities and duties of the audit committee of theboard of directors for Verizon is to “review and discuss with manage-ment any proposed public release of earnings or guidance information”(http://investor.verizon.com/corp gov/audit finance committee.html).7

The preceding discussion suggests that a greater proportion of outside

CEOs (Borokhovich, Parrino, and Trapani [1996]). Rosenstein and Wyatt [1990] documentthat shareholder wealth increases with the addition of outsiders to the board, and Cotter,Shivdasani, and Zenner [1997] provide evidence that outside directors enhance shareholderwealth during tender offers.

6 The willingness to enhance transparency, and therefore the optimal disclosure policy, isconstrained by the costs of disclosure (including proprietary and litigation costs).

7 We contacted a few companies and directors to obtain further information and anecdotalevidence on this issue. Our sense is that board meetings occur regularly and earnings releases(including guidance) are discussed. In addition, board members are generally given copies ofearnings releases ahead of time. One director we spoke to informed us that the audit committeediscussed earnings announcements and guidance with the management. If the guidance is off-mark, the board would hold the management accountable.


directors (presumably more independent and effective members) shouldhave a favorable effect on earnings forecast properties.

However, an alternative view is that outside directors may be ineffec-tive, either because they are appointed by, or have allegiance to, companymanagers or because the board culture discourages conflict (Mace [1986],Jensen [1993]).8 The effectiveness of outside directors and the extent towhich they represent shareholder interests could also be influenced by thefear of litigation and reputation costs. For example, directors may not inducemanagers to disclose more precise forecasts for fear of personal litigationand reputation costs. To the extent that the directors’ own incentives affecttheir ability to act on behalf of the shareholders, it would bias our resultsagainst the hypothesis that outside directors would have a favorable effecton the properties of management forecasts. The following hypothesis sum-marizes our expectations.

H1: Firms with a greater percentage of outside directors on their boards(a) are more likely to issue earnings forecasts, (b) issue forecastsmore frequently, (c) are more likely to issue specific (precise) fore-casts, (d) are more likely to issue accurate forecasts, and (e) are lesslikely to issue optimistic forecasts.

2.2 INSTITUTIONAL OWNERSHIP AND EARNINGS FORECAST PROPERTIES

Institutions desire and demand more disclosure. Disclosures, especiallyearnings forecasts, are closely watched by market participants.9 This can befound by listening in on conference calls, where institutions consistentlyprobe the company for more specific, unbiased, and accurate informationabout future earnings. Brokerage houses regularly hold conferences wherefirms make presentations to institutional shareholders about the prospectsof the company. Prior work (e.g., Healy, Hutton, and Palepu [1999], Busheeand Noe [2000]) suggests that institutions prefer to buy stocks in firms thathave sustained disclosure enhancements. It seems reasonable that these in-stitutions would continue to demand further augmentation of disclosurefrom the firms. A key precept of the International Corporate GovernanceNetwork, a group representing the interests of major institutional investors,corporations, and financial intermediaries, is related to communicationsand reporting and states that “corporations should disclose accurate, ade-quate, and timely [italics added] information . . . so as to allow investors tomake informed decisions about the acquisition, ownership obligations andrights and sale of shares” (Conference Board [2001, p. 10]).

8 Consistent with these arguments, Yermack [1996] and Bhagat and Black [1997] fail todocument an association between the proportion of independent outside directors and firmperformance.

9 A sell-side (brokerage house) analyst we spoke to stated that one of the key pieces ofinformation the buy-side (including pension funds and asset management funds) investorsconsistently demand from her and the companies whose stock they own is information aboutnear-term earnings.


In a recent survey of more than 1,300 fund managers and financial an-alysts aimed at identifying the best CFOs, Institutional Investor reports that“companies, and CFOs, are being rewarded for delivering on promises. AtPfizer, Shedlarz’s ability to set firm financial targets and then hit them hasearned praise. Daley of P&G gets high marks for issuing earnings guidanceearly and often” (Osterland [2004, p. 35]). Therefore, though institutionsmight not be able to directly oversee the activities of the manager (giventhat they are outsiders), they could elicit greater transparency by demand-ing more information from the firm. Given this disclosure-oriented focus ofinstitutions and their constant probing of companies for their earnings out-look, we expect to see a positive association with a firm’s propensity to issueforecasts, the specificity and accuracy of forecasts issued, and a decrease inthe managerial optimism bias of forecasts. Our expectations about the im-pact of institutional ownership on the properties of management forecastscan be summarized in the following hypothesis.

H2: Firms with a greater percentage of institutional ownership (a) aremore likely to issue earnings forecasts, (b) issue forecasts morefrequently, (c) are more likely to issue specific (precise) forecasts,(d) are more likely to issue accurate forecasts, and (e) are less likelyto issue optimistic forecasts.

3. Methodology

3.1 SAMPLE SELECTION AND DESCRIPTION

We obtain management earnings forecast data from the Corporate In-vestor Guidelines (CIG) database, maintained by First Call. The CIGdatabase covers the period from mid-1994 to mid-2003 and has earningsas well as nonearnings forecasts made by companies before the official re-lease of reported earnings. It includes point forecasts, range forecasts, open-ended forecasts, and qualitative forecasts (such as “comfortable with analystexpectations”). First Call carries both annual and quarterly forecasts.10

In this study we report results based on annual earnings forecasts only.Separate analyses based on quarterly forecasts were also performed andthese are discussed in section 5.6. Panel A of table 1 provides a summaryof the screens applied to identify the primary management forecast sample

10 We performed a small-sample test of the comprehensiveness of the CIG database. We pickedJune 1997 and June 2000 for our test and compared the 182 and 211 forecasts appearing inthe CIG database with those extracted from a search of the Factiva (formerly known as theDow Jones News Retrieval) database. Keywords “expects earnings,” “expects income,” “expectslosses,” “expects profits,” “expects results,” and three similar lists with first words “forecasts,”“predicts,” and “sees” were used to identify forecasts from Factiva. These keywords are consistentwith those used by Baginski, Hassell, and Kimbrough [2002]. The Factiva search turned up53 and 84 forecasts made in June 1997 and June 2000, respectively, suggesting that the CIGdatabase is a comprehensive source of management forecast information.


T A B L E 1Sample Selection and Description

Panel A: Sample-selection criteriaAll Forecasts Point Forecasts

Initial sample of all annual forecasts 24,406 5,825Less:

Nonearnings forecasts (1,097) (257)Preannouncements (1,544) (501)Multiple forecasts for the same fiscal period (11,664) (1,692)Firms not followed by at least three analysts (4,244) (1,091)Forecasts not for 1997–2002 (1,366) (766)Governance data unavailable (815) (231)Compustat and other financial data unavailable (742) (242)

Usable annual forecastsa 2,934 1,045Number of different companies 1,467 695

Panel B: Distribution of the frequency of earnings forecasts made by a firm during 1997–20021 2 3 4 5 or More Total

Number of firmsb 267 183 142 120 541 1,253

Panel C: Distribution of the specificity of earnings forecasts made by firms during 1997–2002Point Range Open Ended Other Total

Number of forecastsc 873 1,272 551 68 2,764aFor tests of occurrence, the forecasting samples were matched with all nonforecasting firms for which

requisite data were available to compute the independent variables (4,811 firm-year observations), resultingin the combined sample of 7,745 observations.

bTests of forecast frequency could be performed based on a potential sample of 1,467 firms (frompanel A, column 2). However, because forecast frequency from 1997 to 2002 was regressed on averages ofthe independent variables over the sample period, firms that did not have at least three years of data weredropped, resulting in the reduced sample of 1,253 observations.

cTests of forecast specificity could be performed based on a potential sample of 2,934 observations(forecasting sample in panel A). However, some observations were deleted because analyst followinginformation just before the forecast was not available, resulting in a sample of 2,764 observations. Samplesize information is provided at the bottom of each table reporting the regression results.

used in our tests. Initially, we selected all annual earnings forecasts made be-fore the fiscal period-end (excluding pre-announcements) for 1997–2002.11

Year 2002 is the last complete year for which explanatory variable data wereavailable. We ignored forecasts made before 1997 for two reasons. First, thenumber of usable forecasts is substantially lower for years before 1997. It isnot clear whether this is solely because of the fewer forecasts made duringthis period or whether the data collection in the earlier years was less com-prehensive. Second, for tests of the probability of forecast occurrence, wematch the management forecast sample with all other (nonforecast) firmsby fiscal period for which the requisite data could be obtained. Althoughthe sample was restricted to six years in an effort to reduce problems ofinterdependence of observations that arise from pooling financial data forthe same firm over multiple years, the duration accommodated the pre– and

11 We define pre-announcements as management forecasts issued after the fiscal period endbut before the actual earnings announcements.


post–Reg FD periods. To reduce further problems of data interdependence,if a company made multiple forecasts during a fiscal period, we retained onlythe latest forecast.12

The sample of management earnings forecasts was then matched withFirst Call data on financial analyst following. Companies that did not havevalid ticker symbols or were not followed by at least three analysts weredropped.13 The observations that satisfied the preceding criteria were thenmatched with the specified governance data (institutional ownership andoutside directors) collected from Compact Disclosure. This database pro-vides information on stock ownership collected from Spectrum and infor-mation on officers and board of directors collected from proxy statements.The June CD-ROM for each of the years 1997 to 2002 was examined to obtaininstitutional ownership and outside directors’ information. The ownershipdata in these CD-ROMs represent holdings as of March 1 of each year. Ev-ery forecast observation was matched with the institutional ownership asof the period immediately preceding March. Thus, a forecast made in be-tween March and December 2000 is matched with institutional ownershipas of March 2000, whereas forecasts made in January or February 2000 werematched with institutional ownership as of March 1999. Data on officersand boards of directors were based on the latest available proxy statementincluded in the June CD-ROMs. Finally, some observations were lost becauseof lack of data for control variables (obtained mainly from Compustat). Thefinal sample comprises 2,934 annual management earnings forecasts (1,045point forecasts).14 The control group of companies initially consisted of allfirms for which First Call analyst forecast information was available for 1997to 2002 and for which the company did not make a management earningsforecast. We deleted all firms with less than three analysts following the firm,missing Compustat data, and missing institutional ownership and outsidedirectors’ data, resulting in a final control sample of 4,811 observations. Weavoid problems relating to outliers by winsorizing the variables at the 1%and 99% levels.

12 Analyses carried out after retaining the earliest of multiple forecasts yielded similar results.13 The analysts screen was necessary because one of the key control variables is the standard

deviation of analysts’ forecasts. Analyses carried out without applying this screen yielded similarresults (we replaced the standard deviation of analysts’ forecasts with the standard deviation ofstock returns).

14 The actual number of observations used in the regressions varies. For tests of occurrence,the management forecast sample was matched with all other firms for which First Call, gover-nance, and other financial data were available, resulting in 7,745 observations (2,934 forecastingand 4,811 nonforecasting). For tests of frequency, the number of forecasts made by a companyduring 1997–2002 was matched with averages of all control variables calculated from 1997 to2002. If a company did not have at least three years’ of data it was dropped, resulting in asample of 1,253 observations (firms). Tests of forecast specificity were based on a sample of2,764 forecasts (some of the original forecast sample firms were missing analyst following infor-mation before the earnings forecast). For tests of forecast error and bias, only point forecastswere used, resulting in a sample of 1,045 observations (for further clarification, see footnotesto table1).


Panel B of table 1 shows the forecast frequency for the sample of 1,253firms making annual forecasts from 1997 to 2002. The table reveals that267 companies made only one forecast during the six-year period, whereas541 firms made five or more earnings forecasts over the period. Panel C oftable 1 provides data on the specificity of the forecasts. The table shows thata large proportion of the forecasts are point and range forecasts.

3.2 RESEARCH DESIGN

3.2.1. Measures of Forecast Properties. To examine the association of insti-tutional ownership and outside directors to voluntary disclosure, we focuson several aspects of management forecasts. Our primary measure is theprobability of occurrence of forecasts, defined as:

OCCUR = 1 if the firm issued an earnings forecast during the fiscalperiod, and 0 otherwise

For tests of forecast occurrence, a firm that issues multiple forecasts andone that issues just a single forecast in the period are treated the same. Simi-larly, a firm might issue just one forecast in our sample period whereas othersmight be more consistent in their disclosure policy. Sporadic occurrence offorecasts could be attributed to managerial opportunism, as opposed to aconsistent disclosure policy induced by governance. If effective governanceis inducing disclosure, we should find an association between the numberof forecasts that a firm issued (in our sample period) and the governancevariables. This would lend additional support to the results from the occur-rence specification. To test this we define the frequency, or total incidenceof forecasts, as:

FREQ = total number of forecasts issued by a firm in our sample period(1997–2002)

To evaluate the effect of governance variables on the quality of earningsforecasts issued by management we focus on specificity, accuracy, and opti-mism (bias) of the forecasts:

SPECIFIC = 3 if the firm issued a point forecast during a fiscal period, 2if an interval forecast, 1 if an open-ended forecast, and 0 ifa qualitative forecast

ERROR = absolute value [(management forecast of earnings per share(EPS) − actual EPS)/price at the beginning of the fiscalperiod]. Accuracy of forecasts is the inverse of ERROR .15

15 The actual earnings numbers, as well as the forecasts, are both derived from First Callto ensure consistency across the two numbers. The actual earnings number represents theactual per share numbers reported by the companies following a fiscal period-end. Accordingto First Call, the actual values have been adjusted to exclude any unusual items that a majorityof the contributing analysts deem nonoperating or nonrecurring. Similarly, the majority ofanalysts’ estimates on First Call are real time and come from broker notes or through electronictransmission and are adjusted to exclude any unusual items that a majority of the contributing


BIAS = [(management forecast of EPS − actual EPS)/price at thebeginning of the fiscal period]. If BIAS > 0, the earningsforecast is optimistically biased.

3.2.2. Measures of Corporate Governance. To determine the association be-tween the voluntary information disclosure environment and corporate gov-ernance, we specify two widely used measures of corporate governance—theproportion of the board consisting of outsiders and the proportion of ag-gregate institutional ownership:

OUTDIR = percentage of the board of directors that are not also officersof the firm16

INST = percentage of the company’s aggregate common stock heldby institutions

3.2.3. Control Variables. Based on prior research, we selected several addi-tional independent variables to control for other possible determinants ofthe properties of management forecasts:

LMVAL = log of the market value of a firm’s common equity at thebeginning of the fiscal period. The prior literature providesevidence supporting the positive association between firmsize and management earnings forecasts (e.g., Kasznik andLev [1995]).

AUDIT = 1 if the company is audited by one of the Big 5 auditors,and 0 otherwise. Auditor reputation could also be a factorin disclosure decisions. Thus, prior research indicates thatfirms using Big 5 auditors tend to have better disclosure(Lang and Lundholm [1993]).

NUMEST = number of analysts following the firm. Prior research (Langand Lundholm [1993, 1996]) documents a positive associa-tion between corporate disclosure quality and the numberof analysts following a firm.

LITIGATE = 1 for all firms in the biotechnology (2833–2836 and 8731–8734), computers (3570–3577 and 7370–7374), electron-ics (3600–3674), and retail (5200–5961) industries, and

analysts deem nonoperating or nonrecurring. We called First Call to discuss this issue andwere told that in the event of an outlier analyst forecast or perceived nonconformity of ananalyst with the construct of the majority of analysts, an editor contacts the outlier analyst todetermine whether it is just a difference of opinion or difference in construct, and the editortakes corrective action accordingly. To the extent that management guidance is intended toinfluence analysts’ forecasts and market expectations, it would seem reasonable that managersare forecasting the same construct as analysts (and First Call actuals). However, it is possible insome cases that the construct that managers are forecasting and that is captured on First Callis different from the analysts’ forecasts and actual earnings construct. Unless this differenceis systematically correlated with our explanatory variables of interest (INST and OUTDIR), itshould not bias our results.

16 We also use the number of outside directors as an alternative proxy for this variable. Thisyielded results that are similar to those reported with OUTDIR .


0 otherwise (based on Francis, Philbrick, and Schipper[1994]). Furthermore, Jones and Weingram [1996] findthat market capitalization and equity beta are both deter-minants of litigation risk. We use both these measures ascontrols in our analyses.

MKBK = ratio of market value to book value of common equity at thebeginning of the fiscal period. We use MKBK as a proxy forproprietary costs (Bamber and Cheon [1998]). In keepingwith Bamber and Cheon [1998], we also use sales concen-tration as an alternative proxy to measure proprietary costs.The results based on this second proxy (not reported) weresimilar to those using MKBK .

LOSS = 1 if the firm reported losses in the current period, and0 otherwise. Prior research suggests that earnings are lessvalue relevant for loss-making firms (Hayn [1995]) and thatmeeting or beating financial analyst expectations is less im-portant for these firms (Degeorge, Patel, and Zeckhauser[1999]). Brown [2001] documents substantial differencesbetween the analyst forecast errors of loss and profit firms.Analysts have greater problems forecasting earnings forloss firms. It is therefore likely that management’s abilityto forecast earnings would be similarly circumscribed forfirms making losses.

HORIZON = number of days between the forecast date and the fiscalperiod-end date. Prior work uses this measure to proxy forgreater earnings uncertainty and the unobservable preci-sion of managers’ beliefs (Baginski and Hassell [1997]).

SURPRISE = absolute value [(management forecast of EPS − mediananalyst forecast of EPS)/price at the beginning of the fiscalperiod]

DISPFOR = standard deviation of analysts’ forecasts divided by the me-dian forecast. This variable captures the interanalyst uncer-tainty in the earnings prospects of a firm (Ajinkya and Gift[1984], Brown, Foster, and Noreen [1985], Swaminathan[1991]). Analogously, management would also likely findit more difficult to forecast earnings when the value ofthis variable is higher and could face greater litigationexposure.

NEWS = 1 if the current-period EPS is greater than or equal to theprevious-period EPS, and 0 otherwise. Baginski, Hassell,and Kimbrough [2002] find that the sign of random-walkdifferences in earnings is negatively associated with fore-cast occurrence. Bhojraj [2002] suggests that a reason forthis negative association is that management is likely to is-sue a forecast in this case (i.e., if NEWS = 0) to preventunfavorable litigation.


EARNVOL = standard deviation of quarterly earnings over 12 quartersending in the current fiscal year, divided by median assetvalue for the period. Waymire [1985] finds an associationbetween a firm’s earnings volatility and the frequency ofmanagement earnings forecasts. Kross, Lewellen, and Ro[1994] use a similar measure called stability, defined as thestandard deviation of return on equity.

BETA = equity beta for the fiscal period. This variable representsthe proxy for market risk (Bushee and Noe [2000]).

FD = 1 if the observation is related to the post–Reg FD pe-riod (after October 2000), and 0 otherwise. Heflin, Sub-ramanyam, and Zhang [2003] and Bailey et al. [2003] findthat the number of forecast issuances has increased afterReg FD.

The variables DISPFOR , EARNVOL, and BETA (and to a limited extentLOSS) capture different and possibly overlapping dimensions of uncer-tainty; hence, the respective coefficients of these variables may not all bestatistically significant. The appendix provides a complete listing of all vari-able definitions.

3.2.4. Regression Specifications. The specifications of the various regres-sions are as follows:

OCCUR = α0 + α1OUTDIR + α2INST + α3LMVAL + α4AUDIT

+ α5NUMEST + α6DISPFOR + α7LITIGATE + α8MKBK

+ α9LOSS + α10NEWS + α11EARNVOL + α12BETA + α13FD

(1)

FREQ = α0 + α1OUTDIR + α2INST + α3LMVAL + α4AUDIT


+ α9LOSS + α10NEWS + α11EARNVOL + α12BETA (2)

SPECIFIC = α0 + α1OUTDIR + α2INST + α3LMVAL + α4AUDIT


+ α9LOSS + α10NEWS + α11EARNVOL

+ α12BETA + α13FD +14 HORIZON (3)

ERROR = α0 + α1OUTDIR + α2INST + α3LMVAL + α4AUDIT


+ α9LOSS + α11EARNVOL + α12BETA + α15SURPRISE (4)


BIAS = α0 + α1OUTDIR + α2INST + α3LMVAL + α4AUDIT + α5NUMEST

+ α6DISPFOR + α7LITIGATE + α8MKBK + α9LOSS

+ α11EARNVOL + α12BETA + α15SURPRISE (5)

Because the dependent variable, OCCUR , is a binary variable, we estimateequation (1) with a probit model. For the dependent variable, SPECIFICITY ,which has four ordinal values, we use an ordered probit model on equation(3). We use an ordinary least squares (OLS) model to estimate equations(2), (4), and (5). For tests of occurrence, NUMEST and DISPFOR werecalculated based on the last analyst forecast data available before the fiscalyear-end. For the other four dependent variables, NUMEST and DISPFORwere calculated based on the latest data available before the managementforecast date.

4. Results

Summary descriptive statistics for selected variables are provided in pan-els A and B of table 2. Panel A provides summary statistics for variables basedon 2,934 annual forecast observations. Summary statistics for ERROR , BIAS,HORIZON , and SURPRISE are based on the sample of 1,045 observationsused for tests of forecast accuracy and bias. Panel B of table 2 also pro-vides summary statistics of the key variables for the control (nonforecasting)group. Forecasting firms tend to be larger, with a median market value ofequity of about $1.43 billion compared with approximately $805 million forthe nonforecasting sample. Median institutional ownership is about 64% forthe forecast sample and about 54% for the nonforecast sample. The mediannumber of analysts following the firm is eight for the forecast sample andsix for the nonforecasters.

Table 3 presents results of our basic analysis examining the link betweenour governance measures and forecast occurrence, frequency, and speci-ficity. Column 3 shows that the probability of occurrence of a managementearnings forecast is, as expected, positively associated with the two gover-nance variables, OUTDIR and INST , and the respective coefficients are sta-tistically significant at the .01 level. Several of the control variables in thisregression are in the expected direction and significant (including LMVAL,NUMEST , LOSS, and BETA). Consistent with Miller and Piotroski [2000],we find that LITIGATE has a positive and significant effect on forecast oc-currence. The coefficient on FD is positive and highly significant, which isconsistent with prior work (Heflin, Subramanyam, and Zhang [2003], Baileyet al. [2003]) that shows an increase in the level of forecast activity after theintroduction of Reg FD. Heflin, Subramanyam, and Zhang [2003] arguethat previously private information releases might be substituted for publicdisclosures after Reg FD.

Column 4 of table 3 provides regression results of tests of the effect of ourgovernance measures on the frequency of earnings forecasts. The results


T A B L E 2Descriptive Statistics

Variables Mean Std. Dev. Median 25% 75%

Panel A: Forecasting groupERRORa 0.022 0.067 0.003 0.001 0.017BIASa 0.018 0.068 0.000 −0.001 0.015HORIZON a 173.881 112.364 161.000 70.000 263.000SURPRISEa 0.007 0.017 0.001 0.000 0.004INST 60.977 21.859 64.100 47.400 77.200INST5 24.534 10.354 23.861 17.422 30.943INSTCONS 0.023 0.032 0.018 0.010 0.029OUTDIR 71.633 19.803 76.923 66.667 84.615MVALb 8.612 28.095 1.425 0.534 5.020AUDITOR 0.759 0.428 1.000 1.000 1.000NUMEST 9.685 6.097 8.000 5.000 13.000DISPFOR 0.004 0.069 0.001 0.000 0.002LITIGATE 0.307 0.461 0.000 0.000 1.000MKBK 4.202 5.108 2.714 1.714 4.686LOSS 0.123 0.329 0.000 0.000 0.000NEWS 0.478 0.500 0.000 0.000 1.000EVARNVOL 0.034 0.084 0.014 0.008 0.030BETA 1.047 0.699 0.913 0.595 1.342DE 0.490 0.382 0.451 0.103 0.914YIELD 0.010 0.015 0.001 0.000 0.015LIQUID 0.148 0.843 0.098 −0.347 0.660

Panel B: Control groupc

MVALb 4.291 14.788 0.805 0.303 2.645INST 50.413 25.837 53.580 31.510 70.750OUTDIR 68.535 21.690 73.684 60.000 83.333MKBK 3.871 5.219 2.400 1.564 4.083NUMEST 7.568 5.005 6.000 4.000 10.000

Variables are defined in the appendix.aSummary statistics for ERROR , BIAS, HORIZON , and SURPRISE are based on the sample of 1,045

point forecasts; summary statistics for the other variables are based on the sample of 2,934 annual earningsforecasts.

bMVAL represents market value of common equity in $ billions. In regressions, log of market value ofcommon equity (in $ millions) is used.

cThe summary statistics for the control sample were calculated based on the sample of 4,811 observationsthat were matched with the annual forecast data for tests of occurrence.

indicate that the coefficient on OUTDIR is positive and significant at the.05 level. The coefficient on INST is also positive and significant (one sidedp-value < .05). Thus, it appears that firms with effective governance mecha-nisms in place are likely to have more frequent earnings forecast disclosures.Fewer of the control variables are significant in explaining FREQ comparedwith OCCUR . NUMEST and LITIGATE cease to be significant in explain-ing forecast frequency. However, the results suggest that firms with greatervolatility (proxied by EARNVOL) are likely to issue forecasts less frequently.

Next, we evaluate another important property of the dependent variable,that is, how specific (or precise) the disclosure is provided that disclosure(management forecast) occurs. Column 5 of table 3 summarizes the results


T A B L E 3Regression Results of the Effects of Outside Directors and Institutional Ownership on the Probability

of Occurrence of Annual Earnings Forecasts, Frequency, and Specificity

Forecast Occurrence Forecast Frequency Forecast SpecificityPredicted Sign (OCCUR) (FREQ) (SPECIFIC)

Intercept ? −1.5804 −2.7708 2.0473(−15.226)∗∗∗ (−2.684)∗∗∗ (12.899)∗∗∗

OUTDIR + 0.0030 0.0164 −0.0006(4.048)∗∗∗ (1.680)∗∗ (−0.572)

INST + 0.0085 0.0130 0.0039(12.937)∗∗∗ (1.963)∗∗ (4.022)∗∗∗

LMVAL + 0.0466 0.9197 −0.0329(3.642)∗∗∗ (6.458)∗∗∗ (−1.829)

AUDIT + 0.0328 0.2610 −0.1111(0.930) (0.851) (−2.207)

NUMEST + 0.0203 0.0493 0.0066(5.641)∗∗∗ (1.068) (1.259)

DISPFOR − −0.0335 −0.1592 −0.2106(−0.201) (−0.737) (−0.553)

LITIGATE ? 0.1906 −0.0605 0.0605(4.948)∗∗∗ (−0.190) (1.183)

MKBK − 0.0050 0.0128 0.0043(1.506) (0.346) (1.025)

LOSS − −0.4195 −1.2942 −0.1994(−9.387)∗∗∗ (−2.814)∗∗∗ (−3.052)∗∗∗

NEWS − 0.0213 1.8266 0.0172(0.687) (3.037) (0.395)

EARNVOL − (0.0920) −2.0710 0.0173(0.878) (−2.115)∗∗ (0.872)

BETA − −0.1505 −1.1278 −0.0449(−5.829)∗∗∗ (−4.321)∗∗∗ (−1.369)∗

HORIZON − 0.0004(2.538)

FD + 0.5887 −0.1251(17.883)∗∗∗ (−2.674)∗∗∗

Log likelihood −4,607.12 −3,109.79Adjusted R2 0.20No. of observations 7,745 1,253 2,764

Variables are defined in the appendix. White’s [1980] heteroskedasticity-adjusted t-values are providedin parentheses below each coefficient.

∗,∗∗, and ∗∗∗ indicate significance at the 10%, 5%, and 1% levels, respectively (one-tailed test, except forthe intercept and LITIGATE).

of the ordered probit regression. INST is positively associated with the speci-ficity of earnings forecasts (significant at the .01 level). Outside directors,however, do not seem to influence the specificity of forecasts. The coeffi-cient is negative, although not statistically significant. This finding could beattributable to the personal incentives (discussed earlier), including fear oflitigation, constraining the outside directors’ willingness to facilitate morespecific disclosure. Although outside directors might be willing to facilitateforecast issuance, they might leave the specificity of the forecast to the dis-cretion of the manager. Among the control variables, the result relating to


LOSS is particularly interesting. The evidence from the last three columnssuggests that a manager is less likely to have issued a forecast when the firmreports a loss.

Another provocative finding is that although forecast occurrence has in-creased after Reg FD (column 3), specificity is reduced (column 5). Thusthe coefficient for FD is negative and significant at the .01 level in column 5.Although this finding may be explained in various ways, we posit the fol-lowing hypothesis. When the forecast environment changed after Reg FD(and the financial analyst channel was shut off), firms for which the cost-benefit ratio of disclosure was marginally unfavorable before Reg FD mightbe willing to forecast earnings publicly for the first time provided they coulddo so with less specificity and thereby reduce the costs of disclosure. Thismay suggest why after Reg FD, the specificity would be reduced even thoughoccurrence has increased.17

Results of tests of the association of corporate governance with the accu-racy of management earnings forecasts are presented in table 4 (dependentvariable used is ERROR , inverse of accuracy). Outside directors (OUTDIR)are, as expected, positively (negatively) associated with forecast accuracy(ERROR), with the coefficient statistically significant at the .01 level. Thecoefficient on INST is also positively related to accuracy and statistically sig-nificant at the .01 level. This finding is in accordance with institutions consis-tently probing firms for more accurate information. Among the control vari-ables, HORIZON , DISPFOR , EARNVOL, and SURPRISE are influential whenthe dependent variable is management forecast ERROR . A longer HORIZONmeasure (i.e., earlier management forecast relative to fiscal year-end date)suggests a greater forecast error. DISPFOR , which measures interanalyst earn-ings forecast uncertainty, is positively associated with management forecastERROR .

Table 4 also summarizes the regression results of the association of the gov-ernance variables with the ex post bias in management earnings forecasts. Apositive value of BIAS suggests that managers are optimistic in their forecasts.The table shows that the coefficients for OUTDIR and INST are negative, asexpected, and significant at conventional levels. This is consistent with theview that a greater proportion of outside directors and larger institutionalownership are associated with more conservative (as opposed to optimistic)forecasts. The coefficients of the control variables HORIZON , EARNVOL,and SURPRISE are statistically significant in expected directions.18

17 We conducted additional analysis to check whether our conjecture is valid. We tabulatedspecificity for two groups of firms: (1) those that forecast only in the post–Reg FD period anddid not forecast in the pre–Reg FD period, and (2) those that disclosed both in the pre– andpost–Reg FD period. The first group had lower specificity, consistent with our argument. Thedifference in specificity between the two groups is statistically significant at p-value < .05.

18 We also carried out our analysis using a dichotomous variable OPTIM , where OPTIM =1 if the forecast was optimistically biased, and 0 otherwise. The results are similar to the BIASfindings. Also, FD is omitted in the ERROR and BIAS regressions. Heflin, Subramanyam, and


T A B L E 4Regression Results of the Effect of Outside Directors and Institutional Ownership on the Error and Bias

in Management’s Annual Earnings Forecasts

Forecast Accuracy Forecast BiasPredicted Sign (Dependent Var. = ERROR) (Dependent Var. = BIAS)

Intercept ? 0.0340 0.0299(2.085)∗∗ (1.798)∗

OUTDIR − −0.0002 −0.0002(−2.331)∗∗∗ (−1.835)∗∗

INST − −0.0003 −0.0003(−2.348)∗∗∗ (−2.183)∗∗∗

HORIZON + 0.0001 0.0001(6.006)∗∗∗ (5.286)∗∗∗

LMVAL − −0.0014 −0.0018(−1.109) (−1.352)∗

AUDIT − −0.0013 0.0028(−0.298) (0.600)

NUMEST − 0.0000 0.0000(−0.116) (−0.052)

DISPFOR + 1.3500 0.6284(2.588)∗∗∗ (1.132)

LITIGATE ? −0.0006 0.0022(−0.125) (0.447)

MKBK + −0.0006 −0.0005(−2.089) (−1.649)

LOSS + 0.0059 0.0032(0.640) (0.338)

EARNVOL + 0.1564 0.1351(2.086)∗∗ (1.834)∗∗

BETA + 0.0021 0.0011(0.658) (0.321)

SURPRISE + 0.9010 0.9882(4.093)∗∗∗ (4.426)∗∗∗

Adjusted R2 0.16 0.13No. of observations 1,045 1,045



The results in tables 3 and 4 are consistent with the argument that outsidedirectors foster an environment of greater disclosure transparency. Outsidedirectors are associated with a greater likelihood of earnings forecasts andgreater frequency of earnings forecasts as well as more accurate and con-servative forecasts. Institutional ownership is similarly associated with dis-closure quality. Firms with high institutional ownership are not only morelikely to issue a forecast but tend to forecast more frequently, and the fore-casts are more specific, accurate, and conservatively biased. Although we

Zhang [2003] find that, for analysts earnings forecasts, there is no change in accuracy or biasafter Reg FD. We had initially included FD, but because of insignificant results, we decided toexclude this variable in table 4.


view the improvements in disclosure as arising primarily from the demandpressure generated by institutional investors, to the extent the disclosuresare in the interests of all shareholders, we see an indirect governance rolefrom institutional investors.

5. Additional Analyses and Robustness Checks

5.1 ENDOGENEITY BETWEEN INSTITUTIONAL OWNERSHIPAND DISCLOSURE

The positive association between institutional ownership and disclosurecan be explained in two ways. Healy, Hutton, and Palepu [1999] and Busheeand Noe [2000] suggest that institutions prefer to buy stock in firms thathave superior disclosure. However, institutions also have incentives to en-courage greater disclosures from companies in which they choose to invest.This suggests that the link between institutional ownership and disclosure islikely to be endogenous. Although ownership decisions could be influencedby a firm’s disclosure policy, it is also reasonable that a firm’s disclosure pol-icy is influenced by its institutional ownership. Thus, disclosure could leadto future institutional ownership, which in turn could lead to future disclo-sure, and so on. We examine this endogenous link between our governancevariables and the propensity to disclose by adopting (1) a Granger-type lead-lag approach and (2) a simultaneous equation analysis. The specification ofthe lead-lag regression is as follows:

OCCUR = α0 + α1OCCURL + α2OUTDIR + α3INST + α4LMVAL

+ α5AUDIT + α6NUMEST + α7DISPFOR + α8LITIGATE

+ α9MKBK + α10LOSS + α11NEWS + α12EARNVOL

+ α13BETA + α14FD, (6)

where:

OCCURL = OCCUR lagged one period.

In this specification, INST lags the dependent variable OCCUR . OCCURLin turn lags INST , such that the time sequence is OCCURL → INST →OCCUR . The purpose of this specification is to isolate the incremental ex-planatory power of INST on future disclosure after controlling for the po-tential effect of prior disclosure on INST .19

We also estimate a simultaneous equations system of the form:

OCCUR = α0 + α1OUTDIR + α2INST + α3LMVAL + α4AUDIT


+ α9LOSS + α10NEWS + α11EARNVOL + α12BETA + α13FD, (7a)

19 See Hamilton [1994, pp. 304–05] for a description of econometric tests for Grangercausality.


INST = α0 + α1OCCUR + α2LMVAL + α3AUDIT + α4NUMEST

+ α5LITIGATE + α6MKBK + α7LOSS + α8NEWS + α9BETA

+ α10DE + α11YIELD + α12LIQUIDITY + α13SNP . (7b)

Control variables for equation (7b) are drawn from prior work, includ-ing O’Brien and Bhushan [1990], Bushee and Noe [2000], and Bushee[2001]. Control variables not defined earlier and used in equation (7b) areas follows:

DE = ratio of long-term debt to stockholders equity. This vari-able controls for the possibly negative relationship be-tween institutional ownership and leverage.

YIELD = dividend yield for the fiscal period. This variable capturesthe effect of performance based on which institutionsmight make ownership decisions.

LIQUIDITY = log(trading volume/shares outstanding). This controlsfor an institution’s preference for more liquid stocks.

SNP = 1 if the company is part of the S&P 500, and 0 otherwise.This variable captures preference for S&P 500 stocks.

If institutional ownership induces disclosure, the coefficient for INST inequation (7a) should be positive. The estimation of the system of equationswas performed by first regressing each endogenous variable on all exoge-nous variables (instruments). In the second stage, equations (7a) and (7b)were separately estimated with the right-side endogenous variable replacedby its fitted value from the first-stage regression.20 In the preceding speci-fications, the disclosure variable equals 1 if the firm issued a forecast in agiven period, and 0 otherwise. Our approach is analogous to two-stage leastsquares, but not identical, because one of the equations requires a probitanalysis.

Column 3 of table 5 provides results of governance effects after control-ling for past forecast occurrence. The coefficient of OCCURL is positive andstatistically significant at the .01 level, suggesting that past disclosure is agood indicator of future disclosure. However, as we posited, INST contin-ues to provide significant explanatory power, suggesting that institutionalownership is associated with future disclosure after controlling for the corre-lation between institutional ownership and past disclosure. The Granger testcomparing the restricted and unrestricted sum of squares residuals rejectedthe null hypothesis of INST = 0 at p-value < .001.

Columns 4 and 5 of table 5 provide results of the simultaneous regressionanalysis. Consistent with prior work, we find that institutional ownership isinfluenced by disclosure practices (column 5, equation (7b)). More impor-tant, and consistent with our expectations of an endogenous relationship,

20 See Maddala [1983, p. 244] for a discussion of this issue. Because OCCUR is a binaryvariable, (7a) was estimated using probit both in the first and second stages.


T A B L E 5Granger Causality and Simultaneous Determination of Forecast Occurrence and Institutional Ownership

Two-Stage Least SquaresEstimation with INST Endogenous

Predicted Occurrence RegressionSign with Lagged Occurrence (OCCUR) (INST)

Intercept ? −1.452 −2.176 50.5859(−13.436)∗∗∗ (−18.714)∗∗∗ (29.397)∗∗∗

OCCUR + 3.4573(3.876)∗∗∗

OCCURL + 1.193(30.939)∗∗∗

OUTDIR + 0.002 0.002(2.821)∗∗∗ (3.218)∗∗∗

INST + 0.006 0.022(9.115)∗∗∗ (15.810)∗∗∗

LMVAL + 0.017 0.056 1.9234(1.271) (4.289)∗∗∗ (6.681)∗∗∗

AUDIT + 0.033 0.009 2.1461(0.890) (0.249) (3.929)∗∗∗

NUMEST + 0.015 0.007 0.0662(3.774)∗∗∗ (1.966)∗∗ (1.084)

DISPFOR − −0.152 0.314(−0.630) (1.957)

LITIGATE ? 0.188 0.174 −3.2361(4.730)∗∗∗ (4.495)∗∗∗ (−5.048)∗∗∗

MKBK − 0.007 0.008 −0.3983(1.979) (2.275) (−7.770)∗∗∗

LOSS − −0.389 −0.324 −4.9714(−8.261)∗∗∗ (−7.008)∗∗∗ (−6.679)∗∗∗

NEWS − 0.005 0.008 1.8963(0.160) (0.248) (4.046)

EARNVOL − 0.157 0.382(0.828) (2.027)

BETA − −0.116 −0.181 −6.9689(−4.263)∗∗∗ (−6.857)∗∗∗ (−10.914)∗∗∗

FD + 0.468 0.515(13.279)∗∗∗ (15.296)∗∗∗

DE − 2.3118(3.029)

YIELD − −119.5646(−2.741)∗∗∗

LIQUIDITY + 12.8980(30.198)∗∗∗

SNP + 1.2141(1.590)∗

Log likelihood −5,090.60 −4,561.73Adjusted R2 0.32

Regressions are based on a sample of 7,745 observations. Variables are defined in the appendix. White’s[1980] heteroskedasticity-adjusted t-values are provided in parentheses below each coefficient.



we find that institutional ownership continues to be associated with occur-rence of forecasts (column 4, equation (7a)) and is statistically significantat the .01 level in this simultaneous equations setting. Outside directorshipalso continues to be a significant factor in explaining forecast occurrence.Among the control variables, LMVAL, NUMEST , LITIGATE , LOSS, BETA,and FD are consistently effective in explaining forecast occurrence.

5.2 CHANGES SPECIFICATION

To examine the robustness of our results, we perform additional analysisexamining the association between changes in institutional ownership andoutside directors and changes in the number of earnings forecasts issued bya company over a fiscal year. The specification is as follows:

�FREQ = α0 + α1�OUTDIR + α2�INST + α3�LMVAL + α4�AUDIT

+ α5�NUMEST + α6�DISPFOR + α7�LITIGATE

+ α8�MKBK + α9�LOSS + α10�NEWS

+ α11�EARNVOL + α12�BETA. (8)

We include both annual and quarterly forecasts for the analysis to increasethe power of our tests for the changes specification. All other variables areas defined earlier, and the changes are measured annually. The change inthe number of forecasts issued (FREQ) is measured for the period after thechange in institutional ownership and outside directors. The results of thisanalysis are provided in table 6. The coefficient on the change in outsidedirectors is positive and statistically significant at the .10 level, which indi-cates that firms that increase their outside directorships have a subsequentincrease in disclosure. The coefficient on the change in institutional own-ership is also positive and significant (p-value < .01). Among the controlvariables, changes in AUDIT and NUMEST seem to explain the change inforecast frequency.21

5.3 CONCENTRATED INSTITUTIONAL OWNERSHIPAND THE EFFECT OF REG FD

The analysis thus far is based on the argument that institutional owners,on average (or in the aggregate), act as outsiders relative to management.However, prior work finds that under certain circumstances institutions be-have like insiders. Specifically, these studies find that when ownership in afirm is concentrated in the hands of a few institutions, these institutions arelikely to have an undue influence over management, whereby they secureself-serving benefits that are detrimental to other capital providers (other

21 Table 6 considers all observations of changes. We performed an alternate regression wherewe eliminated observations with no change in the dependent variable, and the results still held(improved marginally).


T A B L E 6Regression Results of the Effects of Changes in Outside Directors and Institutional Ownership

on Changes in the Number of Managements’ Earnings Forecasts

Change in the Number ofPredicted Sign Forecasts (All Changes)

Intercept ? 0.0815(7.654)∗∗∗

�OUTDIR + 0.0007(1.433)∗

�INST + 0.0017(3.714)∗∗∗

�LMVAL + 0.1853(8.510)∗∗∗

�AUDIT + 0.1431(2.905)∗∗∗

�NUMEST + 0.0399(9.422)∗∗

�DISPFOR − −0.0405(−0.561)

�LITIGATE ? −0.1372(−1.242)

�MKBK − −0.0013(−2.109)∗∗

�LOSS − −0.0060(−0.199)

�NEWS − 0.0108(0.714)

�EARNVOL + −0.0085(−0.041)

�BETA − 0.0950(3.237)

No. of observations 6,571Adjusted R2 0.04

The dependent variable represents the change in the number of earnings forecasts made by the firmduring a fiscal year (change is calculated as the difference over two consecutive fiscal periods). All othervariables are as defined in the appendix except that each variable represents changes over two consecutivefiscal years. White’s [1980] heteroskedasticity-adjusted t-values are provided in parentheses below eachcoefficient.


shareholders and bondholders).22 These concentrated (or blockholder)institutions often have better access to private information (Porter [1992])and consequently may not press the firms for public disclosures. Some mayactively prefer fewer forecasts or forecasts of lower quality, thereby givingthem an advantage relative to the market. Effectively, concentrated owner-ship can be seen as analogous to insiders. In such a case, voluntary public

22 This is called the private benefits hypothesis. Consistent with this argument, Bhojraj andSengupta [2003] find that bond yields (ratings) are negatively (positively) associated with in-stitutional ownership but positively (negatively) associated with ownership concentration. SeeBarclay and Holderness [1992] for a discussion of the private benefits and the shared bene-fits hypotheses. Other studies examining the benefits of large blockholders include Huddart[1993] and Maug [1998].


disclosure may suffer and thus would lessen the quality of management fore-casts. We choose two alternate proxies for concentrated ownership vis-a-visinstitutional incentives:23

INST5 = percentage of company’s common stock held by the fivelargest institutional owners of the firm

INSTCONS = Herfindahl index of institutional ownership concentra-tion, measured as:

N∑i=1

(shares held by institution i

total shares outstanding

)2

.

The argument that concentrated institutions may discourage frequent dis-closures rests on the assumption that these institutions have access to privateinformation from the firms. In October 2000, Reg FD was introduced, whichprohibited firms from privately disclosing information to select audiences.The regulation was intended to level the playing field among individuals,analysts, and institutional investors through enhanced and simultaneouspublic disclosure of information. If Reg FD indeed increased the cost of pri-vate information transfer from firms to favored audiences, which includeslarge and concentrated institutional owners, we would expect concentratedinstitutional ownership to have a less negative or dampening effect on dis-closure (or zero effect if Reg FD was completely effective) in the post–RegFD period. We examine this issue by including an additional explanatoryvariable that interacts the concentrated institutional ownership variable withthe FD variable.

Table 7 provides the results of including FD (a dummy variable thatequals 1 for the post–Reg FD years 2001 and 2002, and 0 otherwise), INST5or INSTCONS, and the interaction of either of these two concentrated insti-tutional ownership measures with FD in our occurrence regressions. Severalobservations can be made from the results. First, the coefficients for INST5and INSTCONS both turn out to be negative and statistically significant atthe .01 level as expected, suggesting that the probability of issuing a forecastis lower when institutional ownership is highly concentrated (similar resultswere obtained when we performed the regressions without the interactionterms).24 The interaction terms INST5 ∗ FD and INSTCONS ∗ FD, however,

23 Similar variables are used in the prior literature (e.g., Brickley, Lease, and Smith [1988],Agrawal and Mandelker [1990], Baysinger, Kosnik, and Turk [1991]). Bushee [1998] andBushee and Noe [2000] devise a compound partition that combines private benefits and invest-ment horizons whereby institutions are separated into three classes: dedicated, quasi-indexers,and transient. As an alternative to our two measures, we conducted a separate analysis usingBushee’s three-way classification. The results of this separate analysis (not reported) showedthat firms with a larger fraction of shares held by dedicated institutions were less likely to is-sue earnings forecasts. This is consistent with the results we report based on our measures ofinstitutional ownership concentration.

24 Similar results are found for the specificity (SPECIFIC) regression (i.e., higher institutionalconcentration leads to forecasts of lower precision). However, the concentrated institutionalownership variables are not significant in explaining forecast error (ERROR) and bias (BIAS).


T A B L E 7Regression Results of the Effect of the Nature of Institutional Ownership on the Probability of Occurrenceof an Earnings Forecast and the Differential Impact of Such Ownership in the Pre– and Post–Regulation

Fair Disclosure Periods

Predicted Sign Model 1 Model 2

Intercept ? −1.3655 −1.5640(−11.786)∗∗∗ (−14.998)∗∗∗

OUTDIR + 0.0030 0.0030(3.981)∗∗∗ (3.997)∗∗∗

INST + 0.0122 0.0098(11.943)∗∗∗ (14.190)∗∗∗

INST5 − −0.0117(−4.759)∗∗∗

INSTCONS − −2.5722(−6.812)∗∗∗

INST5 ∗ FD + 0.0041(1.461)∗

INSTCONS ∗ FD + 0.9921(1.531)∗

LMVAL + 0.0290 0.0442(2.150)∗∗ (3.424)∗∗∗

AUDIT + 0.0343 0.0364(0.973) (1.032)

NUMEST + 0.0190 0.0190(5.245)∗∗∗ (5.260)∗∗∗

DISPFOR − −0.0416 −0.0332(−0.251) (−0.199)

LITIGATE ? 0.1880 0.1868(4.873)∗∗∗ (4.846)∗∗∗

MKBK − 0.0051 0.0050(1.529) (1.509)

LOSS − −0.4011 −0.4100(−8.794)∗∗∗ (−9.026)∗∗∗

NEWS − 0.0180 0.0189(0.577) (0.604)

EARNVOL − 0.1596 0.1603(0.846) (0.855)

BETA − −0.1565 −0.1553(−5.949)∗∗∗ (−5.912)∗∗∗

FD + 0.4894 0.5669(6.448)∗∗∗ (15.452)∗∗∗

Log likelihood −4,596.66 −4,598.07No. of observations 7,745 7,745



turn out to be positive and statistically significant at the .1 level, indicatingthat the effect of the concentrated institutional ownership is less negative in

A possible explanation for this latter result is that once the decision to disclose is made, theconcentrated institutional owners may not want the information to be biased or erroneous forlitigation reasons.


the post–Reg FD period than in the pre–Reg FD period. Additional testsreveal that in the post–Reg FD environment, the marginal impact of con-centrated ownership remains negative and significant (p-value = .01). Theseresults suggest that Reg FD has been partially effective in reducing private in-formation communication, although institutions seem to continue derivingprivate information benefits, even in the post–Reg FD era. This is consistentwith an Institutional Investor [2001] survey of approximately 1,600 CFOs thatfinds that although almost 57% of CFOs had private conversations with an-alysts in the pre–Reg FD environment, only 37% continue to do so in thepost–Reg FD environment.

We carry out additional analysis to explore differences in the associationbetween institutional ownership, outside directors, and forecasts disclosure.Untabulated results indicate that there is no significant shift in coefficientvalues of INST and OUTDIR across the two regimes.

5.4 FIRM CHARACTERISTICS AND OUTSIDE DIRECTORS

One possible explanation for the results relating to OUTDIR is that theproportion of outside directors is related to underlying firm characteristics,which could also be related to disclosure. However, there is little prior workrelating firm characteristics to board composition. Denis and Sarin [1999]find that the proportion of outside directors is related to the size of firm,size of the board, and the industry median ratio of market to book value. Wetherefore carry out a two-stage analysis where we initially regress OUTDIRon these characteristics. The error term from this regression represents theportion of OUTDIR not explained by the firm characteristics identified in theprior literature. In the second stage, we regress OCCUR on the error termfrom the first-stage regression and control variables. Untabulated resultsfrom this analysis find that the error term is significant in explaining OCCUR ,suggesting that OUTDIR continues to be significant after controlling forknown underlying firm characteristics that affect OUTDIR .

It is also possible that the proportion of outside directors is the resultof greater investor oversight that is also the source of greater disclosure.To examine this possibility, we carry out a simultaneous equation analy-sis with a system of three equations where OCCUR , INST , and OUTDIRare endogenous.25 We include several variables that proxy for investoroversight in the OUTDIR regression, including analyst following, indus-try dummy, size, and institutional ownership. OUTDIR continues to be sig-nificant in explaining OCCUR in this specification. Although this suggeststhat outside directors are associated with disclosure beyond that accountedfor by proxies of investor oversight, we cannot rule out this alternativeexplanation.

25 See the excellent survey paper on boards of directors and endogeneity by Hermalin andWeisbach [2003].


5.5 ENDOGENEITY BETWEEN INST AND NUMEST

O’Brien and Bhushan [1990] document a simultaneous relationship be-tween INST and NUMEST . It is therefore possible that the results for INSTcould be due to the underlying influence of NUMEST on INST . We attemptto control for this possibility in two ways. First, we carry out a simultaneousequation analysis with a system of three equations where OCCUR , INST ,and NUMEST are endogenous. INST continues to be a significant explana-tory variable of OCCUR in this specification. Second, we carry out a two-stage analysis similar to the previous OUTDIR analysis. In the first stage weregress INST on NUMEST and other control variables. In the second stagewe regress OCCUR on the error term from the first regression and on con-trol variables. The error term is significant in explaining OCCUR , suggest-ing that INST continues to be significant after controlling for the effect ofNUMEST .

5.6 ANALYSES USING QUARTERLY FORECASTS

The analyses and results so far are based on annual forecasts. Separateanalyses are also carried out using a sample of quarterly forecasts. Un-tabulated results find that our corporate governance variables continue tobe significant in their association with forecast occurrence and frequency.These results continue to hold after carrying out the simultaneous equationanalysis and other robustness checks detailed in the previous subsections.As with the annual forecasts, institutional ownership is also significant in ex-plaining the specificity of management forecasts. Furthermore, institutionalownership is positively associated with more accurate and more conserva-tive forecasts. However, unlike the annual forecast sample results, outsidedirectors are not significant in explaining forecast error and bias. The re-sults of the quarterly analyses are subject to the following limitation. Ouranalysis is carried out using annual governance measures. To the extentthat quarterly changes in governance variables influence quarterly disclo-sure, the power of our tests would be compromised. The quarterly error andbias regressions tend to have lower power (compared with those based onannual forecasts reported in table 4), with some control variables turninginsignificant in these regressions. This could be attributable to a structuraldifference in the error and bias of quarterly and annual forecasts that mightmerit separate investigation.

5.7 OTHER ROBUSTNESS CHECKS

We carry out additional analyses using the number of outside directorsas an alternate proxy for outside directorship. The results are qualitativelysimilar to those using the proportion of outside directors. Finally, we carryout a simultaneous equation analysis with a system of four regressions whereOCCUR , INST , OUTDIR , and NUMEST are the endogenous variables. INSTand OUTDIR continue to be significantly associated with OCCUR .


Regression diagnostics revealed that multicollinearity was not a se-vere problem in the individual regressions because the condition num-bers in the regressions did not exceed 50 (see Belsley, Kuh, and Welsch[1980]). We identified a few influential observations in our samples andexamined the robustness of our results to these influential observationsby eliminating the influential observations. The results are essentiallyunchanged.

6. Summary and Conclusions

The focus of this study is to investigate the relation between a set of gover-nance mechanisms and voluntary disclosure, surrogated by the propertiesof management earnings forecasts. Specifically, we examine the associationbetween governance proxies (outside directors and institutional investors)and the occurrence, frequency, specificity (i.e., precision), accuracy, and biasof earnings forecasts disclosed by firm managers. Using a sample spanningfrom 1997 to 2002, we find that institutional ownership and the proportionof outside directors are favorably associated with the likelihood of forecastoccurrence and frequency of forecast issuance. The evidence also indicatesthat the forecasts issued are more specific and accurate. In addition, gov-ernance mechanisms are negatively associated with managerial optimism;that is, firms with greater institutional ownership and percentage of outsidedirectors are likely to issue less optimistically biased (or more conservative)forecasts. Subsample analysis indicates that the coefficients on outside direc-tors and institutional ownership are not significantly different in the pre–and post–Reg FD eras.

Additional analysis suggests that concentrated institutional ownership isnegatively associated with forecast properties. The association is less negativein the post–Reg FD environment, which is consistent with Reg FD reducingthe ability of firms to privately communicate information to select audiences.This finding is also consistent with survey data that document a reduction,but not elimination, of private communication between CFOs and analystsin the post–Reg FD era.

This article contributes to the literature on discretionary disclosure andon corporate governance. We find that the monitoring mechanisms arerelated to the extent and quality of discretionary information a managerdiscloses. These results are interesting given the current scrutiny of corpo-rate governance mechanisms and the state of the financial reporting sys-tem. Recent financial bankruptcies have led to a greater focus on the needfor stronger governance and more transparent disclosure. Our results sug-gest that the two are linked and that promoting stronger governance couldalso promote more transparent disclosure. This article also contributes tothe literature on the effectiveness of governance variables by focusing ona firm attribute—information disclosure environment—that is novel to theliterature.


APPENDIX

Variable Definitions

OCCUR = 1 if the firm made an earnings forecast during a fiscalperiod, and 0 otherwise

OCCURL = 1 if the firm made an earnings forecast during fiscal pe-riod t–1, and 0 otherwise

FREQ = number of management forecasts made by a firm from1997 to 2000

SPECIFIC = 3 if the company made a point forecast, 2 for a closed-interval forecast, 1 for an open-interval forecast, and 0for other kinds of forecasts

ERROR = absolute value [management’s forecasted EPS − actualEPS]/price at the beginning of the fiscal period

BIAS = [management’s forecasted EPS − actual EPS]/price atthe beginning of the fiscal period

INST = percentage of common shares held by institutionsINSTCONS = Herfindahl index of concentration of institutional own-

ership measured as:

N∑i=1

(shares held by institution i

total shares outstanding

)2

OUTDIR = percentage of the board of directors who are not officersof the firm

HORIZON = number of days between the forecast date and the fiscalperiod end date

SURPRISE = absolute value [management’s forecasted EPS − mediananalysts’ forecasted EPS]/price at the beginning of thefiscal period

LMVAL = natural log of the market value of a firm’s common equity(in $ millions) at the beginning of the fiscal period

AUDIT = 1 if the auditor is one of the Big 5 (previously Big 8)auditors, and 0 otherwise

NUMEST = number of analysts following the firm. For tests of occur-rence and frequency, this is based on the last-available an-alyst forecast information in First Call before fiscal period-end. For tests of specificity, accuracy, and bias, this is basedon the last-available analyst forecast information in FirstCall before the management forecast.

DISPFOR = standard deviation of analyst forecasts divided by medianforecast. For tests of occurrence and frequency, this isbased on the last-available analyst forecast information inFirst Call before fiscal period-end. For tests of specificity,accuracy, and bias, this is based on the last-available analystforecast information in First Call before the managementforecast.


LITIGATE = 1 if the firm belongs to the biotechnology (SIC codes2833–2836), R&D services (8731–8734), programming(7371–7379), computers (3570–3577), electronics (3600–3674), or retailing (5200–5961) industry, and 0 otherwise

MKBK = market value of equity divided by the book value of equityat the beginning of the fiscal period

LOSS = 1 if the firm reported losses in the current period, and 0otherwise

NEWS = 1 if the current-period EPS is greater than or equal to theprevious-period EPS, and 0 otherwise

EARNVOL = standard deviation of quarterly earnings before extraor-dinary items for the 12 quarters before the current fiscalyear, divided by median assets over the 12 quarters

DE = long-term debt/total stockholders’ equity at the begin-ning of the fiscal period

YIELD = dividend yield for the fiscal periodBETA = equity beta for the fiscal year

LIQUIDITY = log(trading volume/shares outstanding) for the fiscalyear

SNP = 1 if the company was part of the S&P 500, and 0 otherwiseFD = 1 if the observation is related to the post–Regulation

Fair Disclosure period (after October 2000), and 0otherwise

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