Managerial and Market Responses to Regulation Fair Disclosure: The Case of ManagementForecasts*

CARLA CARNAGHAN, University of Waterloo

RANJINI SIVAKUMAR,** University of Waterloo

Submission for the Northern Finance Association Meetings, 2003

*The assistance of Thomson/First Call in providing data is gratefully acknowledged, as isfinancial support from the University of Waterloo.** Contact author: Centre for Advanced Studies in Finance, School of Accountancy, Universityof Waterloo, Waterloo, Ontario N2L 3G1. Phone: (519) 888-4567 x 5703; fax: (519) 888-7562;rsivakumar@uwaterloo.ca

Managerial and Market Responses to Regulation Fair Disclosure: The Case of ManagementForecasts

Abstract

We examine management forecasts as one type of highly sought after voluntarydisclosure to determine whether Regulation Fair Disclosure (Reg FD) has improved the qualityand quantity of public disclosures. We find that the information disclosed by managers hasincreased in frequency and improved in terms of both specificity and supplemental informationprovided with the disclosure. We also find that Reg FD has reduced private disclosure ofinformation, but has not resulted in greater returns volatility. Our results suggest that Reg FD hasachieved one of its stated goals of providing "a more level playing field" to all investors.

JEL Classification Code: G14, M41

Key words: Financial disclosure, management earnings forecasts, Regulation Fair Disclosure,capital markets

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1. Introduction

The U.S. Securities and Exchange Commission (SEC) issued Regulation Fair Disclosure

(Reg FD) in August 2000 in response to perceived inequities in disclosure, wherein financial

analysts and select investors were being privately provided with material information before its

public release. The SEC was concerned that these disclosure practices were disadvantageous to

individual investors. In passing Reg FD, the SEC hoped that “This regulation will place all

analysts on [an] equal footing with respect to competition for access to material

information.”(SEC 2000)

Considerable controversy ensued in the business press regarding the expected and actual

effects of Reg FD, which became effective on October 23, 2000. A survey conducted by

Thomson Financial and Carson Global Consulting in late 2000 found that 29% of companies said

they had less contact with analysts, and 27% said they gave out less information about operations

than they had previously. However, 98% of those surveyed said they were announcing general

access to earnings conference calls, while 25% had expanded their earnings guidance (Craig,

2001). The Securities Industry Association claimed that business and securities firms could

spend hundreds of millions of dollars complying with the new regulation, and that it had a

“chilling effect” on the amount of information companies were releasing (Bresiger 2001).

In this study, we examine the effect of Reg FD on management earnings forecasts (MEF).

These forecasts provide an interesting context in which to study Reg FD, because the forecasts

are voluntary, but at the same time highly desired by analysts and investors since they provide

future oriented information in the form of earnings guidance (Hutton 2002). Managers who

comply with Reg FD have options ranging from saying nothing to providing management

earnings forecasts with supplementary information. Accordingly, examining the effects of Reg

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FD on MEF can provide insights into how managers have chosen to respond along this

continuum, and the factors that affect this choice.

We select a random sample of 500 from those firms listed on NYSE/AMEX, or NASDAQ

with complete Research Insight and CRSP data for the period October 1997 - December 2000.

We then examine press releases made by those firms both pre and post Reg FD for occurrences of

MEF, and code the forecast characteristics such as form and timing as well as the supplementary

information provided with the forecast. These characteristics and the market responses to the

MEF are then examined to determine the effects of Reg FD. We find that after Reg FD became

effective, firms issue more management forecasts. We find as well that the specificity of the

forecasts has increased. We also find that there has been an increase in the amount of

supplemental information provided with the forecasts. Overall, the quality and quantity of public

disclosure provided in management forecasts has increased. The size of the price bid-ask spread

in the period immediately prior to the forecast issuance has become smaller after Reg FD while

the price reaction during the forecast has increased slightly, which suggests that information

leakage has been reduced. Contrary to some of the concerns expressed in the business press, we

find no evidence of increased returns volatility in response to the forecast release. Overall, we

interpret our findings as suggesting that Regulation FD has had its desired effect of providing

more equitable access to information, and that at least the public information available to all

investors regarding management earnings guidance has improved.

The remainder of this paper is organized as follows: The next section provides some

additional background on Reg FD as well as related research; Section Three specifies our

hypotheses, models and variables used to test the hypotheses; Section Four explains our analysis

and results; and Section Five provides a summary of our findings and discusses the limitations of

our study.

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2. Background and Related Research

Reg FD was passed in response to concerns about selective disclosure of material information

by managers to analysts and institutional investors. The SEC was also concerned that selective

disclosure was being used by companies to both curry favour with particular analysts in return for

more favorable stock recommendations, and to punish analysts who did not provide favorable

recommendations by denying them access to these private disclosures.

The regulation requires that:

(1) When a senior executive or representative of an issuer intentionally discloses material

information to certain enumerated persons (in general, securities market professionals or

holders of the issuer's securities who may well trade on the basis of the information) it does

so through public disclosure, not through selective disclosure; and

(2) Whenever a senior executive or representative of an issuer learns that it has made a non-

intentional material selective disclosure, the issuer make prompt public disclosure ( the later

of 24 hours or commencement of next day’s trading) of that information.

Under the regulation, the required public disclosure may be made by filing or furnishing a

Form 8-K, or by another method or combination of methods that is reasonably designed to

achieve broad, non-exclusionary distribution of the information to the public (e.g. press releases,

wire service releases, or conference calls, but not web sites by themselves (McBride 2000; SEC

2000). Reg FD became effective on October 23, 2000.

The business press has reported widely differing predicted and actual effects of Reg FD.

Some polls and articles suggest that companies have less contact with analysts and are providing

less disclosure (Forster 2000; Opdyke 2001; CPA Journal 2001). Others indicate that many

companies have opened up conference calls and increased disclosure (Craig 2001; Weber 2000;

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Heffes 2002), and some suggest the majority of companies planned no changes to their disclosure

practices (e.g. Investor Relations Business, 2000). Some of these same articles suggest that the

medium used to provide disclosure has changed, with some companies now providing less

information in conference calls (Munk 2001) and others providing additional information such as

forecasts with their quarterly earnings announcements. This latter approach has been

recommended by the National Investor Relations Institute as well as securities litigation experts

as one means of dealing with Reg FD (Forster 2000; McCarthy 2001).

Business press predictions of market consequences have also varied, with early expectations

suggesting that analysts’ earnings estimates would likely be more varied. In addition, there was

an expectation that there would be a greater market price reaction to news because of a lack of

“leakage” via private disclosure to analysts before the formal news release (McGough and Bryan-

Low, 2000; Williams and McGough, 2000). However, later analyses have suggested that

company stock prices have not been affected (Heffes, 2002).

A variety of academic studies have tried to clarify what effects Reg FD has had, primarily on

analyst forecasts and the associated market responses, and on the market responses to earnings

announcements. These findings are generally as follows:

1. There has been no increase in returns volatility around earnings announcements post Reg FD

(Bailey, Li, Mao and Zhong 2003; Eleswarapu, Thompson, and Venkataraman 2003) and there

are smaller price reactions to earnings announcements (Shane, Soderstrom, and Yoon 2001;

Eleswarapu et al 2003). However, Bushee, Matsumoto, and Miller (2003) find returns

volatility has increased post-FD during conference calls.

2. There are mixed findings regarding analyst forecast dispersion and accuracy, with Mohanram

and Sunder (2002), Agrawal and Chadha (2002) and Bailey et al (2003) finding lower

accuracy and increased dispersion and Heflin, Subrahmanyam, and Zhang (2003) finding no

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evidence of this. Shane et al (2001) find analyst accuracy is no different post-FD by the end of

the quarter being forecast, although analysts are less accurate at the start of the quarter. Both

Bushee et al (2002) and Irani (2003) find analyst forecast accuracy has increased post FD after

conference calls.

3. There appears to be less information leakage, at least of earnings announcements (Eleswarapu

et al 2003; Gadarowski and Sinha 2002).

4. There has been an increase or no decrease in some types of disclosure. Heflin et al (2003) and

Cotter, Tuna, and Wysocki (2002) both find MEF have increased; Straser (2002) finds

increases in various disclosure types including SEC filings and press releases, and Bushee et al

(2002) find no evidence of decreases in the amount of information provided during conference

calls post-FD.

This pattern of results suggests that Reg FD has achieved its objective of reducing

information leakage, at least with respective to earnings announcements. It also appears that the

public volume of disclosure is no lower and may be higher for some types of disclosures. It is not

clear that the information being provided is of the same quality as before, given some evidence of

lower analyst consensus and accuracy. Both Bailey et al (2003) and Mohanram and Sunder

(2002) interpret some of their evidence as suggesting that analysts need to exert more effort post

FD to acquire and interpret information. The previous studies generally do not directly examine

the quality of public disclosures provided by managers post-FD, instead relying on market

proxies to indicate quality.

We focus on MEF as one type of public disclosure that is highly sought after by analysts and

institutional investors (Hutton 2002; Bamber and Cheon 1998; Frankel, Johnson, and Skinner

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1999; Hutton et al. 2003; Bushee, Matsumoto and Miller 2003). We examine whether the quality

of this disclosure has changed post-FD and investigate market reactions to MEF pre and post -FD.

We extend the prior studies of the effects of Regulation FD in several ways. First, we focus

on MEF, whereas most of the prior studies have focused on earnings announcements or analyst

forecasts. Findings from studies such as Patell (1976), Pownall, Wasley, and Waymire(1993),

and Skinner (1994) have helped establish that markets and analysts react to information found in

MEF, and thus that these represent an important form of disclosure. These studies also help to

establish relationships between the format and information content of the MEF that are related to

the nature of the news being provided. MEF are also a particularly interesting area to study the

effects of Reg FD, because unlike earnings announcements, managers can choose not to provide

earnings guidance, or provide less precise, the same, or additional guidance in response to Reg

FD. The range of possible managerial responses to Reg FD is thus quite large. Of the previously

cited studies, only Heflin et al (2003) and Cotter et al (2002) directly examine MEF. In Heflin et

al. (2003), they find that the frequency of issuance of MEF has more than doubled post Reg FD,

and there has been an increase in the proportion of range forecasts and decrease in the proportion

of point forecasts (which Cotter et al (2002) also find). Cotter et al also find an increase in bad

news MEF relative to analyst consensus estimates post FD, which continues a trend they identify

as occurring throughout the 1990's. We elaborate on these studies to further explore how Reg FD

has affected the quality of management disclosures, and include control variables to help refine

the interpretation of previous findings.

Second, we also consider the supplemental information provided with the MEF, such as

explanations for the forecast and whether the explanations are verifiable. Hutton et al (2003) find

that these supplements are important in determining market and analyst reactions to MEF, and

thus clearly have information content. In a broader non-MEF context, Francis, Schipper, and

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Vincent (2002) also find that supplementary disclosures have information content, in that those

made at the time of earnings announcements have increased over time, and are associated with

the increase in market reaction to earnings announcements. Since managers are less able to use

analysts as information filters, it is an empirical question whether they will provide more

information with the MEF to aid the audience in interpreting the forecast, or will choose not to

because of concerns about proprietary costs. We use the characteristics of the MEF as well as the

supplemental disclosures accompanying the MEF to assess the quality of the MEF.

Third, we also examine market responses to the MEF pre and post FD, including controls for

this supplemental information. None of the prior studies of the effects of Reg FD have

investigated potential differences in market response to MEF, yet such differences may now exist

given that managers should no longer be preferentially providing earnings ahead of a press

release on the same topic. However, if investors find MEF more difficult to interpret, then the

market response may have decreased. Finally, this line of investigation will also make clear

whether information asymmetry has been reduced for MEF, which was an apparent objective of

Reg FD.

Fourth, we use data collected from Lexis/Nexis to obtain the MEF for a random sample of

500 firms. This should avoid any possible differences over time in the coverage of the First Call

database that may create bias in trying to determine whether the frequency of MEF have

increased or decreased, and provides a different data source to corroborate MEF studies that have

used the First Call database.

We thus hope to contribute to the policy issues surrounding Reg FD by providing further

evidence on whether MEF, as one particular highly desired type of disclosure, has changed post-

FD. The next section provides our specific research questions and hypotheses, and describes our

research design.

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3. Hypotheses and Research Design

Hypotheses

In this study, we investigate the policy goals of Reg FD. Specifically, we examine whether

Reg FD has improved the quality and quantity of public disclosures. As noted by Healy and

Palepu (2001), there is little empirical research on disclosure regulation. Studies of voluntary

disclosure generally are based on managerial motives for making such disclosures in terms of

managerial costs and benefits. Predicting Reg FD's effects therefore requires some basis for

determining how this regulation would affect managerial costs and benefits. Reg FD's primary

goal is to force managers to disclose MEF approximately concurrently in a public forum to

analysts, institutional investors, and smaller investors, rather than relying on financial analysts to

interpret and filter particular disclosures. There may also be differences in the detail or nature of

information provided with MEF post Reg FD if there are managerial concerns about how the

news will be interpreted.

The studies most related to our investigation of the change in disclosure mandated by Reg FD

are Hutton (2002), Hutton et al. (2003), Bushee et al (2003) and Bamber and Cheon (1998), .

Hutton et al (2003) find that supplementary statements have significant information content.

Hutton (2002) finds that analyst objectivity and investor response are affected by management’s

decision to provide earnings guidance and thus clearly have information content. Bushee et al

(2003) focus on the determinants of providing more open versus more closed access to

information, while Bamber and Cheon (1998) consider factors affecting both the medium for

disclosure and the specificity of the disclosure. Considered jointly, these studies suggest that the

main determinants of disclosure medium and specificity are (1) demands and composition of the

investor base; (2) complexity of the information being discussed; (3) legal liability; and (4)

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proprietary costs. These determinants will guide our choice of factors to be controlled so that we

can isolate Reg FD's effects on management earnings forecasts and the associated market

responses.

Characteristics of Management Earnings Forecasts after Reg FD

Post FD, those firms that previously tended to disclose more privately are likely to feel

pressure from analysts as well as investors to provide public MEF. The need for public

concurrent disclosure will drive managers to add some supplemental information to the MEF to

aid in interpretation, since the previous filtering and interpretation done by analysts prior to the

public disclosure will be absent, and it will be more difficult to provide supplemental material

information informally. This leads to our first set of hypotheses, the first of which should provide

a confirmation of Heflin et al (2003) and Cotter et al.'s (2002) findings. All hypotheses are stated

in alternative form.

HYPOTHESIS 1. After Reg FD, the frequency of public MEF increases.

HYPOTHESIS 2. Firms are more likely to provide supplemental information with public MEFafter Reg FD.

If disclosure is somewhat "sticky" as previous research such as Lang and Lundholm (1993)

has suggested, managers may feel a need to provide more specific guidance because of the loss of

analysts as information intermediaries. However, the existence of proprietary costs may reduce

or distort disclosure (Verrecchia, 1983; Okuno-Fujiwara, Postlewaite, and Suzumara 1990;

Newman and Sansing 1993), particularly for good news. However, this will be somewhat

tempered in the case of good news MEF by concerns about proprietary costs outweighing the

benefits of public disclosure. We therefore propose:

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HYPOTHESIS 3a. Managers issue more specific public MEF after Reg FD.

HYPOTHESIS 3b. Good news public MEF are less specific after Reg FD.

Hypothesis 3a will provide a confirmation to Cotter et al.'s (2002) findings

Market Reactions to MEF after Regulation FD

One intended effect of Reg FD was to reduce informed trading by those who had earlier

access to disclosures, and thus create a "more level playing field" for all investors. Bid-ask

spreads are usually viewed as one measure of the likelihood of informed traders, with wider-bid

ask spreads used as a means to reduce the costs of such trading for market makers. Baginski

(1995) interprets the tendency to provide MEF with a greater surprise value during non-trading

periods as an attempt by some managers to give less informed investors more evaluation time,

and thus reduce asymmetry. Evidence of the impact of MEF on bid ask spreads is provided by

Coller (1997). She finds that firms providing MEF do have increasing bid ask spreads in the 12

months prior to the forecasts, and that the spread is generally smaller in the nine days after the

forecast compared to the nine days prior to the forecast. If Reg FD has been successful in

reducing information asymmetry, then bid-asks spreads leading up to the forecast should be

smaller after Reg FD. This suggests the following hypothesis:

HYPOTHESIS 4. Bid-ask spreads in the period leading up to the release of the MEF aresmaller after Reg FD.

Patell (1976) and Penman (1980) also note that during the period before the forecast, stock

price adjustment was in the same direction as the change in investor expectations, suggesting that

at least some investors already had the information or alternative sources of the information. This

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would suggest that if information leakage is reduced or eliminated, the price reaction to the news

at the time of the MEF announcement will increase. We therefore hypothesize the following:

HYPOTHESIS 5. Price reactions in the window immediately surrounding the release of theMEF are larger after Reg FD is passed.

When MEF are released, the lack of prior leakage suggests that time to interpret the MEF will

have been reduced, which may result in greater diversity of beliefs. The existing FD studies of

analyst forecasts such as Mohanram and Sunder (2002) and Bailey et al (2003) suggest that

traders with superior information processing or acquisition abilities may differentially interpret

the MEF when it is released. These two factors will likely result in a greater diversity of opinion

in a shorter time frame that will affect price volatility. Hence,

HYPOTHESIS 6. Abnormal return volatility is greater in the period immediately after theissuance of MEF after Reg FD.

Research Design

Sample

We choose a random sample of 500 U.S. firms from all those listed on NYSE/AMEX or

NASDAQ with complete market and financial statement data for October 1997 - December 2000.

Financial statement and market data were obtained from Standard and Poor's Research Insight

and the CRSP datasets respectively. We exclude utilities and financial-services related firms.

Specifically, each firm should have complete market returns data from November 1997 onwards

and complete financial statement data at least for one year prior to the fiscal year ending October

31, 1998. We then searched for annual and quarterly management forecasts for these companies

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in the Business Wire and PR Newswire segments of Lexis-Nexis for press releases that contained

terms that matched a list of keywords for October 1998 - March 2001 (the latest month available

when we began data collection).1 The intent behind this sample period was to have two

equivalent seasons (October 23, 1998-March 31, 1999 and October 23, 1999 - March 31, 2000) to

our post-FD time period of October 23, 2000 – March 31, 2001 so that we could control for

trends that might affect our results. The resulting sample should be reasonably representative of

the NYSE/AMEX and NASDAQ population of companies (other than utilities and those in

financial service industries) and their management forecast disclosure behavior both before and

after the passing of Regulation FD.

Student coders who did not know the study hypotheses then analyzed each of the press

releases retrieved to determine if it was a MEF. If it was a MEF, the student coded its

characteristics, including: whether the forecast was annual or quarterly, the period being forecast,

whether the forecast was qualitative or quantitative, the estimate provided by management, and

the number of supplemental information items provided with the MEF. For purposes of this

study, a MEF was defined as a prediction of a future period's earnings for the company as a whole

by a company representative, where the prediction is made before the end of the period being

forecast.

Analyst data were obtained from Thomson Financial Services First Call Historical Database.

External financing data was obtained from Thomson Financial Securities SDC Platinum

1 The precise search phrase used in Lexis-Nexis was: (((anticipat! OR expect! OR forecast! OR predict! ORestimat! OR project!) W/p (loss! OR profit! OR earning! OR (financial performance) OR (net income) OREBIT OR EBITDA OR result) ) AND (manager OR management OR CEO OR executive OR president ORofficial! OR officer OR spokesperson OR spokesman OR spokeswoman)) and (headline (earn! or eps orresult! or loss! or profit! Or performance or ebit or ebitda or income or anticipat! OR expect! or forecast orestimat! or predict! or project!)) and date aft 10/31/98 and date bef 04/01/01)

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Database. The resulting sample consisted of 467 firms 2 with 1,046 MEF issued over the entire

sample period.3

Models for Testing Hypotheses

We test our hypotheses at both the univariate and multivariate level. While the dependent

variable varies depending on the hypothesis being tested, the explanatory variables in the

multivariate models remain largely the same for each model. When we need to control for

seasonal differences and /or the length of time being studied, we divide our sample period into

three groups: Pre-FD1 (October 23, 1998-March 31, 1999); Pre-FD2 (October 23, 1999 - March

31, 2000) and post-FD (October 23, 2000 – March 31, 2001). Splitting the pre-FD period into

two sub-periods gives us some ability to detect trends across time. However, discussions

concerning draft versions of Regulation FD began in December 1999, so companies may have

begun altering their behavior between December 1999 and October 2000 when regulation became

effective. Assuming this is the case, the Pre-FD1 period also gives us a comparison period that is

unaffected by anticipation of Reg FD. In our tests, we would expect no significant differences

2 Some of the original random sample of firms were lost because of changes in some of the explanatoryvariables made after the sample was chosen that required re-running the Research Insight data retrieval.These firms were not available on the newer Research Insight dataset. Exploration of the reasons for theirloss suggests that the firms in question substantially changed their business sometime around 2001. Giventhis might change their disclosure behavior, we have not made additional efforts to re-integrate these firmswith the sample.3 A number of the prior studies of MEF such as Skinner (1994) drop those announcements that are issuedconcurrently with an earnings announcement. We choose not to drop such announcements for this studybecause some of the business press articles discussing the effect of Regulation FD specifically suggestedthat one possible way of providing earnings guidance post-FD was to issue MEF with earningsannouncements. If we dropped such MEF, then we would be missing part of the impact of Reg FD ondisclosure frequency. In addition, more recent studies such as Heflin et al (2003) and Cotter et al (2002)use the Thomson/First Call database to collect MEF. It is likely that such databases include MEF issuedwith earnings announcements, and so better comparability is provided by having these in our sample. Toprovide some continuity with both the earlier and the more recent management forecast literature, weprovide some descriptive information in our tables on the characteristics of MEF issued in isolation versusthose issued concurrently with earnings announcements.

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between pre-FD1 and pre-FD2 after controlling for other factors if companies had not begun to

alter their behavior in anticipation of Reg FD, but we would expect significant differences in

comparing pre-FD1 to post-FD and pre-FD2 to post-FD.

All of our variables are defined in Table 1, and are discussed next.

<<< Insert Table 1 about here >>>.

Our models to explain MEF characteristic are based on the following specification (firm and

period subscripts omitted). In all cases, the equations are estimated as ordered-response logistic

models since all of the dependent variables are ordinal. The logistic regression fits the probability

that the MEF characteristic is from the dependent variable category i or smaller, given the

explanatory variables. A positive coefficient means that the higher values of the explanatory

variable are associated with a larger value of the MEF characteristic category value, while a

negative coefficient means that the higher values of the explanatory variable are associated with

smaller values of the MEF characteristic category value. For example, a positive coefficient on

Analyst when the MEFNum version of (1) is fitted means that larger analyst followings are

associated with more disclosures.

(MEF Characteristic ) = β0 +β1 AfterFD + β2 Analyst + β3 Inst + β4Disp_share + β5 Intang +β6

MkttoBook + β7Var_Rev + β8 Financedum + β9 Litdum + β10 ln(Asset),+ β11 Earn_sprze +β12 Earn_sprzedum + β13Gnewsdum + ε (1)

where MEF Characteristic for a particular firm and period is one of MEFNum, MEFSuppl, or

MEFPrecision.

MEFNum is defined as an ordinal variable, with values of 1 to 3 assigned corresponding to

the number of forecasts made by the company, and a value of 4 assigned when the company has

made four or more forecasts in a particular period. For purposes of testing Hypothesis 1

regarding an increase in the frequency of MEF post-FD, we compare the total number of MEF

15

issued in the post-FD period versus the pre-FD1 period and versus the pre-FD2 period.

Accordingly, we keep only one firm level observation for each company for each of the three

periods.4

MEFSuppl is also defined as an ordinal variable, with values of zero to two assigned

corresponding to the number of supplementary items disclosed by the company, and a value of

three assigned when the MEF contains three or more supplemental items. Supplemental items

include internal and external factors explaining the forecast, and additional forward looking

verifiable items of information, such as revenue forecasts. This approach to measuring

supplementary information is based on Hutton, Miller and Skinner (2003). MEFSuppl provides a

proxy for how much additional information management is provided along with the forecast.

MEFPrecision is defined as a binary variable where 1=quantitative forecasts and

0=qualitative MEF.5 When examining the effects of Reg FD on MEFPrecision, we also include a

control variable Horizon as per the findings of Bamber and Cheon (1998) and Baginski and

Hassell (1997), which is the length of time between the issuance of the MEF and the end of the

period being forecast.

We expect the coefficient on the AfterFD dummy variable to be positive if Hypotheses 1 – 3a

are supported, and the coefficient on the AfterFD *Gnewsdum to be negative if Hypothesis 3b is

supported.

4 Where multiple observations exist for a particular company in a period, we keep the one associated withthe earliest press release date in the period for a quarterly MEF. Since the frequency represents the totalMEF made by that company for that period, this determines only what explanatory variables are kept.5 So that the ordering of our ordinal variables is consistent among MEFNum, MEFSuppl and MEFPrecisionsuch that small values are always less desirable and larger values more desirable, we estimate the logitregressions with the dependent variables in descending order. This ensures that our explanatory variablecoefficients should always have the same sign if they have a similar effect on the dependent variables, e.g.Analyst should always have a positive sign if it leads to greater numbers of forecasts, more supplementalinformation, and more precise forecasts.

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The variables Analyst and Disp_share represent respectively analyst following and the degree

of dispersion of stock ownership. These are proxies for the degree of demand for information,

with analyst following shown by Lang and Lundholm (1996), Frankel, Johnson, and Skinner

(1999) and Cairney and Richardson (2000) to be positively associated with disclosure. Bushee et

al (2003) show these variables are related to whether conference calls are open or closed. Since

closed conference calls of material information such as MEF are not allowed under Reg FD, we

expect all of these variables to be positively associated with the issuance of MEF post FD.

Our proxies for complexity of information are Intang, MkttoBook , and Var_Rev. Previous

studies such as Tasker (1998), Lev and Zarowin (1999), and Bushee et al (2003) have suggested

that these variables may be reasonable proxies for firms with more complicated financial

disclosures and firms for whom financial statements are less informative. Investors in such firms

may require more assistance in predicting future performance and thus demand more and clearer

earnings guidance from managers. We would therefore generally expect the coefficients on these

variables to be positive when used to regress MEFNum, MEFSuppl and MEFPrecision.

Frankel, McNichols and Wilson (1995) and Ruland, Tung, and George (1990) show that there

is generally a positive relationship between issuance of forecasts and seeking of external

financing, so we include a dummy variable Financedum with a value of one for firms that seek

external financing in the 12 months following the issuance of the MEF. The expected sign on

Financedum is positive for MEFNum and unknown for the other dependent variables.

There is conflicting evidence from prior studies such as Skinner (1994), Skinner (1997) and

Francis, Philbrick and Schipper (1994) regarding the relationship between disclosure and

litigation. We do use a control variable to control for litigation risk based on industry

classification, as used in Soffer, Thiagarajan, and Walther (2000). We also include measures of

the most recently ended fiscal period's earnings surprise (the absolute magnitude of the surprise

17

and a dummy variable with a value of 1 if the sign is negative), since prior studies such as

Bamber and Cheon (1998) and Lang and Lundholm (1993) have shown that current performance

is related to voluntary disclosure. We would generally expect a negative relation between

EarnSprzedum and MEFNum, and have no expected direction for the other dependent variables.

We include the log of total assets as a control for size, since size is positively related to

voluntary disclosure (Lang and Lundholm 1993), although its effects on MEFPrecision and

MEFSuppl are unknown.6 Finally, we include a dummy variable GNewsDum for whether the

MEF is good news or bad news, since this has been found to be related to the likelihood of an

MEF being provided (Skinner 1994) and is also related to the precision of the disclosure (Kasznik

and Lev 1995) and the supplementary information provided (Hutton et al 2003). We include an

interaction variable AfterFD*GNewsDum to test Hypothesis 3b with respect to MEFPrecision.

Our market reaction models include the explanatory variables specified in Equation 1 except the

financing and litigation variables, but include the variables MEFPrecision and MEFSuppl as

forecast precision and supplementary information have been shown in prior research such as

Hutton et al (2003) to affect market response. The equation is estimated using ordinary least

squares. The model is:

(Market Response Characteristic) = β0 +β1 AfterFD + β2 Analyst + β3 Inst + β4Disp_share + β5

Intang +β6 MkttoBook + β7Var_Rev + β8 MEFPrecision + β9 MEFSuppl + β10 ln(Asset) +β11Earn_sprze + β12 Earn_sprzedum + β13Gnewsdum + β14Gnewsdum* MEFSuppl + ε

(2)

where the Market Response Characteristic is one of Abs_CAR, PreBidAsk or Vol_Ret. Abs_CAR

is defined as the absolute value of the cumulative excess returns (over the value weighted CRSP

index return) for days (-1, +1) centered around the press release date for the MEF. PreBidAsk is

6 We also use the market value of equity as an alternative to total assets, with no change to our conclusions.

18

the mean bid-ask spread from days –9 to –1 relative to the MEF press release date. Vol_Ret is the

standard deviation of market adjusted returns from days 0 to +15. For Hypothesis 4 we expect

the sign on AfterFD to be negative, while for Hypotheses 5 and 6, we expect the sign to be

positive.

To the extent that the information demand variables Analyst, Inst, and Disp_share also proxy

for informed trading (i.e. an audience demanding information from managers will also likely

engage in information acquisition, which increases the ex ante likelihood of informed trading),

we expect the coefficients on these variables to be positive when used to fit model (2) for

PreBidAsk , and negative when fitting model (2) for Abs_CAR. We have no expectations

regarding variable signs when fitting Model (2) for Vol_Ret. The effect of the information

complexity variables Intang, MkttoBook, and Var_Rev on PreBidAsk and Abs_CAR is unclear,

but we expect the greater difficulty of interpreting the information to lead to greater volatility in

the returns around the MEF issue date, and so expect these variables to have positive coefficients

when used to explain Vol_Ret. MEF that are more precise or that have more information to aid in

interpretation are less likely to result in differing interpretations, so we expect a negative sign on

MEFPrecision and MEFSuppl when fitting Model (2) for PreBidAsk and Vol_Ret. We would

also expect a greater price reaction to a clearer signal, so we expect the signs on these variables to

be positive when fitting Model (2) for AbS_CAR. The variables Earn_sprze and Earn_sprzedum

are to control for the effects of any earnings announcement issued concurrently with the MEF.

We have no expectations regarding the sign on Gnewsdum with respect to fitting Model (2) to

PreBidAsk or Vol_Ret.

19

4. Analysis

Descriptive Statistics

Tables 2-4 provide descriptive statistics about our sample to aid in comparing it to prior

research on MEF. Table 2 provides some descriptive statistics of the firm characteristics for

companies issuing MEF versus those that do not over the entire sample period. In total, of the

467 firms in the sample, 240 provided at least one MEF either pre or post Regulation FD, while

227 provided no MEF over the sample period. Panel A shows that, consistent with previous

research on voluntary disclosure, forecasting firms tend to be larger on average both in terms of

total assets and sales. The forecasting firms also have a greater net income and larger earnings

per share, but a smaller market to book value. A comparison of the medians suggests similar

findings, except that the market to book ratio of the forecasters and nonforecasters is now very

similar in size.

Panel B provides an analysis by major industry classification at the two digit SIC level. The

forecasting firms are more concentrated in the food products, primary metals, industrial

machinery and computers, electrical, durable goods and apparel industries, while the non-

forecasters appear to be more concentrated in the chemicals, instrumentation, and business

services industries.

<<< Insert Table 2 about here >>>.

Table 3 provides some detail on the issuance of MEF either in isolation (non-bundled MEF)

or concurrently with earnings announcements (bundled MEF). Panel A shows the forecasts for

the full sample period, divided into two full-year pre-FD periods and the five month post-FD

period. The table shows that nearly 73 percent of MEF in the sample are issued concurrently with

earnings announcements. Panel B shows the same comparison controlling for seasonality and

length of period. The table shows that the total number of MEF Post FD is nearly twice as great

20

as the number in the Pre FD 2 period, while there are roughly 50% more MEF in the Pre FD II

period relative to the Pre FD I period. Thus, while there is a trend over time to increasing

numbers of MEF, the increase between the Pre FD II period and the Post FD period is

substantially greater than the increase between the Pre FD I and Pre FD II period. Panel C shows

that while 119 of our sample firms provided forecasts before Reg FD, 157 firms provided

forecasts after the implementation of Reg FD. 74 firms provided forecasts both before and after

Reg FD. These results provide support for Hypothesis 1 that the frequency of MEF have

increased after Reg FD became effective. This is also consistent with findings of Heflin et al

(2003) and Cotter et al (2002) that MEF have increased significantly after Reg FD became

effective. Most of the increase has occurred in the bundled MEF, as would be expected given the

earlier advice noted in the business press about providing earnings guidance concurrently with the

earnings announcement. Prior studies of MEF have often included only those forecasts issued in

isolation. Panel B suggests that future studies that take this approach will tend to miss the

majority of MEF issued if the bundling practice continues.

<<< Insert Table 3 about here >>>.

Table 4 provides some descriptive information about the MEF characteristics, including

proportion of good versus bad news MEF and qualitative versus quantitative MEF, categorized

according to whether the MEF were for a quarter or a year. Panel A shows that in total there are

slightly more annual than quarterly forecasts in the sample, although when only unbundled MEF

are considered there are more quarterly than annual forecasts. There is a greater proportion of

good news MEF in the bundled forecasts, but a greater proportion of bad news MEF in the

unbundled forecasts. Again, this suggests that including only unbundled MEF in the analysis

results in a somewhat different sample than also including all MEF issued with an earnings

announcement. Quarterly MEF are more likely to be bad news, while annual MEF are more

21

likely to be good news, which corresponds to the results of Hutton et al (2003). The number of

"confirming news" MEF (those forecasts that stated they were confirming a previous MEF,

analyst forecast, or some other previous earnings forecast) comprises 30 percent of the total

sample of forecasts. Panel B shows that the majority of the MEF are quantitative (either a point,

range, or open range MEF), with a greater proportion of bad news MEF being quantitative, and

the good news MEF nearly evenly split between quantitative and qualitative MEF. The MEF

bundled with earnings announcements have a greater proportion of MEF issued in qualitative

form than do the MEF issued by themselves. The results for the sample as a whole and for the

unbundled forecasts only are somewhat different than those of prior studies such as Skinner

(1994) and Kasznik and Lev (1995), which tend to find that bad news disclosures are more likely

to be issued in qualitative form while good news is more likely to be issued in quantitative form.

<<< Insert Table 4 about here >>>

Univariate Results

Tables 5 and 6 provide the results of the univariate tests of our hypotheses 2-6. Table 5

shows the results for Hypotheses 2 and 3 as well as some additional information.7 Panel A shows

the statistics concerning the attributes of the MEF. While we have no hypotheses concerning

these attributes, panel A shows that quarterly MEF comprise a significantly greater proportion of

all MEF after Reg FD relative to the two pre-FD periods. However, there is no difference in the

proportion of MEF conveying good news versus bad news post FD relative to either of the pre FD

periods.

7 Table 5 shows the results unadjusted for seasonality and length of time period differences. When we doadjust for these differences, the inferences drawn remain qualitatively identical.

22

The next item in panel A classifies forecasts as to whether they discuss at least one of

external, internal, segment or verifiable forward looking information. While there is a significant

difference in the proportion of MEF discussing at least one type of supplemental information post

FD relative to the second pre FD period, there is no significant difference between the first FD

period and post FD. There is thus qualified support for Hypothesis 2.

The next comparison shows that there is a significantly greater proportion of quantitative

MEF issued after Reg FD relative to both of the two Pre FD periods.8 This provides support for

Hypothesis 3a. The Chi-square p-values for the comparison of the proportions of point, range,

open-ended and qualitative MEF also shows that the proportions are significantly different post

FD relative to the two pre FD periods. Examination of the MEF precision categories shows that

the biggest changes have been a reduction in the proportion of qualitative forecasts and an

increase in the number of range forecasts. This corresponds to the findings of Heflin et al (2003)

that MEF overall have become more precise since Reg FD became effective.

The last section of Table 5 panel A shows the proportions of good versus bad news MEF

categorized according the specificity of the forecast (i.e. quantitative or qualitative). The chi-

square statistic shows that the overall distribution of specificity relative to good and bad news

MEF has significantly changed post FD relative to both pre FD periods. However, inspection of

the table shows that the proportion of good news MEF in the quantitative categories has increased

rather than decreased post FD, which is the opposite of what Hypothesis 3b predicts. It appears

8 Previous studies of MEF have shown that good news tends to be provided in a more quantitative form,while bad news is often provided in a more qualitative form. Changes in the nature of the news beingconveyed because of changes in economic conditions in 2001 could certainly cause differences in precisionof the MEF that would be unrelated to Reg FD effects. However, given that the proportion of good versusbad news is relatively unchanged, while the proportion of quantitative forecasts has changed significantly,it seems unlikely that it is the change in the news being conveyed by the MEF that is leading to the changein precision. In any case, the worsening economic conditions of 2001 and beyond would have likely led toa greater proportion of qualitative forecasts had previous relationships between precision and news contentcontinued to hold. Instead, we find that it is the proportion of quantitative forecasts that has increased.

23

that for both good news and bad news MEF the specificity of forecasts has increased after Reg

FD became effective.

Panel B of Table 5 shows how the frequency of MEF, which include various types of

supplemental MEF, have changed. A smaller proportion of MEF include discussion of internal

factors since Reg FD became effective, and a marginally significant greater proportion of MEF

discuss external factors post FD relative to the Pre FD2 period. A greater proportion of MEF also

discuss verifiable forward looking information after Reg FD relative to both pre FD periods.

Both good and bad news MEFS provide more verifiable information post Reg FD, while good

news MEFS provide more supplemental information in the post FD period.

<<< Insert Table 5 about here >>>

Table 6 shows the results of univariate testing of Hypotheses 4-6. Panel A shows the results

for the full sample of MEF. The bid-ask spread preceding the MEF announcement date tends to

be slightly smaller post-FD. The absolute CAR is higher in the post FD period compared to Pre

FD2 period. The standard deviation of returns around the MEF announcement date tends to be

slightly larger in the Post-FD period, but is not significantly different. These results are

supportive of the idea that there is less information leakage as well as slightly greater diversity of

opinion and more precise public information associated with the MEF after Reg FD becomes

effective. The overall price reaction post FD is larger after Reg FD consistent with Hypothesis 5.

The evidence also supports Hypotheses 4 that there are smaller bid ask spreads before the MEF.

<<< Insert Table 6 about here >>>

Panel B of Table 6 shows the univariate results after controlling for seasonality. The results

are similar to the full sample. Overall, the univariate results provide support for Hypotheses 1, 2

and 3a that the frequency of MEF has increased post FD, that there is more supplemental

information provided with the MEF post FD, and that managers are issuing more specific MEF

24

post FD. There is evidence to support Hypotheses 4, and 5 that after Reg FD, MEF are preceded

by smaller ask-bid ask spreads and result in larger price reactions. Further testing of these

hypotheses will be done next with multivariate approaches to refine these results.

Multivariate Results

Table 7 shows descriptive statistics for the dependent and control variables used in the

multivariate analyses to help clarify the impact Reg FD has had on MEF characteristics and

market responses to MEF. Panel A shows that the average pre bid-ask spread in the window (–9

to –1) preceding the forecast is 1.25 cents. The average absolute price impact (Abs_Car) in a

three day window around MEFS is 9.18%. The standard deviation of abnormal returns (Vol_Ret)

in the window (0 to +15) post MEF is 4.06% on average. The average precision of public

information at 276 is similar to that reported by Mohanram and Sunder (2002). The average

number of disclosures by a firm during the sample period is 2.9 with the median number being 1.

On average, a firm makes more than one disclosure, with the median firm making none.

Panel B results show that the average firm in the sample has a low percentage of intangible

assets (with the median being 0). The average analyst following is 7.6. The average absolute

value of the earnings surprise variable is 4% of the previous period's earnings. The average MEF

horizon is nearly 187 days, or about six months, with the median being closer to four months.

Panel B shows the correlations among the control variables. Not surprisingly, there are high

levels of correlation particularly among those variables that are proxies for the same type of

construct, such as demand for information. Size is also closely correlated with the proxies for

demand for information. MkttoBook is significantly correlated with size and analyst following.

Variability of revenue is significantly negatively correlated with analyst following and

institutional ownership.

25

<<< Insert Table 7 about here >>>

Table 8 shows the results of the logistic regressions used to test our Hypotheses 1-3. Model 1

of Table 8 shows the coefficients from fitting the ordered logistic model for MEFNum. The

coefficient for AfterFd is positive and significant at the .05 level, supporting Hypothesis 1 that the

frequency of MEF increases after Reg FD becomes effective. The marginal impact analysis

indicates that firms with more intangible assets are likely to make fewer disclosures. Model 2

shows the results of fitting Equation (1) for the dependent variable MEFSuppl. The coefficient on

AfterFD is significant at the 5% level. The marginal impact analysis shows that firms with more

intangible assets and more volatile revenues are less likely to provide supplementary information.

Part of what is coded as supplemental information with each MEF is qualitative discussion, which

is not readily verifiable. Information which is not verifiable may not aid as much with making

forecasts interpretable or credible as does verifiable forward looking information. We therefore

re-run Model (1) but use the verifiable information subset of the supplemental information coded

for each MEF. MEFVerifiable is coded as 1 if the MEF has supplementary verifiable forward

looking information, and 0 otherwise. Results of this logistic regression are shown in Model 3.

With this subset of MEFSuppl, the coefficient on AfterFD is positive and significant at the 1%

level. This provides support for Hypothesis 2. The coefficient in model 4 (MEFPrecision) for

AfterFD is also positive and significant at the .01 level, providing support for Hypothesis 3a. The

coefficient on the interaction term AfterFD*Gnewsdum is positive and marginally significant at

the .10 level, meaning that good news MEF after Reg FD have tended to be more rather than less

specific, which is the opposite of what Hypothesis 3b predicts. The effects of Reg FD is this case

seem to be more in keeping with Hypothesis 3a, which is that all MEF, including good news

MEF, become more precise. The marginal impact analysis shows that firms with more intangible

26

assets and firms that are more vulnerable to litigation are likely to provide more quantitative

forecasts.

The models for MEFNum, MEFPrecision, and MEFVerifiable fit reasonably well, with the

percentage of observations classified correctly being between 67 and 74 percent for these three

models. Where the control variable coefficients are significant, they are generally in the right

direction, although MEFVerifiable seems to have more signs in the unexpected direction than the

other dependent variables.

<<< Insert Table 8 about here >>>

Table 9 shows the results of the multivariate OLS regressions used to test Hypotheses 4-6.

The standard errors have been adjusted using White's (1980) heteroskedastic-consistent

covariance matrix. The coefficient on AfterFD is significant in the first two regressions and has

the predicted sign in both of those regressions, providing support for Hypotheses 4 and 5. This

suggests that even after controlling for other factors that can affect market responses to MEF, the

bid-ask spreads prior to the MEF release have significantly decreased and the absolute cumulative

abnormal returns have significantly increased.9 These two results suggest that Reg FD has had its

intended effect of reducing information leakage of management earnings forecasts. The

coefficient on AfterFD for the Vol_Ret regression is not significantly different from zero after

controlling for the suggested factors that could affect returns volatility, suggesting there has been

no increase in returns volatility after Regulation FD became effective. While this does not

support the prediction made in Hypothesis 6, it is consistent with prior findings of no increase in

returns volatility around earnings announcement dates after Reg FD became effective (Bailey et

al 2003; Eleswarapu et al 2003).

9 NYSE and AMEX trading was decimalized on 19 January 2001. Our post FD period is affected bydecimalization. Incorporating the impact of decimalization does not change our results.

27

While a number of the control variables are significant, and overall the models' goodness of

fit as measured by the adjusted R-squared statistic are reasonable given the sample size, the signs

on the control variable are sometimes in the expected direction and oftentimes not. For example,

MEFSuppl is positively associated with Abs_CAR as expected, suggesting that supplementary

information provided with a forecast generally results in a bigger price reaction. However, the

sign on the Gnewsdum*MEFSuppl interaction term is negative and significant, contradicting with

Hutton et al's (2003) findings that the supplementary information provided with good news MEF

result in a stronger price reaction to the MEF overall. Unlike Hutton et al (2003), however, the

positive significant coefficient on Gnewsdum after controlling for supplemental information

suggests that in our sample good news MEF have information content even without supplemental

information.

<<< Insert Table 9 about here >>>

The model goodness of fit for Prebidask is better than for the other two market response

models. The results suggests that the bid-ask spread prior to the MEF release date is greater for

larger firms but smaller for firms with more dispersed shareholders. Firms with greater market to

book ratios have larger bid-ask spreads. More precise MEF are associated with larger bid-ask

spreads prior to the MEF release. The demand for information proxies Analyst and Disp_share

are associated with greater volatility of returns, while the information complexity proxies are

generally associated with smaller returns volatility. In summary, the multivariate results provide

support for Hypotheses 1 that Regulation FD has increased the provision of management earnings

forecasts. There is support for Hypothesis 2 that firms are more likely to provide supplemental

information with public management earnings forecasts after Reg FD. We find support for

Hypothesis 3a that firms issue more specific public management earnings forecasts post Reg FD.

There is no support for Hypothesis 3b that good news public MEF are less specific than bad news

28

MEF after Reg FD, and in general it appears that all forecasts have become more precise.

Hypotheses 4 and 5 are supported, in that MEF are associated with greater price reactions around

the MEF release and smaller bid-ask spreads prior to the MEF release after Reg FD. This

suggests that information leakage of management earnings forecasts has decreased. We find no

support for Hypothesis 6 (greater returns volatility associated with the issuance of management

earnings forecasts) after Reg FD, which is in keeping with prior research that also finds no

increased returns volatility around earnings announcements.

For the analyses presented in Tables 8 and 9, the collinearity diagnostics do not indicate

any multicollinearity problem. We repeat the multivariate analysis reported in Tables 8 and 9 for

each of the two sets of like periods.10 Our results are consistent with the earlier findings for the

full sample.

5. Conclusions

In this paper we investigate the effects of the SEC's Regulation FD on management earnings

forecasts and the associated market responses. The intent of Regulation FD was to eliminate the

differential provision of information to financial analysts and institutional investors. Since

earnings guidance is highly desired by financial analysts, we investigate whether management

earnings forecasts and the associated market responses have been affected by this regulation.

Previous research of Regulation FD has focused on its effects on earnings announcements and

analyst forecasts, but only cursory examination of changes in management earnings forecasts in

response to Regulation FD has been done to date. The effects on management forecasts is also

particularly interesting because unlike earnings announcements, management can choose to cease

10 Tables 8 and 9 report the results for the full sample. In unreported results, we repeat the tests using thesub-periods (Pre FD1 and Post FD ; pre FD2 and postFD) for the full period and the seasonality controlledsample period. Our results are qualitatively similar.

29

providing earnings forecasts in response to Regulation FD since forecast issuance is voluntary.

Managers who previously chose only to provide informal guidance, or preferred providing private

guidance to analysts must thus tradeoff pressure for analysts and other stakeholders with concerns

about proprietary costs and ability of a broader audience to interpret the information provided in

the manner desired.

Like prior studies of the effect of Regulation FD, we find a significant increase in the

issuance of management earnings forecasts, even after controlling for other factors that might

affect this behavior. We also find that firms increase their provision of supplemental information

with the management earnings forecasts compared to those issued publicly prior to Regulation

FD. While we can not determine whether this increase in supplemental information makes the

total information provided comparable to that previously issued privately to financial analysts, it

does suggest that the information environment that is equally accessible to all investors has been

improved. Management forecasts issued after Reg FD have also become more specific, with a

marked increase in range forecasts and decrease in qualitative forecasts. Contrary to our

expectations based on proprietary costs, we find that good news management forecasts issued

after Regulation FD have increased in specificity rather than decreased. The bid-ask price

spreads preceding the forecast release have decreased for those forecasts issued after Regulation

FD, and the price reaction has increased. This suggests that private provision of earnings

guidance prior to public issuance of the management forecast has been reduced. We find no

increase in returns volatility in the period immediately after the issuance of the management

forecasts, which is contrary to our expectation but in keeping with the findings of studies of the

effects of Regulation FD on earnings announcements.

Our findings suggest that Regulation FD has had its intended effect of reducing information

leakage in the context of management earnings guidance. Contrary to some of the concerns

30

expressed in the business press, our findings also indicate that the overall information provided

publicly by managers has improved both in terms of specificity and supplemental information

provided relative to that publicly available prior to the regulation being passed. However, one

limitation of our study is that we cannot compare the overall earnings guidance provided by

managers both privately and publicly prior to Regulation FD to the overall guidance provided by

managers after FD. It is thus possible that the total information environment has deteriorated,

which may be in keeping with the results of studies of the effects of Regulation FD on analyst

forecasts that suggest reduced accuracy and greater dispersion. Nonetheless, our results support

the idea that Regulation FD has achieved one of its stated goals of providing "a more level

playing field" to all investors.

31

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36

TABLE 1

Variable definitions

Panel A: Dependent Variables

Variable Name Definition and SourceMEFNum The number of MEF made by the company in a particular period. For purposes of the ordered logit, this is then mapped as the

values 1-4 as follows: 1=1 MEF during the period, 2=2 MEF during the period, 3=3 MEF and 4=4 or more MEF during theperiod. Based on the press releases retrieved from Lexis/Nexis.

MEFSuppl The sum of the number of external + internal factors + additional verifiable forward looking information listed by the companyelated to the MEF being made. For purposes of the ordered logit, this is then mapped as the values 0-3 as follows: 0=0upplemental information items, 1=1 supplemental information item, 2=2 supplemental information items, and 3 = 3 or moreupplemental information items. Based on the press releases retrieved from Lexis/Nexis.

MEFPrecision A binary variable indicating the precision of the MEF, with 1=quantitative forecast and 0=qualitative forecast.Abs_CAR The absolute value of the cumulative excess returns (over the value weighted CRSP index return) for days (-1, +1) centered

around the press release date for the MEF. From CRSP.PreBidAsk The mean bid-ask spread for days (-9,-1) centered around the MEF press release date. From CRSP.Vol_Ret The standard deviation of market adjusted returns for days (0, +15) centered around the MEF press release date.

37

TABLE 1

Variable definitions

Panel B: Explanatory VariablesAfterFD A dummy variable, set to one if the forecast was made after Reg FD became effective, and 0 otherwise.Earn_sprze The absolute value of ( (most recent period's basic earnings per share - previous equivalent period basic earnings per share) /

current basic earnings per share). Based on Compustat (Item A58/Q19)Earn_sprzedum A dummy variable with a value of 1 if the most recent period's basic earnings per share was less than the previous seasonally

equivalent period, and 0 otherwise.Analyst The maximum number of analyst estimates at any calculation date for a consensus estimate of a particular fiscal period. From

Thomson/First Call.Ln(Asset) The log of the total assets of the company (in millions of dollars), as reported by Compustat (Item #A6/Q44) for the start of the

most recent equivalent period available6.Financedum A dummy variable set equal to 1 if the company procured public financing (debt or equity) in the 12 month period following the

MEF press release date and 0 otherwise. Obtained from Thomson SDC DatabaseGnewsdum A dummy variable set equal to 1 if the press release suggested the forecast was good news, and 0 otherwise. We follow a similar

approach as Skinner (1994) to perform this classification. The coder read each press release to determine if it suggested thatearnings would be better, worse, or the same as previously expected by investors. If this was not determinable, then the forecastwas coded as 0.

Intang total intangible assets of the company for the most recent fiscal period available divided by total assets. From Compustat.

MkttoBook The ratio of market value to book value of common equity,. Calculated as the most recent period's closing stock price (ItemA24/Q14) multiplied by the shares outstanding (Item A25/Q61), divided by the book value of equity (Item A60/Q56). FromCompustat.

Horizon The number of days between the issuance of the forecast and the end of the fiscal period to which the forecast pertains.Calculated from the MEF press release.

Var_Rev The standard deviation of the preceding 16 quarters seasonally differenced log of revenue (in millions of dollars). FromCompustat.

Litdum A dummy variable with a value of 1 if the firm's 4 digit SIC code falls into one of the following ranges: 3570-3577, 3600-3674,5200-5961, 7370-7374, 8731-8734, 2803-2836.

Disp_share Log of the number of shareholders for the most recent period minus the log of the mean number of shareholders in the same sizedecile as the firm.

38

TABLE 2: Descriptive statistics on characteristics of sample firms that provide MEFversus those in the sample that do not provide MEF: October 1998 – March 2001

Panel A: Descriptive statisticsFirms that provide management

forecasts (N=240)Firms that do not provide

management forecasts (N=227)Means Median Means Median

Sales ($MM) 2434.82 320.29 467.03 98.22Assets ($MM) 2147.91 299.21 631.89 94.53Net Income ($MM) 128.29 10.95 25.73 1.11Market to book 3.57 2.04 5.03 2.01Earnings per share ($) 0.73 0.79 0.17 0.20Diluted earnings pershare ($)

0.70 0.77 0.15 0.18

Panel B : Industry categorization of firms based on 2-digit SIC classification

IndustrySIC 2-digit

codeFirms that provide MEF Firms that do not

provide MEFAgriculture 01-09 0 1Mineral Industries 10-14 8 7Construction 15-17 2 2Food Products 20 7 3Tobacco Manufacturers 21 1 0Textile Products 22-23 5 2Lumber,Furniture and 24-25 4 5

26 5 3Paper and Allied ProductsPrinting & Publishing 27 7 4Chemicals 28 15 33Petroleum Refining 29 3 1Rubber and Plastics 30 4 1Leather and Leather 31 1 2Stone, Clay, Glass Products 32 0 1Primary Metals 33 8 1Fabricated Metals 34 2 3Industrial Machinery & 35 28 8Electricals 36 23 18Transportation 37 6 4Instrumentation 38 14 23Miscellaneous 39 5 4Transportations 40-49 9 9Durable Goods 50 14 8Nondurable Goods- 51 5 1General Merchandise 53 3 2Food Stores 54 2 4Apparel 56 7 4Home Furniture 57 4 1Restaurants 58 4 4Miscellaneous Retail 59 8 5Financial Institutions 60-67 0 3Business Services 73 22 35Other services 14 25TOTAL 240 227

39

TABLE 3Frequency of management earnings forecasts issued by sample firms before and afterRegulation FD: October 1998 – March 2001 (Figures in parentheses represent rowpercentages)Panel A: Full Sample period

Number of forecastsnot accompanied byearningsannouncements(unbundled forecasts)

Number of forecastsaccompanied byearningsannouncements(bundled forecasts)

Totalforecasts

PreFD 1October 23, 1998 – October 22, 1999

86(31.97%)

183(68.03%)

269

PreFD 2October 23, 1999 – October 22, 2000

112(28.43%)

282(71.57%)

394

Post FDOctober 23, 2000 – March 31, 2001

87(22.72%)

296(77.28%)

383

Panel B : Controlling for SeasonalityNumber of unbundledforecasts

Number of bundledforecasts

Totalforecasts

PreFD 1 33(36.26%)

58(63.74%)

91

PreFD 2 41(28.67%)

102(71.33%)

143

PostFD 87(22.72%)

296(77.28%)

383

Chi-square tests for equality ofproportions(p-values)PreFD 1 vs PostFD .0001 .0001 .0001

PreFD 2 vs PostFD .0001 .0001 .0001Panel C: Forecasting patterns for 467 sample firms Number of firms - Seasonality

Controlled sample

Firms that provided forecasts during sample period 202

Firms that did not provide forecasts during sample period 227

Firms that provided forecasts before Reg FD 119

Firms that provided forecasts after Reg FD 157

Firms that provided forecasts before and after Reg FD 74

Firms that provided forecasts before Reg FD but not after Reg FD 45

Firms that provided forecasts after Reg FD but not before Reg FD 83

40

TABLE 4Frequency analysis of bundled and unbundled management forecasts: October 1998 –March 2001 (Column percentages are shown in parentheses)

Panel A: Forecasts classified by nature of news and periodicity of forecast

Unbundled Forecasts

Annual and Quarterly Annual Quarterly

Good news 87(30.53%)

53(41.73%)

34(21.52%)

Bad News 124(43.51%)

33(25.98%)

91(57.59%)

Confirming News 74(25.96%)

41(32.29%)

33(20.89%)

Total 285 127 158

Bundled Forecasts

Annual and Quarterly Annual Quarterly

Good news 378(49.67%)

230(51.45%)

148(47.13%)

Bad News 140(18.40%)

53(11.86%)

87(27.71%)

Confirming News 243(31.93%)

164(36.69%)

79(25.16%)

Total 761 447 314

All Forecasts

Annual and Quarterly Annual Quarterly

Good news 465(44.46%)

283(49.30%)

182(38.56%)

Bad News 264(25.24%)

86(14.98%)

178(37.71%)

Confirming News 317(30.30%)

205(35.72%)

112(23.73%)

Total 1046 574 472

41

TABLE 4 continued

Panel B: Forecasts classified by nature of news and type of forecast

Unbundled ForecastsGood news Bad news Confirming News

Quantitative forecast 64(73.56%)

87(70.16%)

63(85.14%)

Qualitative forecast

23(26.44)

37(29.84%)

11(14.86%)

Total 87 124 74

Bundled Forecasts

Good news Bad news Confirming News

Quantitative forecast 169(44.71%)

85(60.71%)

169(69.55%)

Qualitative forecast 209(55.29%)

55(39.29%)

74(30.45%)

Total 378 140 243

All Forecasts

Good news Bad news Confirming News

Quantitative forecast 233(50.11%)

172(65.15%)

232(73.19%)

Qualitative forecast 232(49.89%)

92(34.85%)

85(26.81%)

Total 465 264 317

42

TABLE 5Overall management earnings forecast characteristics

Panel A: Frequency analysis of management forecast features before and after Regulation FD,October 1998 – March 2001 (Column percentages are denoted in parentheses.)

Chi-square test p-valuesPreFD 1N=269

PreFD 2N=394

PostFDN=383 PreFD 1

vs.PreFD 2

PreFD 1vs.PostFD

PreFD 2vs.PostFD

Periodicity of forecast

Annual 163 (60.59) 232 (58.88) 179 (46.74)Quarterly 106(39.41) 162(41.12) 204(53.26)

0.6592 0.0005 0.0007

Nature of forecast

Good news 126 (46.84) 176 (44.67) 163 (42.56)Bad news 74(27.51) 95(24.11) 95(24.80)Confirming news 69(25.65) 123(31.22) 125(32.64)

0.2725 0.1579 0.8357

Discussion of some type of supplemental information (internal factors or external factors or segment orverifiable information)

0 38(14.13) 69 (17.51) 48(12.53)1 82(30.48) 128 (32.49) 97(25.33)2 79(29.37) 99(25.13) 125(32.64)3 70(26.02) 98(24.87) 113(29.50)

0.4760 0.3829 0.0075

Type of forecast

Quantitative (Quant) 129 (47.96) 221 (56.09) 287 (74.93)Qualitative (Qual) 140(52.04) 173(43.91) 96(25.07)

0.0393 0.0001 0.0001

Precision of forecast

Point estimate 32(11.90) 84(21.32) 84(21.93)Range 81 (30.11) 104 (26.40) 180 (47.00)Open-ended range 16(5.95) 33(8.38) 23(6.01)Qualitative 140(52.04) 173(43.91) 96(25.07)

0.0061 0.0001 0.0001

Nature of news and type of forecast

Gnews and Quant. 41(32.54) 82(46.59) 110(67.48) .0143 .0001 .0001Gnews and Qual. 85(67.46) 94(53.41) 53(32.52)

Bnews and Quant. 44 (59.46) 61 (64.21) 67 (70.53) .5276 .1327 .3532Bnews and Qual. 30(40.54) 34(35.79) 28(29.47)

43

TABLE 5Overall management earnings forecast characteristics

Panel B : Analysis of supplementary information provided with management forecasts before andafter Regulation FD, October 1998 – March 2001 (Column percentages are denoted inparentheses.)

Chi-square test p-valuesPreFD 1N=269

PreFD 2N=394

PostFDN=383 PreFD 1

vs.PreFD 2

PreFD 1vs.PostFD

PreFD 2vs.PostFD

Discussion of internal factors

Yes 124 (46.10) 165 (41.88) 121 (31.59)No 145(53.90) 229(58.12) 262(68.41)

0.2948 0.0002 0.0027

Discussion of external factors

Yes 97 (36.06) 135 (34.26) 153 (39.95)No 172(63.94) 259(65.74) 230(60.05)

0.6341 0.3148 0.1010

Discussion of segment information

Yes 101 (37.55) 144 (36.55) 137 (35.77)No 168(62.45) 250(63.45) 246(64.23)

0.7937 0.6428 0.8215

Discussion of verifiable forward looking information

Yes 79 (29.37) 110 (27.92) 193 (50.39)No 190(70.63) 284(72.08) 190(49.61)

0.6848 0.0001 0.0001

Nature of news and provision of verifiable forward looking information

Gnews and Veri. info 34(26.98) 50(28.49) 86(52.76) .7852 .0001 .0001Gnews and No Veri.Info

92(73.07) 126(71.59) 77(47.24)

Bnews and Veri. info 19 (25.68) 22 (23.16) 46 (48.42) .7048 .0026 .0003Bnews andNo Veri. info

55(74.32) 73(76.84) 49(51.58)

Nature of news and provision of supplemental information (internal factors or external factors orsegment or verifiable information)

Gnews and Suppl. info 106(84.13) 148(84.09) 147(90.18) .9933 .1220 .0953Gnews and No Suppl.info

20(15.87) 28(15.91) 16(9.82)

Bnews and Suppl. info 70(94.59) 89(93.68) 91(95.79) .8035 .7167 .5158Bnews and No Suppl.info

4(5.41) 6(6.32) 4(4.21)

44

TABLE 6Univariate analysis of the market effects of management forecasts before and after REGFD (October 1998 – March 2001)

Panel A: Full SamplePreFD 1N=269

PreFD 2N=394

PostFDN=383

PreFD 1vs.

PreFD 2p-value

PreFD 1vs.

PostFDp-value

PreFD 2vs.

PostFDp-value

AbsPreBidAsk 1.1404*** 1.4288*** 1.0558***

.0062 .1415 .0002

Abs_CAR 0.0953*** 0.0867*** 0.0983***

.1498 .3690 .0791

Vol_Ret 0.0476*** 0.0471*** 0.0491***

.4499 .3977 .2949

Panel B : Sample after controlling for seasonalityPreFD 1

N=91PreFD 2N=143

PostFDN=383

PreFD 1vs.PreFD 2p-value

PreFD 1vs.

PostFDp-value

PreFD 2vs.

PostFDp-value

AbsPreBidAsk 0.9498** 1.4529*** 1.0558*** .0029 .1176 .0100

Abs_CAR 0.1105***

0.0793 0.0983*** .0166 .1919 .0277

Vol_Ret 0.0547***

0.0552*** 0.0491*** .4771 .2599 .1203

Please see Table 1 for definitions of the variables. ***, **, and * denote a significant differencefrom 0 at the 1%, 5% and 10% levels respectively. P-values for period comparisons are all forone-tailed tests.

45

TABLE 7: Descriptive statistics for dependent and independent variables

Panel A: Descriptive statistics for the dependent variables

VariableMean Median Minimum Maximum Standard

deviation

PreBidAsk 1.2452 .8973 .0208 14.7292 1.3983

Abs_CAR .0918 .0553 .0000 .7893 .1088

Vol_Ret .0406 .0329 .0082 .2692 .0269

MEFNum 2.9027 3.0000 1.0000 4.0000 1.1766

MEFSuppl 1.4723 1.0000 0.0000 3.0000 0.9054

Panel B : Descriptive statistics for the control variablesLn (Asset) 6.75 6.57 1.09 11.28 1.94

Intang 0.06 0.00 0.00 0.78 0.11

Mkttobook 3.90 2.11 0.26 24.74 4.56

Analyst 8.10 6.00 0.00 32.00 7.84

Disp_share 0.41 0.19 -2.02 8.55 1.18

Horizon 186.85 128.00 2.00 1077.00 183.99

Earn_sprze 0.04 0.02 0.00 0.30 0.06

Var_Rev 0.21 0.16 0.01 3.91 0.24

Please see Table 1 for definitions of the variables.

46

TABLE 7 continuedPanel C: Correlation matrix of firm characteristics: Pearson (Spearman) correlation coefficients are above (below) the diagonal.

Ln (Asset) Intang Mkttobook Analyst Disp_share

Mtrvol Litdum Earn_sprze Earn_Sprzedum

Var_Rev Financedum

Ln(Asset) 1.00 .11*** .33*** .70*** .38*** -.04 .11*** -.21*** .02 -.31*** .22***

Intang .12*** 1.00 -.08** .01 .02 -.14*** -.08** .08** -.06* -.04 -.01

Mkttobook .33*** .00 1.00 .48*** .22*** .15*** -.05 .21*** .07** -.08** .19***

Analyst .71*** .04 .41*** 1.00 .38*** .13*** .02 .02 .02 -.18*** .20***

Disp_share .35*** .10*** .12*** .28*** 1.00 -.11*** .04 .05 .08*** -.03 .15***

Mtrvol .09** -.12*** .29*** .24*** -.01 1.00 .01 .10** -.09** .17*** .03

Litdum .08** -.05 -.06* .02 .04 .02 1.00 -.10*** -.03 .00 .02

Earn_sprze -.12*** .19*** .10*** .01 .08** .15*** -.05 1.00 .10*** .11** .02

Earn_sprzedum .01 -.07** -.003 .00 .06* -.07** -.03 .06* 1.00 .00 -.09***

Var_Rev -.41*** -.11*** -.16*** -.28*** -.09*** .10*** -.01 .05 -.02 1.00 -.003

Financedum .22*** .06* .16*** .13*** .14*** .05 .02 .09*** -.09** .02 1.00Please see Table 1 for definitions of the variables. ***, **, and * denote significance at the 1%, 5% and 10% levels respectively.

47

TABLE 8Ordered Logistic regression of determinants of management forecast characteristics before and after REG FD :October 1998 – March 2001. Significance levels (in parentheses) are based on one-sided tests for the variables AfterFd and AfterFD*Gnewsdum andtwo-sided tests for all other variables.

(1) (2) (3) (4)Variable MEFNum Marginal

ImpactMEFSuppl Marginal

ImpactMEFVerifiable Marginal

ImpactMEFPrecision Marginal

ImpactIntercept -1.976

(.303).049

(.949)1.495*(.065)

-1.868**(.023)

Intercept 2 -1.162(.544)

1.875***(.008)

Intercept 3 .100(.958)

3.771***(.0001)

AfterFD .800**(.011)

.133 .262(.020)

.063 1.093***(.0001)

.250 .999***(.0001)

.220

Gnewsdum -.057(.644)

-.013 .025(.864)

.006 -.960***(.0001)

-.223

AfterFD*Gnewsdum

.396(.092)

.089

Analyst .043(.178)

.008 .025(.041)

.006 .089***(.0001)

.020 .031**(.041)

.007

Disp_share .115(.390)

.021 .008(.888)

.002 .261***(.0001)

.058 .143**(.043)

.033

Intang -2.184(.210)

-.407 -1.048(.068)

-.254 .960(.152)

.213 1.711**(.021)

.399

MkttoBook -.009(.860)

-.002 -.017(.269)

-.004 .018(.347)

.004 .068***(.002)

.016

48

TABLE 8 - continued(1) (2) (3) (4)

Variable MEFNum MarginalImpact

MEFSuppl MarginalImpact

MEFVerifiable MarginalImpact

MEFPrecision Marginal Impact

Var_Rev -.378(.455)

-.071 -1.211***(.001)

-.294 -.095(.761)

-.021 -.354(.290)

-.083

Financedum -.174(.437)

.000 -.837***(.008)

-.160 -.240(.431)

-.057

Litdum -1.744(.244)

.000 -.708(.298)

.000 -.622(.419)

-.149 1.426*(.066)

.340

MEFPrecision .047(.715)

.001

Ln(Asset) .202*(.052)

.038 -.147***(.005)

-.036 -.387***(.0001)

-.086 .033(.583)

.008

Earn_Sprze -.561(.626)

-.136 -2.798**(.046)

-.622 .540(.705)

.126

Earn_sprzedum -.388**(.001)

.000 -.259*(.075)

-.057 .054(.708)

.013

Horizon 001(.184)

.000

-2 loglikelihood

443.4 2536.17 1175.30 1169.91

ModelChisquare

31.22 44.40 124.78 173.96

p-value .0001 .0001 .0001 .0001

Percentcorrectlyclassified

67.7% 59.3% 71.0% 74.1%

N 185 1002 1002 1002

49

TABLE 8 - continued

Please see Table 1 for definitions of the variables. Dependent variables are categorized in descending order. Columns 1 and 3 are the results of orderedlogit analysis. A negative logit coefficient for column 1 is associated with fewer forecasts for MEFNum. A negative logit coefficient for column 3 isassociated with less supplemental information for MEFSuppl. In column 2, MEFPrecision is a binary variable, with a value of 1 for quantitative MEFand 0 otherwise. In column 4, the dependent variable MEFVerifiable is coded as 1 if the MEF contained verifiable forward looking information, and 0otherwise.In model 1, the marginal impact represents the change in the probability of making 4 or more disclosures during the sample period given a unit changein the independent variable if it is continuous. In model 3, the marginal impact represents the change in the probability of the firm providing 3or morecategories of supplementary information in a forecast given a unit change in the independent variable if it is continuous. In each of the models, forindicator variables, the marginal impact is the difference in probability when the variable equals one versus when it equals zero, evaluated at the mean ofthe other variables .

50

TABLE 9Regression analysis of the market effects of management forecasts before and after REGFD (October 1998 – March 2001) (t-statistics in parentheses).

Variable(1)

PreBidAsk(2)

Abs_CAR(3)

Vol_RetIntercept -.715***

(2.72).194

(7.60).074***(12.53)

AfterFD -.176***(2.38)

.011*(1.37)

.001(.43)

Gnewsdum .484***(3.15)

.016(1.32)

.005(1.62)

Analyst -.005(.36)

.001(1.64)

.0004**(2.45)

Disp_share -.149***(4.71)

.006(1.22)

.001*(1.71)

Intang -1.029***(3.89)

.003(.10)

-.010(1.28)

MkttoBook .110***(5.79)

.001(1.56)

-.0001(.52)

Var_Rev .218(1.28)

-.022*(1.66)

.001(.41)

MEFPrecision .268***(3.31)

.006(.78)

.002(1.14)

MEFSuppl .053(1.21)

.013***(3.26)

.001(1.31)

Ln(Asset) .212***(4.90)

-.021***(5.87)

-.006**(7.57)

Earn_Sprze -1.440**(2.57)

-.036(.50)

.038***(2.63)

Earn_sprzedum -.235***(3.13)

.014**(1.99)

.0004(.31)

Gnewsdum*MEF Suppl

-.068(.91)

-.014**(2.13)

-.002(1.55)

Adjusted R-square

0.2779 0.0899 0.1560

N 1002 1002 1002Please see Table 1 for definitions of the variables. Standard errors have been adjusted forheteroskedasticity using White’s (1980) heteroskedastic-consistent covariance matrix. ***, **, and *denote significance at the 1%, 5% and 10% levels respectively. Significance levels are based on one-sidedtests for the variables and two-sided tests for all other variables.