Regulation Fair Disclosure and Earnings Information: Market, Analyst, and Corporate Responses WARREN BAILEY, HAITAO LI, CONNIE X. MAO, and RUI ZHONG n ABSTRACT With the adoption of Regulation Fair Disclosure (Reg FD), market behavior around earnings releases displays no signi¢cant change in return volatility (after controlling for decimalization of stock trading) but signi¢cant in- creases in trading volume due to di¡erence in opinion. Analyst forecast dis- persion increases, and increases in other measures of disagreement and di¡erence of opinion suggest greater di⁄culty in forming forecasts beyond the current quarter. Corporations increase the quantity of voluntary disclo- sures, but only for current quarter earnings.Thus, Reg FD seems to increase the quantity of information available to the public while imposing greater de- mands on investment professionals. GOVERNMENTS REGULATE SECURITIES MARKETS with a variety of goals in mind. A £ow of information that is accurate, e⁄cient, and fair contributes to a well-function- ing capital market that satis¢es the needs of all users and enhances economic growth and stability. A continuing concern of regulators and investors is‘‘selec- tive disclosure,’’ the practice of companies disclosing important information to certain securities analysts and institutions. Former U.S. Securities and Exchange Commission (SEC) Chairman Arthur Levitt (U.S. Securities and Exchange Commission (2000a), p. 1) speaks of information as ‘‘the lifeblood of strong, vibrant markets’’and warns that ‘‘when that information is used to pro¢t at the expense of the investing public, when that information comes by way of favored access rather than by acumen, insight, or diligence, . . . we risk nothing less than the public’s faith and con¢dence in America’s capital market.’’ As part of the former chairman’s drive to improve transparency and fairness in U.S. stock markets, the SEC approved Regulation Fair Disclosure (Reg FD) on August 10, 2000. Reg FD is intended to level the playing ¢eld by reducing THE JOURNAL OF FINANCE VOL. LVIII, NO. 6 DECEMBER 2003 n Bailey and Li are from Cornell University, Mao is from Temple University, and Zhong is from Fordham University. We are grateful to First Call/Thompson Financial and I/B/E/S for providing us with much of the data used in our study.We thank the editor, Rick Green, and the anonymous referee for many useful comments.We thank Haluk Unal, Rex Thompson, and Kumar Venkataraman for helpful discussions, Charles M. C. Lee for help in obtaining the First/Call database, and Mancang Dong for technical support. Previous versions of this paper circulated under the title ‘‘Regulation FD and Market Behavior around Earnings Announce- ments: Is the CureWorse than the Disease?’’Any errors are our own. 2487
Transcript
Regulation Fair Disclosure and EarningsInformation: Market, Analyst,
and Corporate Responses
WARREN BAILEY, HAITAO LI, CONNIE X. MAO, and RUI ZHONGn
ABSTRACT
With the adoption of Regulation Fair Disclosure (Reg FD), market behavioraround earnings releases displays no signi¢cant change in return volatility(after controlling for decimalization of stock trading) but signi¢cant in-creases in trading volume due to di¡erence in opinion. Analyst forecast dis-persion increases, and increases in other measures of disagreement anddi¡erence of opinion suggest greater di⁄culty in forming forecasts beyondthe current quarter. Corporations increase the quantity of voluntary disclo-sures, but only for current quarter earnings.Thus, Reg FD seems to increasethe quantity of information available to the public while imposing greater de-mands on investment professionals.
GOVERNMENTS REGULATE SECURITIES MARKETS with a variety of goals in mind. A £owof information that is accurate, e⁄cient, and fair contributes to a well-function-ing capital market that satis¢es the needs of all users and enhances economicgrowth and stability. A continuing concern of regulators and investors is ‘‘selec-tive disclosure,’’ the practice of companies disclosing important informationto certain securities analysts and institutions. Former U.S. Securities andExchange Commission (SEC) Chairman Arthur Levitt (U.S. Securities andExchange Commission (2000a), p. 1) speaks of information as ‘‘the lifeblood ofstrong, vibrant markets’’and warns that ‘‘when that information is used to pro¢tat the expense of the investing public, when that information comes by way offavored access rather than by acumen, insight, or diligence, . . . we risk nothingless than the public’s faith and con¢dence in America’s capital market.’’
As part of the former chairman’s drive to improve transparency and fairnessin U.S. stock markets, the SEC approved Regulation Fair Disclosure (Reg FD)on August 10, 2000. Reg FD is intended to level the playing ¢eld by reducing
THE JOURNAL OF FINANCE � VOL. LVIII, NO. 6 � DECEMBER 2003
nBailey and Li are from Cornell University, Mao is from Temple University, and Zhong isfrom Fordham University.We are grateful to First Call/Thompson Financial and I/B/E/S forproviding us with much of the data used in our study.We thank the editor, Rick Green, andthe anonymous referee for many useful comments.We thank Haluk Unal, Rex Thompson, andKumar Venkataraman for helpful discussions, Charles M. C. Lee for help in obtaining theFirst/Call database, and Mancang Dong for technical support. Previous versions of this papercirculated under the title ‘‘Regulation FD and Market Behavior around Earnings Announce-ments: Is the CureWorse than the Disease?’’Any errors are our own.
2487
information disparities between individual and institutional market partici-pants.1 Reg FD prohibits selective disclosure of material information and re-quires broad, nonexclusionary disclosure of such information. For example, anadvance warning about earnings telephoned to a security analyst must also beimmediately released to the public with a press release, open conference call, orother public communication.
Aside from increasing public con¢dence in U.S. ¢nancial markets, proponentsbelieve that Reg FD will improve the £ow of information to ¢nancial markets.Corporate managers can no longer treat material information as a commodityto gain favor with analysts, who in turn feel pressure to issue favorable reportsto maintain access to those corporations. Analysts must conduct more indepen-dent research rather than depending on data ‘‘spoon fed’’ by corporate manage-ment. Recipients of selective disclosures will no longer be able to trade on whatis, in e¡ect, private information that can reduce liquidity and increase pricevolatility (Admati and P£eiderer (1988)). The regulation may also contribute toalleviating incentive problems when analyst and investment banking activitiesoccur under the same roof (Teo (2000)).
Skeptics counter that, despite its good intentions, Reg FD will chill corporatedisclosure and trigger an ‘‘information brownout.’’ If the materiality standard isvague, companies will restrict discussions with analysts and institutional inves-tors to avoid potential SEC legal action. Communication will be reduced to‘‘sound bites,’’ ‘‘boilerplate’’ disclosures, or large amounts of nonmaterial andraw information of little value to analysts and the public at large.2 This, in turn,impedes the process (Grossman and Stiglitz (1980)) by which informed tradingincorporates information into prices rapidly and e¡ectively, thereby enhancingmarket e⁄ciency.3 By hampering the ability of analysts to obtain and interpretnew information and, thus, contribute to the formation of market consensus, aregulation intended to equalize the quantity and quality of information availableto ordinary investors may instead leave themworse o¡.
Are concerns about Reg FD legitimate or merely an excuse for investment pro-fessionals to protect their undeserved privileges? Does this well-intentioned reg-ulation have unintended and undesirable consequences? Givenwhat is at stake inthis debate, understanding the impact of Reg FD on ¢nancial markets is an im-portant empirical question with profound practical implications.
In this paper, we assess the impact of Reg FD on stock market responses toearnings releases, on the earnings forecasts produced by analysts, and on the
1See, for example, U.S. Securities and Exchange Commission (2000b).2A recent survey indicates 72% of analysts interviewed by the Security Industry Associa-
tion feel that information communicated by issuers to the public is of lower quality since im-plementation of the regulation. See http://www.sia.com/testimony/html/kaswell5-17.html.
3 Stuart Kaswell, the general counsel of the SIA, states ‘‘We believe that communicationsbetween an issuer and individual analysts or a small group of analysts contribute to the over-all mix of information in the market place, greater accuracy of market prices, less volatilityand, in general greater e⁄ciency . . . We are concerned the proposal will end up restrictingthe £ow of information rather than encouraging it by imposing detailed rules on companies,investors and analysts.’’ See http://www.sia.com/testimony/html/kaswell5-17.html.
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extent to which corporations voluntarily disclose information. A seemingly sig-ni¢cant decline in returnvolatility at times of earnings announcements after theimplementation of Reg FD is due to decimalization of stock trading rather thanthe adoption of Reg FD.There are signi¢cant increases in trading volume due todi¡erential informed judgment or di¡erence in opinion after controlling forother factors.While the accuracyof analyst forecasts of quarterly earnings seemsunchanged, forecast dispersion increases signi¢cantly. Furthermore, increasesin other measures of disagreement and di¡erences of opinion among analystssuggest that forecasting earnings beyond the current quarter has become moredi⁄cult after the imposition of Reg FD. Finally, there is unambiguous evidence ofincreased voluntary corporate disclosure, though only for forthcoming quarterlyearnings.
In some respects, Reg FD serves its intended purpose: Legal remediesare now in place to punish privileged communications, and our evidence showsthat listed companies o¡er a greater quantity of voluntary disclosure to the pub-lic. At the same time, Reg FD imposes costs on market participants:We also ¢ndthat disagreement among traders and analysts has increased since theregulation was adopted. This may validate complaints about Reg FD fromthe investment industry, or may predict that more e¡ort and struggle onthe part of analysts and other investment professionals will enhance markete⁄ciency.
Our paper is part of a growing body of research inspired by the SEC’s approvalof Reg FD in the summer of 2000. Shane, Sonderstrom, andYoon (2001), Eleswar-apu, Thompson, and Venkataraman (2002), Gadarowski and Sinha (2002), andHe£in, Subrahmanyman and Zhang (2003) report that returnvolatility decreasessigni¢cantly after Reg FD. In contrast, we attribute this decrease to decimaliza-tion rather than Reg FD itself. Eleswarapu et al. use intraday data to measuretrading costs and ¢nd that information asymmetry has declined with Reg FD,particularly for small or illiquid stocks. Straser (2002) reports a decline in theproportion of informed traders but an increase in proxies for information asym-metryand private information.Topaloglu (2002) ¢nds that institutions tradehigh-er amounts during the event period, rather than pre-announcement, after theimposition of Reg FD, while Gintschel and Markov (2002) ¢nd a reduced impactof analyst forecasts, particularly from ‘‘high reputation’’ brokerages. Thesepapers indicate that Reg FD induces substantial changes in the information en-vironment, as is also suggested by our results on disagreement. In studies of ana-lyst forecasts, Shane et al. and He£in et al. ¢nd little post Reg FD change in theaccuracy or dispersion of quarterly analyst forecasts, whileAgrawal and Chadha(2002) report that both have increased, especially for small, information-poor, orunpro¢table ¢rms in certain industries. Topaloglu and Irani and Karamanou(2002) report increased forecast dispersion after Reg FD, Monhanram and Sun-der (2001) ¢nd that increases in forecast errors and dispersion after Reg FD donot extend to ‘‘all-star’’ analysts, and Zitzewitz (2002) indicates that analystsrespond more to public releases. We ¢nd no increase in forecast errors, butsigni¢cant increases in forecast dispersion and other indicators of disagree-ment. Straser and He£in et al. report increased voluntary disclosure after
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Reg FD, as we report here as well.4 Our paper contributes to unifyingand interpreting the diversity of empirical facts presented by this growingliterature.
The balance of our paper is organized as follows. Section I describes our dataand methodology. Section II presents event-study evidence, while Section III ex-amines the impact on analyst forecasts and corporate voluntary disclosure. Sec-tion IV is a summary, conclusion, and agenda for future research.
I. Experimental Design
A. Comparison Periods and Data
To study the impact of Reg FD, we compare market behavior around earningsreleases, analyst forecasts, and corporate voluntary disclosures before and afterimplementation of Reg FD. Given that Reg FD became e¡ective on October 23,2000, we de¢ne the fourth ¢scal quarter of 2000 (IV 2000) and the ¢rst and second¢scal quarters of 2001 (I 2001and II 2001) as our ‘‘post Reg FD’’quarters.5 We alsode¢ne two kinds of ‘‘pre Reg FD’’ comparison quarters. First, we use the same¢scal quarter in the previous year to capture any quarter-speci¢c e¡ects. Thus,post Reg FD IV 2000 is compared to pre Reg FD IV 1999, I 2001 is compared toI 2000, and II 2001 is compared to II 2000. Second, we use the last quarter beforeadoption of Reg FD, III 2000, to compare to the post Reg FD quarters, sacri¢cinga matching-quarter comparison, but minimizing the time di¡erence between thepost Reg FD and control quarters.
The First Call database is our source for quarterly earnings announcements,analyst forecasts, and corporate voluntary disclosures. The daily stock price,adjusted return, trading volume, and shares outstanding are obtained from theCenter for Research in Securities Prices (CRSP) of the University of Chicago.Our sample consists of all December year-end ¢rms with quarterly actual andforecast earnings per share and earnings announcement date in the First Calldatabase, and returns and trading volumes from CRSP for the post Reg FD quar-ter of interest and its comparison quarter.6
4 Bushee, Matsumoto, and Miller (2002), Sunder (2002), and Irani (2003) take an imaginativeapproach to the impact of Reg FD on voluntary disclosure by studying conference calls beforeversus after Reg FD.
5We have only three quarters since adoption of Reg FD because our First Call databaseextends through August 2001 only. Reg FD is e¡ective as of October 23, 2000, that is, aboutthree weeks into the fourth quarter of 2000. One concern is that companies disclose informa-tion regarding fourth quarter of 2000 earnings to analysts during the ¢rst 3 weeks beforeReg FD is enforced, thus partially contaminating IV 2000 with disclosure not controlled byReg FD. Contamination may occur for other quarters as well: Companies can guide analystswith information regarding earnings for several forthcoming quarters. However, such contam-ination should decay over time after October 23, 2000. Having three post Reg FD quarters inour study can help us assess the relative strength of the impact of Reg FD over time.
6We eliminate all ¢rms with a ‘‘DDC’’ code indicated in the First Call database. The DDCcode denotes some form of discontinuity in the EPS series arising from events such asaccounting change and mergers and acquisitions.
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B. Methodology
B.1. Matched-pairs Comparison
We compare changes in a variety of market and analyst variables across pairsof pre Reg FD and post Reg FD quarters as described previously.We follow stan-dard event-study practice to generate return and volume residuals. De¢ning theearnings announcement date as day 0, abnormal stock returns are generatedusing one-factor market model residuals estimated from day � 200 to day � 11.The CRSP value-weighted index return is used as a proxy for market return.Abnormal return volatility is the absolute value of daily abnormal returns,summed over awindow spanning the earnings announcement. Abnormal tradingvolumes are di¡erences between trading volume and themean of daily volume forthat stock over the pre-announcement window (� 200, � 11), normalized by themean volume, then summed over a window spanning the earnings announce-ments.T-tests and sign tests assess the signi¢cance of themean and medianwith-in-¢rm change across pairs of pre and post Reg FD comparison quarters.
We also compare di¡erent aspects of analyst forecasts before and after the im-position of Reg FD. The absolute consensus forecast error is the absolute value ofthe di¡erence between a ¢rm’s reported earnings for a quarter and the mean ofmost recent analyst forecasts for the quarter.The absolute time-series forecast erroris the absolute value of the seasonal change in a ¢rm’s quarterly earnings. Theanalyst information advantage is the di¡erence between a particular absolute con-sensus forecast error and the corresponding absolute time-series forecast error.Intuitively, this measures the ‘‘value added’’ by the analyst (He£in et al. (2003)).Analyst forecast dispersion is the standard deviation of individual analyst mostrecent forecasts of a ¢rm’s quarterly earnings. All forecast variables are scaledby the stock price at the end of the pre Reg FD comparison quarter.
B.2. Cross-sectional Regressions
To further assess the impact of Reg FD on return volatility and trading volumeafter controlling for several ¢rm characteristics, we estimate multivariateregressions. For return volatility, the dependent variable is the absolute value ofthe abnormal return (computed from the market model) cumulated over a 3-dayperiod (days � 1, 0, and 1). For trading volume, the dependent variable is cumula-tive mean-adjusted trading volume over the same 3-day period. We constructexplanatory variables for the cross-sectional regressions following Yoon andStarks (1995), Atiase and Bamber (1994), and others. Absolute consensus forecasterror is de¢ned above and serves in regressions to explain return volatility. Firmsize is the market value of common shares outstanding at the end of the quarter.It can re£ect for the amount of information available about the ¢rm,7 market li-quidity, average precision of investors’ private predisclosure information, orother basic cross-sectional di¡erences in information environment across ¢rms.Pre-announcement information asymmetry is proxied with the dispersion in
7The idea is that larger ¢rms tend to draw more press and analyst coverage.
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analyst earnings forecasts as de¢ned above. Additionally, the event-period abnor-mal return is used as an explanatory variable in regressions to explain abnormalvolume. Previous authors have documented a signi¢cant positive relation be-tween trading volume and the magnitude of returns at earnings announcements(Karpo¡ (1987) and Atiase and Bamber) and, as discussed below, this relationmay be uniquely useful in identifying the nature of the market reaction to earn-ings. Finally, speci¢cations for both abnormal return and abnormal volume in-clude dummy variables to distinguish events occurring before versus after theimposition of Reg FD.
Table I summarizes characteristics of our sample ¢rms and principal variablesthat we examine in our event-study and cross-sectional regression tests. Acrossthe seven quarters that we study, the number of ¢rms ranges from 1,683 to 2,144,while average ¢rm size ranges from $897 million to $1.178 billion. Our sampleappears comprehensive in that it includes both small and large ¢rms, rather thanonly including relatively large ¢rms as in many studies (e.g., Barron and Stuerke(1998)).The median number of analysts per ¢rm is ¢ve, which appears low. Againthis may be due to the inclusion of many small ¢rms in our sample. The averageabsolute earnings surprise and analyst forecast dispersion are similar across
Table IMedian Characteristics of Sample Firms
This table reports the median values of ¢rm characteristics.The samples consist of all Decem-ber year-end ¢rms with quarterly actual and forecast earnings per share, earnings announce-ment dates in the First Call database, and returns and trading volume from CRSP for each pairof pre and post RegFDquarter. Size is the ¢rm’s market capitalization, analysts is the number ofanalysts following a particular ¢rm, the absolute consensus forecast error is the absolute valueof the di¡erence between actual earnings and consensus forecasts scaled by stock price at theend of the pre Reg FD quarter, and forecast dispersion is the standard deviation of the mostrecent individual forecasts. Abnormal stock returns are computed based on one-factor marketmodel residuals estimated fromday � 200 to day � 11with CRSP value-weighted index returns.Abnormal return volatility is the absolute value of daily abnormal returns, summed over thewindow indicated. Abnormal trading volumes are generated as the di¡erences between tradingvolume and the mean of daily volume for that stock over the pre-announcement window (� 200,� 11) normalized by the mean volume, then summing over the period indicated.
quarters. There appears to be considerable variation in abnormal market beha-vior across quarters, particularly for volume.
II. Reg FD andMarket Responses to Earnings Releases
A. Univariate Behavior of Abnormal ReturnVolatility andTradingVolume
We begin by examining how abnormal return volatility and trading volumearound earnings releases di¡er before versus after the adoption of Reg FD inOctober 2000.Table II reports univariate summary statistics for six pairs of com-parison quarters. Abnormal stock returns are computed based on one-factormarket model residuals estimated from day � 200 to day � 11 with respect toCRSP value-weighted index returns. Abnormal return volatility is the absolutevalue of daily abnormal returns, summed over the (� 1, þ1) window. Abnormaltrading volume is the di¡erence between trading volume and the mean of dailyvolume for that stock over the pre-announcement window (� 200, � 11) normal-ized by the mean volume, then summing over the same window.8 Results com-puted over windows of (� 1, 0), (� 2, þ 2), and (� 1, þ 5) are similar to those for(� 1, þ1) and are available on request.
Panel A of Table II presents univariate summary statistics on abnormal returnvolatilityaround earnings releases.The results suggest a strong and uniform pat-tern of decreases in announcement period return volatility after the adoption ofReg FD. Return volatility in the post Reg FD quarters is signi¢cantly lower thanin their pre Reg FD comparison quarters. All mean and median di¡erences arestatistically signi¢cant at better than the 1% level for all six pairs of comparisonquarters.
Panel B of Table II presents univariate summary statistics on abnormal tradingvolume around earnings releases. Much of the theoretical and empirical litera-ture on asset markets and information indicates that examining trading volumeadds to our understanding of the market reaction.While security prices aggre-gate or average investor beliefs into a consensus price, the summing of individualtrades into aggregate trading volume represents di¡erences in investor interpre-tations of an accounting disclosure.These di¡erences are suppressed in the aver-aging process that yields prices. Thus trading volume is more re£ective ofindividual di¡erences in obtaining, interpreting, and responding to an earningsevent. Furthermore, previous empirical papers have documented a positive rela-tion between trading volume and measures of di¡erent aspects of disagreementamong traders. Theoretical models of stock trading and anticipated corporatenews arrival (Kim and Verrecchia (1997), e.g.) demonstrate a relation betweentrading activity and ‘‘disagreement’’among heterogeneous traders who seek andinterpret pre-announcement and announcement period information. Therefore,excess trading can be viewed as a symptom of such disagreement whether itresults from di¡erential information or di¡erences in opinion.
8Use of abnormal trading volume is essential since there is a substantial rising trend inraw volume across most of our pairs of comparison quarters. A table of summary statisticsof actual trading volume before and after Reg FD is available on request.
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In contrast to our ¢ndings for return volatility, Panel B of Table II shows thatchanges in abnormal volume after Reg FD are not uniform across di¡erent com-parison periods. For the ¢rst post FD quarter, IV 2000, abnormal volume is lowercompared to IV 1999 but is larger compared to III 2000. For the second and thirdpost FD quarters, I 2001and II 2001, abnormal volume is signi¢cantly higher thantheir ¢scal comparison quarters, I 2000 and II 2000, respectively, but is not di¡er-ent from quarter III 2000. Thus, there is no clear change in abnormal tradingvolume observed across our six pairs of comparison quarters.
Table IIUnivariateAnalysis of Abnormal Stock ReturnVolatility andTrading
Volume around Earnings AnnouncementsAbnormal stock returns are computed based on one-factor market model residuals estimatedfrom day � 200 to day � 11with CRSP value-weighted index returns. Abnormal return volatilityis the absolute value of daily abnormal returns, summed over the window indicated. Abnormaltrading volumes are generated as the di¡erences between trading volume and the mean of dailyvolume for that stock over the pre-announcement window (� 200, � 11) normalized by the meanvolume, then summing over the period indicated.‘‘Change’’measures the mean and median with-in-¢rm change after Reg FD adoption. P-values are two sided, from t-tests for means, and fromsign tests for medians.The results are similar over (� 1, 0), (� 2, þ 2), and (� 1, þ5) windows.
B. Multivariate Regressions to Explain Returns andVolume around EarningsReleases
Previous authors have identi¢ed several factors that explain return volatilityaround earnings announcements such as ¢rm size, the number of analysts follow-ing a ¢rm, the absolute consensus forecast error as a measure of the earningssurprise, and the dispersion in analyst forecasts (El-Gazzar (1998)). To controlfor the impact of these variables, we estimate multivariate regressions to assessthe change in return volatility after implementing Reg FD. Panel A of Table IIIpresents the results of cross-sectional regressions to explain return volatilityaround earnings announcements.The return volatility cumulated over (� 1, þ1)is regressed on ¢rm size, absolute forecast error, forecast dispersion, and a dum-my variable, POST, to indicate the post Reg FD period.9
In Panel Aof Table III, coe⁄cients on ¢rm size are signi¢cantly negative, whichsuggests that return volatility around earnings releases is particularly largefor smaller ¢rms. This is consistent with the empirical results documented byprevious authors (El-Gazzar (1998)). Investors may have less incentive to gatherpre-disclosure information about smaller or lightly followed ¢rms and, as a con-sequence, the market reacts more to an earnings shock. The estimated coe⁄-cients on the absolute consensus forecast error are positive and signi¢cant,which is consistent with previous empirical ¢ndings. Larger earnings shocksyield larger market reactions.The coe⁄cients on analyst forecast dispersion aremainly insigni¢cant. The sign and signi¢cance of the coe⁄cient on the POSTdummy variable is consistently strong and negative across the six comparisonquarters, suggesting lower post Reg FD return volatility around earnings re-leases, even after controlling for other ¢rm characteristics.
Panel B of Table III presents cross-sectional regressions to explain abnormaltrading volume. In the model of Kim andVerrecchia (1997), investors employ twotypes of private information at earnings announcements. Pre-announcement in-formation is private information gathered in anticipation of a public disclosure.Event-period information is a direct product of di¡erential information proces-sing or di¡erential informed judgment across investors stimulated by the earn-ings release.10 Models based exclusively on one type of information yieldincomplete empirical implications involving price change and volume reactions.Their general model with both pre-announcement and event-period informationyields some interesting insights: Trading volume at the earnings announcement
9The change in return volatility cumulated over di¡erent windows like (� 1, 0) and (� 1,þ 5) yields results very similar to those based on (� 1, þ1). In addition, speci¢cations withthe number of analysts, rather than ¢rm size, to proxy for the ¢rm’s information environmentyield similar results.
10Di¡erential information processing is modeled in Kim and Verrecchia (1994). They arguethat some market participants process earnings announcements into private, and possiblydiverse, information about a ¢rm’s performance at a cost. This private information can bethought of as informed judgments or di¡erential interpretations of public announcements.Thus, earnings announcements stimulate informed judgments or di¡erential information pro-cessing which, in turn, create or exacerbate information asymmetries between traders andmarket makers and ultimately lead to higher trading volume.
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Table IIICross-sectional Regressions to Explain Abnormal Stock ReturnVolatility andTradingVolume
Abnormal return volatility and trading volume over a 3-day window (days � 1, 0, and 1) are regressed on explanatory variables. A dummy variable,POST, equals 1 for quarterly earnings event after Reg FD adoption, 0 for earlier comparison quarter. Size is the ¢rm’s market capitalization, theabsolute forecast error is the absolute value of the di¡erence between actual earnings and consensus forecast scaled by stock price at the end of preReg FD quarter, and forecast dispersion is the standard deviation of the most recent individual forecasts. Abnormal stock return volatility alsoserves as an explanatory variable for abnormal trading volume.White’s (1980) heteroskedasticity-consistent p-values are reported beneath eachcoe⁄cient estimate. Speci¢cations with number of analysts, rather than size, to proxy for the ¢rm’s information environment yield similar results.
is positively related to the absolute value of the contemporaneous price reaction,pre-announcement di¡erential informedness, and event-period private informa-tion (or di¡erential informed judgment).Thus, we can isolate trading volume sti-mulated by di¡erential informed judgment by controlling for absolute returnchange and our ¢rm size and forecast dispersion variables that proxy for pre-announcement di¡erential informedness. This motivates the regression speci¢-cations we estimate to explain trading volume. For simplicity, we adopt a linearand additive speci¢cation of the regression model following Bamber, Barron, andStober (1997, 1999).
In Panel B of Table III, abnormal trading volume over (� 1,þ1) is regressed on¢rm size, return volatility cumulated over the same window, analyst forecast dis-persion, and the POST Reg FD dummy variable.11 Given the importance of thereturn volatility term in the theoretical literature, we also add a slope dummyterm to assess changes in the relation between volume and return after Reg FDis imposed.We include two regression speci¢cations with and without controlvariables for each pair of comparison quarters.Without including the other vari-ables, the sign and signi¢cance of the POSTdummy varies across di¡erent com-parison periods. This is consistent with the lack of a de¢nite pattern in volumeindicated in Table II. For the second speci¢cation, the coe⁄cients on absolutereturn are uniformly signi¢cantly positive, while the coe⁄cients on the POSTReg FD slope dummy for absolute return are uniformly signi¢cantly negative.Thus, abnormal trading volume is positively related to absolute return, which isconsistent with the empirical ¢ndings of previous authors (Karpo¡ (1987) andAtiase and Bamber (1994)). The signi¢cant negative coe⁄cient on the absolutereturn slope dummy indicates that the positive association between volume andprice change is signi¢cantly weaker after the adoption of Reg FD. Unreportedtests of the sum of coe⁄cients on absolute return and its slope dummy suggestthat this relation remains signi¢cantly positive after the introduction of RegFD, even if its magnitude declines. Firm size and forecast dispersion proxy forthe pre-announcement information environment. The coe⁄cients on ¢rm sizeare sometimes signi¢cantly positivewhile those on forecast dispersion are signif-icantly negative.
The estimated coe⁄cients on the POSTdummy are positive and highly signi¢-cant at better than the 1% level in all regressions. Recall that the unconditionallevel of volume indicated in Table II, Panel B does not show a uniform pattern ofchange after Reg FD is e¡ective. Once we control for absolute return (whichre£ects the change in aggregate market expectation at the time of the earningsshock) and include proxies for pre-announcement di¡erential informedness,what remains is trading volume generated by di¡erential informed judgment(or di¡erence in opinions).Thus, the strong positive coe⁄cient on POST indicatesthat trading due to di¡erential informed judgment increases signi¢cantlyafter the adoption of Reg FD. Moreover, the estimated coe⁄cients imply an
11Results for trading volume cumulated over di¡erent windows like (� 1, 0) and (� 1, þ5)are very similar to those for (� 1, þ1). Speci¢cations with the number of analysts, rather than¢rm size, to proxy for the ¢rm’s information environment yield similar results.
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economically large impact: After the imposition of Reg FD, abnormal tradingvolume attributed to di¡erential informed judgment increases, on average,by 31%.12
C. Does DecimalizationMatter?
Our results thus far suggest that Reg FD reduces return volatility aroundtimes of earnings releases. However, our sample spans the period when theNYSE, AMEX, and Nasdaq ‘‘decimalized’’ stock trading by reducing tick sizes toone cent.This can have a signi¢cant impact on trading activity. Ronen andWea-ver (2001), for example, ¢nd that reduced tick size is associated with reducedreturn volatility when theAMEX adopted 1/16 ticks in May 1997.To test whetherdecimalization a¡ects our event-study results, we repeat the cross-sectionalregressions of Table III with an intercept dummy for earnings events that oc-curred after decimalization of trading in the particular stock. Given the timingof the decimalization program, the number of pairs of ‘‘before’’and‘‘after’’ Reg FDquarters available for study is reduced.13 Put another way, we can only studythose post Reg FD quarters for which data is available on both decimalized andundecimalized stocks.14
Table IV presents the results. In all regressions to explain return volatility, thedecimal dummy is strongly signi¢cant while the POST dummy is insigni¢cant.The apparent decrease in return volatility at earnings releases (in Panel A ofTables II and III and in Shane et al. (2001), Eleswarapu et al. (2002), and He£inet al. (2003)) is due to decimalization rather thanReg FD. In contrast, the decimaldummy has little impact on our volume results: Our ¢nding that trading volumeat earnings releases increases signi¢cantly after the adoption of Reg FD remainsstrong.15 In brief, we ¢nd no signi¢cant change in return volatility and anincrease in abnormal volume after the adoption of Reg FD.
There is an interesting parallel in the theoretical and empirical literature. InKim andVerrecchia (1997), trading volume is independent of the absolute value ofprice change when there is only event-period private information. Thus, volumecan arise without a price change at public news release, as has been documentedin Kandel and Pearson (1995) for U.S. earnings announcements. Our results sug-gest that Reg FD may not change the £ow of pre-announcement private informa-tion substantially. However, the pattern of no change in return volatility andincreased abnormal volume suggests that Reg FD may stimulate event-periodprivate information about future earnings as a result of increased di¡erences in
12The average of coe⁄cients on POST for the second speci¢cation in Table III, Panel B.13 The NYSE and AMEX were fully decimalized on January 19, 2001 and the Nasdaq on
April 9, 2001. Small numbers of stocks that underwent earlier decimalization are not presentin our ¢nal sample of companies. Therefore, only one post Reg FD quarter, IV 2000 (i.e., the¢scal quarter whose earnings releases would occur in the ¢rst few months in 2001), is avail-able for study, and is compared to pre Reg FD quarters III 2000 and IV 1999.
14Our other two post Reg FD ¢scal quarters, I 2001 and II 2001, are e¡ectively ‘‘post deci-malization.’’
15We thank the referee for pointing out the potential impact of decimalization on our re-sults.
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opinions. This can be interpreted as enhancing market e⁄ciency if it re£ectsmore information gathering and processing by analysts and traders who can nolonger rely on special access to corporate information.
D. Correlation between Return andVolume Reactions
Previous authors have found that the strength of reaction to earnings can dif-fer across price and volume. Kandel and Pearson (1995) report signi¢cant abnor-mal volume even in the absence of a substantial contemporaneous returnreaction. Bamber and Cheon (1995) ¢nd that nearly 25% of earnings events dis-play price andvolume reactions of very di¡erent relativemagnitudes. Kandel andPearson argue that only models with di¡erential interpretation of public infor-mation across investors can generate market behavior consistent with such em-pirical ¢ndings. In other words, di¡erential informed judgment (or di¡erence inopinions) can lead to di¡erent price and volume reactions.Therefore, another in-teresting way of assessing di¡erential informed judgment is to examine the fre-quency of earnings announcement events that stimulate large volume reactions
Table IVThe Impact of Decimalization on Abnormal ReturnVolatility and
AbnormalTradingVolumeNYSE and AMEX trading was fully decimalized on January 19, 2001, and Nasdaq on April 9,2001.This yields one post Reg FD quarter, IV 2000, with data for both decimalized and undeci-malized sample ¢rms. Abnormal return volatility (Panel A) and abnormal trading volume (Pa-nel B) over a 3-day window (� 1, 1) are regressed on explanatory variables including dummyvariables POST (1 for earnings events after Reg FD adoption, else 0) and DECIMAL (1 for earn-ings events at or after decimalization of trading in the stock, else 0). All other variables arede¢ned in Tables II and III.White’s (1980) heteroskedasticity-consistent p-values are reportedbeneath each coe⁄cient estimate. Speci¢cations with number of analysts, rather than size, toproxy for the ¢rm’s information environment yield similar results.
Regulation Fair Disclosure and Earnings Information 2499
but small price changes.We document associations between the price and volumereactions to earnings as follows. We classify the reaction to each earnings an-nouncement into absolute return and abnormal trading volume deciles. Follow-ing Bamber and Cheon, we characterize earnings events as ‘‘large volume-smallprice reactions’’ if the volume decile is 5 or more deciles above the price decile.For example, a decile 4 price reaction is classi¢ed as a large volume-small pricereaction if the contemporaneous volume reaction is in deciles 9 or 10. A contin-gency table summarizes the frequency of earnings announcements across allcombinations of volume and price deciles.
Table V describes the frequency of observing earnings events with large vo-lume-small price reactions.16 We focus on how the frequency of these events dif-fers in comparing pre Reg FD periods to periods since the adoption of Reg FD.Across our six sets of comparison periods, the proportion of earnings events clas-si¢ed as large volume^small price reactions uniformly increases after the adop-tion of Reg FD. The increase in the proportion ranges from 14.05% to 45.39%depending on the particular quarters compared, and averages 23.54%. This re-sult is consistent with our ¢ndings from the multivariate regressions: The asso-ciation between price change and volume becomes weaker under Reg FD. Recallthat the theoretical models ofKandel and Pearson (1995) andKim andVerrecchia(1997) imply that a large volume-small price reaction is largely due to di¡erentialinformed judgment at the time of the earnings release.Thus,TableVcon¢rms theidea that, after the imposition of Reg FD, event-period private information due todi¡erential informed judgment increases signi¢cantly.
E. Further Diagnostics
We conduct an additional check on the robustness of our event-study ¢ndings.We look for shifts in the balance between pre-announcement and announcementabnormal return volatility after the adoption of Reg FD.This serves two relatedpurposes. First, we can measure the extent to which Reg FD appears to have al-tered the pre-announcement environment. Second, we can determine if the‘‘e¡ec-tive’’ earnings announcement date has shifted forward in time before adoptingReg FD since Reg FD may attenuate pre-announcement leakage of the contentsof earnings releases. Following El-Gazzar (1998), pre-announcement return vola-tility equals the average absolute return from the end of the quarter to 2 daysbefore the earnings announcement for the quarter. Earnings announcement vo-latility equals the average absolute return over (� 1, þ1). Thus, the degree towhich the ratio of pre-announcement to event volatility exceeds one indicates,on average, how much more earnings information per day is conveyed prior tothe formal release versus at the time of the release.The ratio is immune to deci-malization e¡ects.
16 The theoretical model in Kandel and Pearson (1995) indicates that the large volume^small price reaction case only occurs in the presence of di¡erence in opinion among investors.It does not suggest any connection between a ‘‘small volume^large price reaction’’ event anddi¡erence in opinion. Therefore we do not assess the change of frequency of small volume^large price reaction events.
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TableVI presents the results. For three pairs of comparison quarters (post RegFDIV 2000 versus pre Reg FDIV1999, IV 2000 versus III 2000, and I 2001versus III2000), the ratio of pre-announcement return volatility to announcement period re-turn volatility increases signi¢cantly after the adoption of Reg FD. For the otherthree pairs, the ratio decreases, though the magnitude of the decrease seems smal-ler than the increases observed for the others.Thus, there is no de¢nitive evidenceon the extent to which Reg FD is associated with more or less pre-announcementprice volatility relative to announcement period volatility.Therefore, these resultso¡er no clear evidence of a change in the pre-announcement environment after theadoption of Reg FD. In particular, there is no evidence that Reg FD prevents orenhances information leakage at the pre-announcement period.
III. The Impact of Reg FD on Analysts and Corporate Disclosure
In the previous section, we studied the impact of Reg FD through the lensof stock trading activity around earnings announcements. Most notably, the
TableVSummary Statistics from ContingencyTables of AbnormalTradingVolume Reaction Deciles byAbnormal ReturnVolatility Deciles
We classify reactions to earnings announcements into returnvolatilityand trading volume dec-iles and tabulate the frequency of earnings events in each volume-return reaction decile cells.We de¢ne large volume^small return reaction events as those for which the di¡erence betweenthe volume and return deciles is ¢ve or more. ‘‘Change’’ represents the percentage change inevents with large volume^small price reactions in post Reg FD quarter.
Quarter
Number ofEvents with LargeVolume^SmallPrice Reactions
TotalEvents
Percent of Eventswith LargeVolume^Small
Price Reactions
IV 1999 177 1914 9.25%IV 2000 228 1914 11.91%Change 28.81%
III 2000 152 1919 7.92%IV 2000 221 1919 11.52%Change 45.39%
I 2000 201 2144 9.38%I 2001 235 2144 10.96%Change 16.92%
III 2000 144 1615 8.92%I 2001 165 1615 10.22%Change 14.58%
II 2000 149 1683 8.85%II 2001 181 1683 10.75%Change 21.48%
III 2000 121 1277 9.48%II 2001 138 1277 10.81%Change 14.05%
Regulation Fair Disclosure and Earnings Information 2501
cross-sectional regressions evidence on abnormal trading volume suggests thatReg FD has signi¢cantly increased di¡erential informed judgments or di¡er-ences in opinion, thereby stimulating trading volume. In this section, we seekfurther speci¢c evidence of the impact of Reg FD on the ability of a class of in-vestment professionals, ¢nancial analysts, to perform e¡ectively. We also mea-sure the extent to which corporations have increased or decreased voluntarypublic disclosures to, in some sense, replace private communication now bannedby Reg FD.
TableVIUnivariateAnalysis of the Ratio of Pre-announcement ReturnVolatility
to Event-period ReturnVolatilityFollowing El-Gazzar (1998), pre-announcement return volatility is the average absolute returnfrom the end of the quarter to 2 days before the earnings announcement for the quarter. Earn-ings announcement event volatility is measured over window (� 1, 0) or window (� 1, þ1).Thedegree towhich the ratio of pre-announcement to event volatilityexceeds one indicates, on aver-age, how much earnings information per day is conveyed prior to the formal release relative tothe amount of information per day conveyed around the time of the release.‘‘Change’’measuresthe mean and median within-¢rm change after Reg FD adoption. P-values are two sided, fromt-tests for means, and from sign tests for medians. Results are similar over window (� 1, 0).
A. The Behavior of Pre-announcement Analyst Forecasts
The intention of Reg FD is to level the playing ¢eld and take away the advan-tage that ¢nancial analysts and others with privileged access to ¢rms enjoy rela-tive to ordinary investors. However, the new regulation may cause a chillinge¡ect: Companies maybecome less forthcoming in public announcements for fearof litigation problems, and may be reluctant to reveal detailed information to thepublic for fear of bene¢ting competitors.The chilling e¡ect may be especially sig-ni¢cant on information regarding earnings beyond the current quarter. Cuttingo¡ private communication between companies and analysts may impair the abil-ity of analysts to form opinions and reach consensus on interpreting earningsinformation, which in turn increases di¡erential informed judgment among in-vestors upon earnings releases.
TableVIIUnivariateAnalysis of Analyst Forecasts
Absolute consensus forecast error is the absolute value of the di¡erence between reported earn-ings for a quarter and the mean of most recent analyst forecasts. Absolute time series forecasterror is the absolute value of the seasonal change in quarterly earnings. Analyst informationadvantage equals absolute time series forecast error minus absolute consensus forecast error.Analyst forecast dispersion is the standard deviation of the most recent forecasts of quarterlyearnings. All variables are scaled by the stock price at the end of the pre Reg FD comparisonquarter.To save space, individual pairs of comparison quarters are suppressed. In Panel A, weaggregate all comparison quarters matched by ¢scal quarters, including three post Reg FDquarters and their corresponding ¢scal quarters in the previous years. In Panel B, we aggregateall comparison quarters match by proximity to Reg FD implementation, including three postReg FD quarters and quarter III 2000. ‘‘Change’’ measures the mean and median within-¢rmchange after Reg FD adoption. P-values are two-sided, from t-tests for means, and from signtests for medians. The results are similar to those of individual pairs of comparison quarters,which are available upon request.
Absolute ConsensusForecast Error
AbsoluteTime-Series Forecast
Error
AnalystInformationAdvantage
Analyst ForecastDispersion
Mean Median Mean Median Mean Median Mean Median
Panel A: Aggregate All Comparison Quarters Matched by Fiscal Quarter
Regulation Fair Disclosure and Earnings Information 2503
TableVII presents summary statistics on several aspects of analyst forecastsbefore and after the adoption of Reg FD.To save space, data are aggregated acrossall comparison quarters matched by ¢scal quarters or by proximity to Reg FDimplementation rather than presenting pairs of comparison quarters individu-ally as in previous tables.The aggregated results are similar to what is found inthe unreported individual quarterly summaries (available upon request).There isno consistent evidence of change in the absolute consensus forecast error afterthe adoption of Reg FD.17 The sign and magnitude of the change varies dependingon whether we match by ¢scal quarter or by proximity to Reg FD, and whethermean or median is examined.The time-series forecast error and analyst informa-tion advantage generally increase after the adoption of Reg FD. However, the sig-ni¢cance of the change depends on whether mean or median is tested. Incontrast, we observe unambiguously strong evidence that analyst forecast dis-persion increases signi¢cantly after Reg FD is adopted, regardless of whether
TableVIIIAnalyst Forecasts across Business Cycles
This table compares forecast behavior in post Reg FD quarters with quarters from the early1990s economic downtown matched on either proximity to the business cycle peak or quarterlyseasonality. Forecast variables are de¢ned in TableVII.To save space, individual pairs of com-parison quarters are suppressed. NBER business cycle peaks are July 1990 and March 2001. InPanel A, we aggregate all comparison quarters matched by ¢scal quarters, including quartersIV 2000 and IV 1989, I 2001 and I 1990, and II 2001 and II 1991. In Panel B, we aggregate allcomparison quarters match by proximity to business cycle peak, including quarters IV 2000and II 1990, I 2001and III 1990, and II 2001and IV1990.‘‘Change’’measures the mean andmedianwithin-¢rm change after Reg FD adoption. P-values are two-sided, from t-tests for means, andfrom sign tests for medians.The results are similar to those of individual pairs of comparisonquarters, which are available upon request.
Absolute ConsensusForecast Error
AbsoluteTime-Series Forecast
ErrorAnalyst Information
AdvantageAnalyst Forecast
Dispersion
Mean Median Mean Median Mean Median Mean Median
Panel A: Aggregate All Comparison Quarters Matched by Fiscal Quarter
17 See Monhanram and Sunder (2001), Shane et al. (2001), Topaloglu (2002), and He£in et al.(2003) for related evidence.
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mean or median dispersion is analyzed. It suggests that Reg FD impairs the abil-ity of ¢nancial analysts to reach pre-announcement consensus given a reduced£owof private communications from corporations. On the other hand, consensusforecasts are not less accurate following the implementation of Reg FD.
While TableVII suggests that forecast dispersion increases after Reg FD, wenote that some of our ‘‘before’’and‘‘after’’periods span di¡erent points in the busi-ness cycle. In particular, our post Reg FD quarters represent a period of risingeconomic uncertainty and disappointing corporate earnings relative to our preReg FD quarters.Therefore, we also benchmark analyst behavior in post Reg FDquarters against quarters from an earlier economic cycle.The NBER’s ‘‘BusinessCycle Expansions and Contractions’’18 indicates abusiness cycle peak around themiddle of our post Reg FD sample, March 2001, and an earlier peak at July 1990.Table VIII presents summary statistics that compare quarters centered on theJuly 1990 peak to our post Reg FD sample of quarters centered on the March2001 peak. The post Reg FD quarters and early 1990s quarters are matched oneither quarterly seasonality or proximity to the business cycle peak. Again, wepresent averages rather than individual quarters.
The most notable ¢nding in this table is that, based on means though not med-ians, post Reg FD forecast dispersion is lower than in comparison quarters(matched on either proximity to the business cycle peak or quarterly seasonality)from the early 1990s downturn. Although we do not report results for individualquarters, this ¢nding is consistent across di¡erent pairs of quarters, is typicallysigni¢cant for means, but typically insigni¢cant for medians. Put another way,forecast dispersion is generally higher in the early 1990s regardless of whether wecompare quarters before, at, or after the business cycle peak. However, we mustnote that the two business cycles are qualitatively di¡erent. The introduction ofReg FD in October 2000 precedes the March 2001 business cycle peak by severalmonths, but occurs 7 months after the stock market peak in March 2000. In con-trast, business cycle andWilshire 5000 stock market peaks in the early 1990s coin-cide exactly at July 1990.The July 1990 peak is also associated with the invasion ofKuwait and higher oil prices while the peak of 2000/2001 is associatedwith the col-lapse of ‘‘dot com’’ stock prices and technology business. In addition, the informa-tion environment, corporate earnings disclosure policy, and information toolsutilized by analysts di¡er in comparing 2001 to 1990. This could contribute to thedi¡erence in analysts forecast dispersionbetween the twoperiods. Finally, one pairof comparison quarters (post Reg FD IV 2000 versus pre Reg FD III 2000) are veryclose in time and occur entirely prior to the formal business cycle peak of March2001. Thus, this comparison pair is drawn from similar business cycle conditionsand the ¢nding that forecast dispersion increases with Reg FD cannot be ascribedto substantially di¡erent phases of the business cycle. Therefore, it appears that,with the adoption of Reg FD, forecast dispersion has probably risen, although itdoes not exceed historical norms suggested by the early 1990s evidence.19
18 www.nber.org/cycles.html.19 See additional evidence on business cycle e¡ects consistent with our interpretation in
Agrawal and Chadha (2002).
Regulation Fair Disclosure and Earnings Information 2505
B. Belief Revisions and OtherAspects of Disagreement among Analysts
In the analysis above, forecast dispersion is a measure of pre-announcementdisagreement among analysts. It is not necessarily directly related to di¡erentialinformed judgment stimulated by the earnings release. Furthermore, disagree-ment has many facets. For example, Karpo¡ (1986) suggests that trading volumein periods immediately after an informative announcement may result from‘‘prior dispersion of beliefs’’ or ‘‘jumbling’’ of beliefs. ‘‘Prior dispersion’’ refers tothe predisclosure variation in beliefs across analysts, which is analyzed inTable VII. ‘‘Jumbling’’ refers to information-triggered belief revisions thatdi¡er across analysts. It is of particular interest, as it can re£ect disagreementtriggered by an earnings announcement, that is, di¡erential informed judgment.In our earlier cross-sectional regression to explain abnormal trading volume,we try to isolate trading volume arising from di¡erential interpretation ofearnings by controlling for price change and di¡erences in pre-announcementinformedness.
Bamber et al. (1997) illustrate three distinct di¡erent aspects of disagreementamong analysts and the incremental role of each in explaining trading volumearound earnings announcements. Dispersion in prior beliefs is the level of varia-tion in expectations before the earnings announcements. It is measured as thestandard deviation of all analysts’ forecasts of annual earnings issued within 45days of the interim earnings announcements, scaled by the absolute value of themean annual earnings forecasts. Belief jumbling occurs as investors’ beliefschange positions relative to each other around the earnings announcement. Itis measured as one minus the correlation between annual earnings forecasts is-sued in the 45 days before the interim earnings announcement and annual earn-ings forecasts issued within 30 days after the interim earnings announcements.Change in dispersion is the di¡erence in the level of dispersion in beliefs after ver-sus before the interim earnings announcement. It is measured as the standarddeviation of annual forecasts issued within 45 days before an interim earningsannouncement minus the standard deviation of annual earnings forecasts issuedwithin 30 days after an interim earnings announcements.The change is de£atedby the absolute value of the mean pre-announcement forecast.
Belief jumbling re£ects some of the analyst disagreement resulting from di¡er-ences in opinion. Kandel and Pearson (1995) develop an empirical measure thatisolates di¡erential belief revisions attributable exclusively to di¡erence in opi-nion rather than predisclosure private information. Their measure identi¢espairs of analyst forecasts that move in opposite directions and also either £ip(i.e., cross) or diverge (i.e., move farther apart).The Kandel and Pearson measureis the proportion of such movements in all possible pairs of analyst forecastsrevisions. Bamber et al. (1999) show that the Kandel and Pearson measure issigni¢cantly correlated with trading volume around earnings announcements.Thus, the Kandel and Pearson measure can be used as a direct test of whetherReg FD induces more di¡erential informed judgment.
In Table IX, we document post Reg FD changes in the measures de¢ned byBamber et al. (1997) and Kandel and Pearson (1995). Given sample restrictions,
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Table IXSummary Statistics on Other Measures of Disagreement
The table summarizes several measures of disagreement. Dispersion of annual earnings fore-casts before interim quarterly earnings announcement equals the standard deviation of all ana-lysts’ forecasts of annual earnings issued within 45 days prior to the interim earningsannouncement, scaled by the absolute value of the mean annual earnings forecasts. Dispersionof annual earnings forecasts after interim quarterly earnings announcement equals the stan-dard deviation of all analysts’ forecasts of annual earnings issuedwithin 30 days after the inter-im earnings announcement, scaled by the absolute value of the mean annual earnings forecasts.Change in forecast dispersion equals the standard deviation of annual forecasts issued within45 days before the interim earnings announcement, minus the standard deviation of the annualearnings forecasts issuedwithin 30 days after the interim earnings announcements.The changeis de£ated by the absolute value of the mean pre-announcement forecast. Belief jumblingaround interim earnings announcements is measured as one minus the correlation betweenannual earnings forecasts issued in the 45 days before the interim earnings announcementsand annual earnings forecasts issuedwithin 30 days after the interim earnings announcements.To be included in our analysis, an analyst must issue a forecast of annual earnings within 45days before the quarterly earnings announcement and the same analyst must issue a revisedforecast of annual earnings within 30 days after the quarterly earnings announcement. Each¢rm must have at least four such analysts. Panels B, C, and D summarize the Kandel and Pear-son (1995) measure of di¡erence in opinion. For all possible pairings of analysts for each quar-terly announcement, the Kandel and Pearson measure is the proportion of analyst pairs whoseforecast revisions both move in opposite direction, and either £ip or diverge. For all panels,‘‘change’’ measures the mean and median within-¢rm change after Reg FD adoption. P-valuesare two-sided, from t-tests for means, and from sign tests for medians.The number of observa-tions is designated ‘‘NOBS.’’
Panel A: Summary Statistics on Measures of Disagreement
Regulation Fair Disclosure and Earnings Information 2507
there are two post-Reg FD quarters, I 2001and II 2001, and two comparison quar-ters, I 2000 and II 2000, available for this purpose.20 Furthermore, there are toofewannual forecasts for I 2001to compute themeasures of disagreement, perhapsbecause very few analysts o¡er or revise annual forecasts in the ¢rst ¢scal quar-ter, given that the ¢scal year end is distant. This yields one pair of comparisonquarters, post Reg FD II 2001and pre Reg FD II 2000, with 268 observations each.
Panel A reports summary statistics on the Bamber et al. (1997) measures. Con-sistent with the results of TableVII, the dispersion of annual earnings forecastsbefore interim earnings announcements is signi¢cantlyhigher after the adoptionof Reg FD.The dispersion of annual earnings forecasts after quarterly earningsannouncements decreases (relative to before quarterly earnings) for both II 2000and II 2001. Although it decreases signi¢cantly more for II 2001, forecast disper-sion after earnings announcements is still signi¢cantly higher in the post RegFD quarter. The results suggest that, after the imposition of Reg FD, disagree-ment about annual earnings is both signi¢cantly higher pre and post interimearnings announcement. Panel A also shows that mean and median belief jum-bling in quarter II 2001are signi¢cantly larger than those in the pre Reg FD com-parison quarter, II 2000.Thus, analysts’ beliefs change positions relative to eachother around the interim earnings announcements, indicating di¡erential inter-pretation of interim earnings.Thus, the adoption of Reg FD induces more di¡er-ential interpretation of earnings among analysts. In brief, we ¢nd thatdisagreement and di¡erential informed judgment about future annual earningsincreases with the adoption of Reg FD.
Panels B to D of Table IX report summary statistics on theKandel and Pearson(1995) measure. Panel B indicates mean values of 23% and 27% for II 2000 and II2001, respectively. Median values are zero for both quarters: The Kandel andPearson measure is zero for 66% (51%) of ¢rms in II 2000 (II 2001).21 Panel C
Panel D: Univariate Statistics on the Kandel and Pearson Measure
20We do not compare post Reg FD quarters with III 2000 since seasonality is very impor-tant for annual forecasts. The number and quality of annual forecasts in the third quarter arenot comparable to those in the second quarter.
21The Kandel and Pearson (1995) measure identi¢es pairs of analysts’ forecasts that move inopposite directions and also either £ip or diverge. This is a much more stringent proxy fordi¡erences in opinion than belief jumbling. It aims to identify analyst behavior exclusivelydue to di¡erences in opinion in a Bayesian model, rather than due to di¡erential predisclo-sure information. In many cases, we may not observe any behavior satisfying the stringentrequirement, and the measure equals zero.
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shows that, after Reg FD is adopted, the Kandel and Pearson measure decreasesfor 25% of ¢rms, remains unchanged for 37% of ¢rms, and increases for 38% of¢rms.Tests reported in Panel D show that mean and median of the change of themeasure after Reg FD are signi¢cantly positive.22 Thus, this measure of di¡er-ence in opinion increases signi¢cantly after the adoption of Reg FD. This sup-ports our earlier ¢nding that Reg FD is associated with increased di¡erentialinformed judgment.
C. Do Firms Respond to Reg FDwithMore Public Disclosure?
We conclude by examining the response of corporations to Reg FD. InTable X,we examine the change in the quantity of voluntary earnings-related disclosuresafter Reg FD is implemented.The source of corporate news releases that containestimates or discussions of current or future earnings releases is the ‘‘CompanyIssuedGuidance’’ (CIG) ¢le of the First/Call databases. For each ¢scal quarter westudy, we examine the number of voluntary earnings-related disclosures occur-ring between the actual earnings announcement date for the previous quarterand the actual earnings announcement release date for the current quarter.
In Table X, the average number of total disclosures per ¢rm (including newsabout current and future earnings) across all our sample ¢rms increases signi¢-cantly after the adoption of Reg FD.This result is robust across six pairs of com-parison quarters, regardless of whether we exclude earnings disclosuresoccurring on the same day as the actual earnings announcement.While the aver-age number of disclosures about current earnings increases signi¢cantly anduniformly after Reg FD, there is some spotty evidence that the average numberof disclosures about future earnings decreases after Reg FD for some compari-son quarters. Overall, onlya small fraction of ¢rms voluntarily discloses informa-tion about current earnings, and voluntary disclosures about future earnings areeven more unusual. Therefore, we also measure the change in the proportion ofsample ¢rms making at least one such disclosure. Unreported results (availableupon request) indicate that the proportion of such ¢rms typically increases afterthe adoption of Reg FD.While the results are robust and uniformwith respect todisclosures about current earnings, the results on disclosures about future earn-ings are inconclusive.23
How do the results on voluntary releases by corporations relate to our ¢ndingson analyst forecasts and market reactions? We ¢nd that corporate informationreleases about forthcoming earnings increase, return volatility at earnings an-nouncements does not increase, and the accuracy of analyst forecasts of current
22 Since more than 50% of observations having KP measures equal to zero in both quartersII 2000 and II 2001, the median values of the KP measure are zero in both quarters. However,a nonparametric sign test is able to examine the null hypothesis that the change in median iszero.
23 Straser (2002) uses a smaller sample of ¢rms and a smaller time period, but broadersources of company releases to conclude that the quantity of voluntary disclosure has in-creased. He£in et al. (2003) also report an increase in voluntary corporate disclosures. Busheeet al. (2002) document changes in the use and impact of conference calls.
Regulation Fair Disclosure and Earnings Information 2509
quarterly earnings does not deteriorate, though forecast dispersion increasesafter the adoption of Reg FD. On the other hand, there is more trading volumedue to di¡erences in opinions, more of a struggle in the form of belief revisionsfor forecasts of earnings beyond the current quarter, and no clear increase involuntary releases concerning earnings beyond the current quarter. Therefore,
TableXChanges in the Quantity of CorporateVoluntary Earnings Disclosures
We obtain corporate news releases that contain estimates or discussions of current or futureearnings from the CIG ¢le of the First/Call databases. For each ¢scal quarter we study, we com-pute the number of disclosures occurring between the actual earnings announcement date forthe previous quarter and the actual earnings announcement date for the current quarter.The‘‘current quarter’’column represents voluntary disclosures regarding the forthcoming earningsannouncement. The ‘‘future quarter’’ column represents voluntary disclosures regarding thequarters beyond the current quarter.The‘‘total’’column includes both types of disclosures. Ana-lysis is conductedwith samples that either include or exclude disclosures occurring on the sameday as the actual earnings announcement. ‘‘Change’’ measures the mean within-¢rm changeafter Reg FD adoption. P-values are two-sided, from t-tests for means.
there may be a term structure to the impact of Reg FD: Greater di⁄culty withdistant forecasts is the cost of more‘‘fairness’’with little e¡ect on short-run fore-casts. It mayalso be the case that companies are not comfortable releasing vaguelonger-horizon information publicly due to potential legal problems but, prior toReg FD, were willing to discuss such information privately to analysts since theyhave the expertise to process such information. Furthermore, the increase in vo-luntarydisclosure does not seem associatedwith aclear increase in the qualityofinformation: The change in pre-announcement return volatility (versus event-period return volatility) after Reg FD is ambiguous. Straser (2002) also docu-ments an increase in corporate disclosure after the adoption of Reg FD.However,this is accompanied by a signi¢cant increase in information asymmetry and ahigher probability of private information in the ¢nancial market. Thus, compa-nies may be responding to Reg FD with an increased quantity of lower qualitypublic information releases.
IV. Summary and Conclusions
We study the impact of Regulation Fair Disclosurewith stock market reactionsto earnings announcements, analyst behavior, and corporate voluntary disclo-sures across several pairs of quarters that span the regulation’s adoption in Octo-ber 2000. Our results reveal a variety of facets to the impact of Reg FD.Whilethere is no evidence that return volatility at earnings announcements is a¡ectedafter controlling for decimalization, volume reveals substantially increased dif-ferential informed judgment (or di¡erence in opinions) among investors afterReg FD came into force. A detailed examination of analyst forecasts indicatesthat several dimensions of disagreement among analysts increase after Reg FDcame into force, con¢rming our interpretation of evidence on volume. In particu-lar, it appears that the ability to forecast future annual earnings is made moredi⁄cult by Reg FD, although the accuracy of forecasts of current quarterly earn-ings seems una¡ected.
In surveys of market participants recently conducted by the Security IndustryAssociation (SIA), the National Investor Relations Institute (NIRI), and the As-sociation for InvestmentManagement and Research (AIMR),24 many survey par-ticipants believe that the quantity and quality of information has declined sinceReg FD was implemented. In particular, public communications of issuers arebelieved to be of lower quality. Thus, market participants perceive Reg FD asdampening the £ow of information. Furthermore, sell-side analysts believe theirrecommendations are adverselya¡ected, portfoliomanagers have less con¢dencein analyst estimates, and buy-side analysts feel that Reg FD has had an adverseimpact on their ability to advise portfolio managers. Ultimately, the costs ofReg FD can trickle down to retail investors who depend on professional advisorsfor investment decisions and information gleaned from the ¢nancial media. Onthe other hand, surveys of analysts and other investment professionals may
24 See Securities Industry Association (2001).
Regulation Fair Disclosure and Earnings Information 2511
merely re£ect their fear that Reg FD removes some of their privileges, ratherthan their concern for ‘‘fairness’’or for the well-being of small investors.
How does our evidence con¢rm or refute the concerns of investment profes-sionals? Our summary of voluntary corporate disclosure suggests that it is nottrue that Reg FD has reduced the quantity of information. Our evidence on theaccuracy of quarterly analyst forecasts indicates that Reg FD has not caused thequality of these forecasts to deteriorate. On the other hand, the evidence on fore-cast revision and belief jumbling suggests that preparing forecasts of futureearnings has become more di⁄cult with the adoption of Reg FD. Our volume evi-dence suggests increased di¡erential informed judgment after the adoption ofReg FD.There is, of course, more than one interpretation of this evidence:Withmore corporate voluntary disclosure and more e¡ort devoted to ¢nancial analy-sis in place of privileged communication, a greater variety of information andopinions manifests itself in increased trading volume.
Our overall evidence suggests the following scenario to explain the e¡ect ofReg FD. With the adoption of Reg FD, corporations release more informationabout current quarter earnings, though not about more distant future earnings.Analysts forecasts about current quarter earnings do not signi¢cantly deterio-rate and event-period return volatility does not change signi¢cantly. Howeverthe chilling e¡ect of Reg FD on future earnings information yields increased ab-normal trading volume due to increased di¡erences in opinion and belief revi-sions among analysts. Fairness increases with no cost in terms of analystactivity regarding current quality earnings, but with an impact on their distantforecasts.
Our results call for more extensive investigation of the possible bene¢ts andcosts of Reg FD. A vibrant and e⁄cient stock market is a source of growth andprosperity, and it is important to determine whether or not this new regulationcauses more problems for the stock market than it solves.We can suggest severaldirections for future research. If market participants are still adjusting to thenew regulation, more de¢nitive results will be possible once more data from theperiod since October 2000 has accumulated. Other markets may provide furtherevidence on the importance of Reg FD. Foreign securities listed in the U.S. arenot subject to Reg FDand may show no post Reg FD changes in market behavior.If foreign securities analysts continue to obtain privileged information aboutU.S. corporations after Reg FD, overseas trading of U.S. stocks may di¡er fromdomestic trading. Finally, legislation similar to Reg FD may spread to other jur-isdictions.The in£uence of such regulation may depend on market structure, le-gal systems, accounting quality, and other aspects of markets beyond the UnitedStates.25,26
25 For example, the securities laws of some Canadian provinces already imply a requirementsimilar to Reg FD, and some regulatory o⁄cials have expressed interest in further strength-ening their e¡ect (see Canadian Press (2000)). U.K. regulators are contemplating strengthen-ing their regulations.
26 See, for example, Fleming (2001) for Australian evidence.
The Journal of Finance2512
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