the informativeness of analyst forecast revisions and the valuation of r&d-intensive firms

J. Account. Public Policy 30 (2011) 1–21

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J. Account. Public Policy

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The informativeness of analyst forecast revisionsand the valuation of R&D-intensive firms

Yuan Huang a,⇑, Guochang Zhang b,1

a School of Accounting and Finance, Hong Kong Polytechnic University, Hung Hom, Hong Kongb Department of Accounting, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

a r t i c l e i n f o a b s t r a c t

Article history:

0278-4254/$ - see front matter � 2010 Elsevier Indoi:10.1016/j.jaccpubpol.2010.09.004

⇑ Corresponding author. Tel.: +852 2766 7953; fE-mail addresses: [email protected] (Y. H

1 Tel.: +852 2358 7569; fax: +852 2358 1693.

Prior studies (e.g., McNichols and O’Brien, 1997; Diether et al.,2002) find that analysts are less willing to disclose unfavorableearnings forecasts than to disclose favorable forecasts, and this ten-dency induces an optimistic bias in disclosed forecasts thatincreases with the degree of earnings uncertainty. Building onthese findings, we predict that, in the context of R&D-intensiveindustries, there should be differential informativeness and asym-metric valuation roles for upward versus downward analyst fore-cast revisions. Consistent with our predictions, we find thefollowing evidence: (i) analyst forecast revisions contain a down-ward bias, causing upward revisions to under-represent, whereasdownward revisions to over-represent, changes in true earningsexpectations, with the extent of over/under-representation greaterfor firms with higher R&D expenditures; (ii) upward revisions areassociated with more rapid reductions in earnings uncertainties(proxied by forecast dispersions) than downward revisions, mainlyfor high R&D firms; and (iii) upward revisions are more effective inmitigating the return differentials between high and low R&D firms(as documented in Chan et al., 2001).

� 2010 Elsevier Inc. All rights reserved.

1. Introduction

This paper examines how and to what extent analyst earnings-forecast revisions can improve theprice discovery process of research-and-development (thereafter R&D) intensive firms. Prior research

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ax: +852 2330 9845.uang), [email protected] (G. Zhang).

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2 Y. Huang, G. Zhang / J. Account. Public Policy 30 (2011) 1–21

suggests that investors do not fully understand the cash-flow implications of R&D investments(e.g., Lev and Sougiannis, 1996; Chan et al., 2001). For example, R&D activities are generally followedby persistent stock-price drifts that increase with a firm’s R&D intensity, and on average a hedge port-folio of buying the stocks of high R&D firms and selling the stocks of low R&D firms earns significantabnormal returns. The purpose of this paper is to explore (i) how useful revisions in analyst earningsforecasts are for conveying the performance of R&D-intensive firms and resolving their earningsuncertainties and (ii) to what extent forecast revisions enhance the informational efficiency of thestock prices of R&D firms.

Analysts, as information intermediaries between firms and capital markets, serve an especiallyimportant role for firms in R&D-intensive industries. R&D activities, while vital for a firm’s innovationand competitiveness, are highly risky investments (Lev, 2002). In general, the economic benefits thatfirms receive from R&D activities tend to be long term and are difficult to predict. For example, Kothariet al. (2002) find that earnings variability associated with R&D expenditures is three to five timesgreater than that associated with investments in property, plants, and equipment. High uncertaintiesof future performance make historical data less useful as the basis for financial forecasting. Further-more, Lev and Sougiannis (1996) suggest that immediate expensing of R&D expenditures leads toaccounting data less informative about the future economic benefits.2 Under such circumstances, theneed is greater for professional analysts to search, process, and disseminate a firm’s fundamental infor-mation so as to compensate for deficiencies in reported financial data (Barth et al., 2001; Boone andRaman, 2001; Amir et al., 2003). Indeed, Pandit et al. (2009) show that the relation between R&D expen-ditures and future performance, in terms of both the level and the variability, is not a mechanical one butis dependent on a firm’s productivity of R&D outlays (in the form of patent counts and citations), whichpoints to the importance of information outside financial statements.

Analysts constantly revise and update earnings forecasts as they receive new information about theunderlying firms, and revisions in forecasts serve as a main source of information for investors’ on-going trading. We explain in this study that the usefulness of forecast revisions for valuation is asym-metric between upward and downward revisions, especially for firms with high (versus low) R&Dexpenditures. Our arguments are developed based on prior findings (see, for example, McNicholsand O’Brien, 1997) that analysts tend to be less willing to disclose unfavorable earnings forecasts thanfavorable forecasts, and thus disclosed forecasts as a group exhibit optimistic biases in situationswhere analysts’ opinions diverge. Furthermore, Diether et al. (2002) argue that such optimistic biasesare greater when analysts’ forecasts are more dispersed. In the context of R&D investments, high R&Dfirms are associated with larger earnings uncertainties and more divergent analyst opinions than lowR&D firms (Barron et al., 2002); therefore, we expect optimistic biases in disclosed forecasts to begreater for the former firm group than for the latter.

Over time, as uncertainties become resolved and all expectations converge to actual earnings, opti-mistic forecast biases gradually diminish. Therefore, we expect revisions in analyst forecasts to gener-ally have a downward trend.3 However, unlike downward revisions, upward revisions are out of theexpected trend and thus are more likely to be triggered by fundamental information as received by ana-lysts. This suggests that on average upward revisions about a firm’s performance should be more infor-mative than downward revisions and more useful for improving the accuracy of valuation.Furthermore, to the extent that there are greater optimistic forecast biases (and therefore a steeperdownward trend in revisions) for high R&D firms than for low R&D firms, we expect the relative infor-mativeness of upward (over downward) revisions to be greater for high R&D firms. In this study, wetest the asymmetries between upward and downward forecast revisions in their association with (i)changes in earnings expectations, (ii) the extent of earnings uncertainty resolution, and (iii) the returnspread between high and low R&D firms.

2 The prevailing accounting standards of the US and many other jurisdictions generally require that R&D expenditures beexpensed regardless of expected future benefits (Financial Accounting Standards Board, 1974).

3Other explanations have also been proposed in theliterature for why there are more downward than upward revisions, such as(i) incentives to guide analyst forecasts down by firms with high transient institutional holdings, a heavy reliance on implicitclaims with the firm’s stakeholders, or a high value-relevance of earnings (Matsumoto, 2002), and (ii) talking down earningsforecasts to a beatable level when insiders are net sellers of stocks after earnings announcements (Richardson et al., 2004).

Y. Huang, G. Zhang / J. Account. Public Policy 30 (2011) 1–21 3

Empirical results based on R&D firms from seven science- and technology-oriented industries areconsistent with our predictions. We find that, on average, forecast revisions are negative (down-ward), and the magnitude of downward revisions increases with a firm’s R&D intensity. Moreimportantly, we find asymmetric associations between upward and downward revisions and funda-mental information in the following sense: (i) upward forecast revisions tend to understate,whereas downward revisions overstate, changes in true earnings expectations, with the magnitudeof over-/under-representation increasing in R&D intensity; and (ii) there is a greater reduction inearnings uncertainty (proxied by the forecast dispersion) following upward revisions than followingdownward revisions (controlling for the magnitudes of revisions) in firms with more intensive R&Dactivities.

We then examine the usefulness of forecast revisions for improving the informational efficiency ofstock prices, and find upward and downward revisions to have asymmetric impacts as well. Condi-tional on downward revisions, a hedge portfolio of buying the stocks in the highest R&D quartileand selling the stocks in the lowest R&D quartile can earn statistically and economically significantreturns; in our sample, the monthly abnormal return adjusted for size, book-to-market and prior-yearreturns amounts to 1.20% (an annualized return of 14.40%) over a 1-year period following portfolioformation. In contrast, conditional on upward revisions, the monthly abnormal return from theR&D-based hedge portfolio is a much smaller 0.27% (an annualized return of 3.24%) and is statisticallyinsignificant. Similar differences are observed in abnormal returns inferred from a factor model be-tween firms with upward revisions and those with downward revisions.

Our further analysis shows that the differential return performance of the R&D-based hedge port-folio following upward versus following downward revisions is not driven by the different degrees ofearnings uncertainty (proxied by the forecast dispersion) prior to the revisions. After independentlysorting firms by the pre-revision forecast dispersion into low and high groups and then by the direc-tion of revision into upward and downward groups, we find that upward revisions are more useful inmitigating R&D-related price drifts than downward revisions in both low- and high-dispersion groups.

Our study contributes to the literature in the following respects. First, the study helps investors(and researchers) to better understand the informativeness of analyst forecast revisions about a firm’sfundamental performance in the context of R&D investments. In general, forecast revisions are usefulfor updating investors on changes in earnings expectations and resolving earnings uncertainties, butthey need to be interpreted with caution. Users should be aware that upward and downward revisionscan be distorted as signals of earnings expectation, especially for firms with intensive R&D activities;failure to correct for the distortions in the signals leads to erroneous interpretations. Furthermore, thespeed of uncertainty resolution differs between upward and downward revisions. While previousstudies have documented that downward revisions occur more frequently than upward revisions(e.g., O’Brien, 1988; Gleason and Lee, 2003), the asymmetric informativeness of upward and down-ward forecast revisions has not been recognized in the existing literature.

Second, the study provides direct evidence on the valuation usefulness of analyst forecasts as analternative source of information about R&D firms. While prior studies have related the level ofR&D expenditures to analyst coverage (Barth et al., 2001), analyst forecast consensus (Barron et al.,2002), and analysts’ tendency to revise forecasts (in terms of revision frequency and magnitude)(Ho et al., 2007), they have not examined the valuation consequences of analyst activities. Thus, it re-mains unclear whether, and to what extent, analyst activities directly help to mitigate delayed pricereactions to R&D investments, as documented by Chan et al. (2001). Our study finds that analyst activ-ities indeed speed up the incorporation of fundamental information into stock prices, but the impacton market efficiency is greater in the case of upward, versus downward, forecast revisions, especiallyfor high R&D firms.4

4 While the basic argument presented here also applies to general information uncertainties, our study focuses on a sample ofR&D firms. The market anomaly related to differential R&D levels (measured as the market-value-scaled R&D expenditures) is wellestablished in the literature, so this provides a clear setting to examine the role of analyst forecasts for improving the informationalefficiency of stock prices. In contrast, we do not find a similar return anomaly associated with general information uncertainties(based on usual proxies such as the inverse of firm size, the forecast dispersion, the earnings quality as measured by Dechow andDichev (2002) or stock return volatilities) for the sample of firms whose forecast revisions can be computed.


Third, while the study does not directly speak to financial reporting practice on R&D activities,which remains a subject of debate (see, for example, Lev and Sougiannis, 1996; Skinner, 2008), it dem-onstrates the important role that analysts play in compensating for deficiencies in current reporting ofR&D activities. It would be useful for standard setters to think about policies that encourage compa-nies to disclose more information and assist analysts in forming forecasts. In this regard, our study cor-roborates with recent studies by Pandit et al. (2009), who show that the performance of R&D activitiesneed to be understood in conjunction with information about the productivity of R&D outlays, and byCiftci et al. (2008), who show that while ‘‘R&D leaders” are initially underpriced, mispricing can be cutin half for firms that disclose more information. Our study reinforces these studies’ findings by show-ing that analyst forecasts help to facilitate information efficiency and, to the extent disclosure im-proves analyst forecasts, it also improves market efficiency and mitigates mispricing.

The remainder of the paper is organized as follows. Section 2 develops our testing hypotheses. Sec-tion 3 describes the sample. Section 4 presents the empirical results, with robustness checks providedin Section 5. Section 6 concludes the paper.

2. Hypothesis development

In this section, we develop hypotheses to examine how analyst forecast revisions help to improvethe informational efficiency of the underlying firms’ stock prices. The hypotheses developed here con-cern how R&D intensity impacts upward and downward revisions differently in reflecting changes inexpected future earnings and resolving uncertainties. We explain that the informativeness of forecastrevisions is asymmetric between upward and downward revisions, with the degree of asymmetryincreasing in R&D intensity. We finally propose that asymmetric roles of upward and downward revi-sions also exist in reducing the return differentials between extreme low and high R&D portfolios.

According to McNichols and O’Brien (1997), financial analysts, who are driven by their own inter-ests, have a tendency not to issue forecasts that contain unfavorable information about a firm. Thisbehavior causes released forecasts as a whole to be optimistically biased, even though individual ana-lysts may have no intention of biasing their forecasts. Following Diether et al. (2002), the extent of thebias in disclosed forecasts should be greater when analysts have more divergent opinions, as will bethe case, for example, when uncertainties are greater about underlying earnings.5

To illustrate the positive relation between earnings uncertainty and the optimistic bias in reportedforecasts as induced by truncation of unfavorable opinions, consider a simple setting in which analystsmake forecasts at two dates, first at date 0 and then at date 1, of a firm’s earnings to be realized in afuture period. Let E(0) and E(1) be the true expectations at dates 0 and 1 of that future period’s earn-ings, respectively, and let F(0) and F(1) be the means of the forecasts actually disclosed at these dates.There are analysts with favorable opinions and analysts with unfavorable opinions, but those withopinions sufficiently unfavorable will not disclose their forecasts, thus inducing an upward bias in dis-closed forecasts.

As illustrated in Fig. 1, the extent of this bias increases with the level of R&D. In the figure, there isless uncertainty about firm 1’s earnings than about firm 2’s earnings, and the distribution of the fullset of (untruncated) forecasts is assumed to be unbiased for both firms. However, those forecasts thatare sufficiently unfavorable (say, below a certain level) are withheld, making the mean disclosed fore-casts to be shifted upward from the true mean. While this general phenomenon occurs for both firms,the optimistic bias is greater for firm 2 due to its larger earnings uncertainty and more truncation atthe low end of the forecast distribution.6

5 Das et al. (1998) and Lim (2001) also find that earnings estimates are more positively biased for firms with greater earningsvariability. They alternatively argue that, to facilitate information access to themanagement, analysts issue optimistic forecasts forfirms with more uncertain earnings (in order to please these firms). Our hypothesis development does not exclude this alternative,though we focus on the ‘‘truncation” argument. However, some basic issues are left unaddressed in this ‘‘strategic” forecastingargument such as (i) why firms necessarily prefer optimistic biases when they eventually need to talk them down to a beatablelevel, and (ii) why some analysts behave strategically whereas others do not (note that a substantial portion of individual analystforecasts actually exhibit pessimistic biases).

6 For simplicity, we assume the same cutoff point in the figure for the two firms concerning the decision to disclose (versusdepress) earnings forecasts, but it is not a necessary condition of the argument.

Fig. 1. Illustration of the relation between earnings uncertainty and the optimistic bias in disclosed forecast.


Thus, we may express the mean (disclosed) forecast at date 0 as F(0) = E(0) + a0RD, where a0 > 0 is aconstant and RD stands for the amount of R&D expenditure of a firm. Similarly, at date 1, we haveF(1) = E(1) + a1RD where a1 > 0 is a constant. From date 0 to 1, gradual resolution of earnings uncer-tainty with respect to the same amount of R&D expenditure suggests a0 > a1. Then, the observed fore-cast revision at t = 1 is Revision � F(1) � F(0) =[E(1) � E(0)] � a RD, where a � a0 � a1 > 0 is a constant.Thus, the observed forecast revision reflects the change in true earnings expectations, but the link be-tween them is intervened by a correction for the bias in previously disclosed forecasts. This leads toour first hypothesis regarding the correspondence between observed forecast revision and the changein true earnings expectation as follows:

H1. The forecast revision for an individual firm should reflect the change in true expectations aboutthe firm’s earnings performance, but it should also contain a negative bias-correction term whoseabsolute magnitude is positively related to the firm’s R&D level.

H1 suggests that (observed) upward revisions tend to under-represent the true increases in expectedearnings (due to the negative correction term), whereas downward revisions tend to over-represent thetrue declines in expected earnings. To see this point, assume that a RD in the above example is equal to0.2. When there is an upward forecast revision of 0.6 from date 0 to date 1, the true earnings expectationhas actually been revised upward by 0.8. On the other hand, when there is a downward revision of 0.6,the true earnings expectation has been revised downward by only 0.4. Furthermore, the extent of under/over-representation by forecast revisions is more severe for firms with high R&D expenditures becausethe bias (and hence the correction term over time, a RD) increases with the R&D level.

While H1 relates forecast revisions to changes in earnings expectations (the first moment), we nextexamine the impact of forecast revisions on earnings uncertainty (the second moment). When revisingthe forecasts of future earnings, analysts who follow the Bayes rule would take into account both thecontent and the precision of the new information they receive.7 Given the magnitude of new signalreceived, the more precise is the new signal received, the larger is the shift in beliefs about expected

7 If analysts’ earnings beliefs (x) and the analysts’ new information received (y) are distributed bivariate normal, then theanalysts’ posterior beliefs should follow a normal distribution with mean = lx + qxy[rx/ry](y � ly) and variance ¼ r2

x ð1� q2xyÞ.

Thus, the revision is equal to qxy ½rx=ry�ðy� lyÞ, which is increasing in the precision of the information (1/ry) and the magnitude ofinformation (y).


earnings away from the prior expectation and toward the signal (thus, a larger forecast revision), and thegreater is the reduction in earnings uncertainty.

If H1 holds, at a given amount of R&D expenditures, upward revisions imply larger changes in trueearnings expectations than downward revisions of equal (absolute) magnitudes. As in above numer-ical example, the mean forecast revision of +0.6 (�0.6) corresponds to a true expectation change of+0.8 (�0.4). One possible reason for triggering a larger change in expectation in the case of upwardrevisions (compared with the case of downward revisions) is that the underlying information receivedis more precise. Following this argument, there will be greater reductions in earnings uncertaintiesfollowing upward revisions than following downward revisions of the same magnitudes, and thisasymmetry should be exhibited mostly for firms with high levels of R&D expenditures. This leads toour second hypothesis.

H2. Given the magnitude of the new information received, upward revisions are associated withgreater reductions in earnings uncertainties than downward revisions, and this is so mainly forfirms with high R&D expenditures.

Our final hypothesis concerns the role of upward versus downward forecast revisions in mitigatingthe return anomaly related to R&D expenditures. Existing research has found that stock prices tend todrift following R&D activities and the extent of drift increases with the R&D intensity. For example, Levand Sougiannis (1996) document a significant association between a firm’s R&D capital and subse-quent stock returns, while Chan et al. (2001) find that a strategy of buying the highest R&D portfolioand selling the lowest R&D portfolio earns an average annual raw (abnormal) return of 11.08% (7.83%)over the subsequent three years. As the abnormal returns are driven largely by high R&D firms, Chanet al. (2001) posit that investors are slow to fully appreciate the cash-flow implications of R&D expen-ditures. In the context of this study, however, if upward revisions are related with greater changes intrue earnings expectations and resolve uncertainties more quickly than downward revisions, theyshould be more effective in incorporating future earnings news into stock prices and improving theprice efficiency with respect to R&D expenditures. This leads to our final hypothesis:

H3. Relative to downward revisions, upward revisions are followed by smaller return differentialsbetween high and low R&D firms.

3. Sample selection and variable definitions

3.1. Sample selection

Following the procedure of Chan et al. (2001), we identify R&D firms from seven science- and tech-nology-oriented industries that undertake substantial R&D activities. They are Drugs and Pharmaceu-ticals (Industry Classification Code: 283), Computers and Office Equipment (357), Electrical Equipment(excluding computers) (36), Transportation Equipment (37), Measuring Instruments (38), Communica-tions (48), and Computer Programming, Software and Services (737). The sample is comprised of firmswith available accounting data from the Compustat active and research file, one- and two-years- aheadearnings forecasts from the I/B/E/S detail file,8 and monthly stock returns from the CRSP database.

We identify 6641 firm-year observations (involving 1743 firms) with non-missing measures on R&Dexpenditures and forecast revisions. The samples for specific tests are further subject to data availabilityfor the necessary variables in the tests. Table 1 presents sample distributions by industry (Panel A) andby year (Panel B). Around 50% of the sample firms or firm-years come from two industries – electricalequipment excluding computers, and computer programming, software and services. There is also a mildclustering of sample firms in the later half of the sample. In Table 1, we also break down industry andyearly samples into downward and upward revisions and find that there are more instances of down-ward revisions than of upward revisions within industries and years, consistent with prior findings.

8 To reduce the impact of stale forecasts in the I/B/E/S summary file, we use the I/B/E/S detailed file to compute forecast revisions(see also Zhang, 2006).

Table 1Sample distributions.

SIC Industry # of firm-year % of firm-years # of firm-years with

Downwardrevisions

Upwardrevisions

Panel A. Industry distribution36 Electrical equipment, excluding computers 1790 26.95 1043 74737 Transportation equipment 551 8.30 316 23538 Measuring instruments 1039 15.65 639 40048 Communications 140 2.11 106 34283 Drugs and pharmaceuticals 965 14.53 598 367357 Computers and office equipment 805 12.12 430 375737 Computer programming, software, and services 1351 20.34 780 571

Total 6641 100 3912 2729

Year # of firm-years % of firm-years # of firm-years with

Downward revisions Upward revisions

Panel B. Year distribution1983 94 1.42 55 391984 159 2.39 113 461985 158 2.38 105 531986 174 2.62 91 831987 178 2.68 91 871988 197 2.97 127 701989 235 3.54 167 681990 241 3.63 160 811991 265 3.99 157 1081992 291 4.38 185 1061993 318 4.79 171 1471994 332 5.00 148 1841995 386 5.81 213 1731996 441 6.64 259 1821997 436 6.57 275 1611998 390 5.87 238 1521999 379 5.71 141 2382000 432 6.51 319 1132001 416 6.26 256 1602002 340 5.12 219 1212003 390 5.87 186 2042004 389 5.86 236 153

Total 6641 100 3912 2729

This table reports the industry and year distributions of the sample. Industry groupings are based on the two/three-digit SICcode. We select seven R&D intensive industries following Chan et al. (2001).


3.2. Definitions of variables

Following Chan et al. (2001) and Kothari et al. (2002), we measure R&D intensity in fiscal year t bythe R&D expenditures in that year (Compustat data item #46) scaled by equity market value,RND_MVEt, with equity market value computed as the number of shares outstanding (#25) timesthe stock price (#199) at the end of year t.9

9 Though Chan et al. (2001) find R&D-related return drifts with only market-value-adjusted R&D measure, other studies findreturn drifts with other measures as well; see Ciftci et al. (2008) for evidence on R&D-to-sales ratio and Chambers et al. (2002) onR&D capital. Chambers et al. (2002) also claim that they do not find a similar return drift using a market value adjusted capitalexpenditures, indicating that the denominator effect (loser and winner stocks) is not the main cause for the R&D return drift, andargue that a low market value of high R&D firms could occur either because of mispricing, which causes high R&D firms to bevalued too low (the denominator effect), or because of high risk, hence requiring a high discount rate. We obtain similar resultswhen using R&D capital, where R&D capital is equal to RDEXPt + 0.8(RDEXPt�1) + 0.6(RDEXPt�2) + 0.4(RDEXPt�3) + 0.2(RDEXPt�4)and RDEXP is R&D expenses in each year.


We match year t R&D expenditures with the forecasts of year t + 1 earnings per share. The forecastrevision, Rev_Prct+1, is equal to the mean forecast of year t + 1 earnings outstanding in the 4th monthafter the end of year t minus the mean forecast of year t + 1 earnings outstanding one month beforethe year t earnings announcement, scaled by the stock price at the end of fiscal year t. A dummy var-iable UPt+1 is used to represent the direction of the revision, which is set to 1 if Rev_Prct+1 is non-neg-ative and 0 otherwise.10

The forecast error, FE_Prct+1, is the actual earnings per share (as reported by I/B/E/S) for year t + 1minus the mean earnings forecast issued in the 4th month after the end of fiscal year t (i.e.�8 month relative to the forecast period end), scaled by the stock price at the end of fiscal year t.In our later empirical analyses, we compute forecast revisions and forecast errors at various forecast-ing points over the annual forecast horizon (�8, �5, �3 and 1 month relative to the forecast periodend).

The change in true earnings expectations is not observable. To the extent that analysts’ earningsexpectation converges to the reported earnings, we proxy it by the change in realized earnings,ChgNI_MVEt+1, computed as the net income for year t + 1 (#18) minus the net income for year t dividedby equity market value at the end of year t. There are other papers that use realized earnings perfor-mance to proxy for the expected earnings (e.g., Collins et al., 1994; D’Mello and Shroff, 2000).

We proxy the change in earnings uncertainty facing analysts after, versus before, the forecast revi-sion by the ratio of the forecast dispersion before to that after the forecast revision, denoted as Dis-p_Ratiot+1, with the pre-revision dispersion measured as the standard deviation of individualanalyst earnings forecasts available over a 3-month period before the revision in month �8 relativeto the end of year t + 1 (i.e., months �9, �10 and �11), and the post-revision dispersion as that samemeasure over a 3-month period after the revision (i.e., months �7, �6 and �5).11 In our regressionsbelow, the natural logarithm of this ratio, denoted as Log(Disp_Ratio)t+1, is used.

Raw_Rett+1 is the monthly raw return, and ARett+1 is the monthly abnormal return computed as theraw return minus the return on a size, book-to-market and prior-year return matched portfolio; bothare computed following the calendar month approach as in Fama (1998). In the analysis, we considerholding periods of 6, 9 and 12 months that start from the July of year t + 1.

In following empirical analyses, we delete the top and the bottom 1% extreme values for the (con-tinuous) variables used. The results are similar if we winsorize these extreme values.

4. Empirical results

4.1. Descriptive statistics

Panel A of Table 2 reports the summary statistics for the main variables, which are the average val-ues of the descriptive statistics in yearly samples. The forecast errors and revisions show a ‘‘tail asym-metry” as detected in Abarbanell and Lehavy (2003); the absolute magnitudes of the 1st percentileforecast errors (FE_Prct+1) and revisions (Rev_Prct+1) are much larger than those of the 99th percentile.The mean and median of Rev_Prct+1 are negative, �0.0074 and �0.0009, respectively, indicating a gen-eral tendency to revise earnings forecasts downward. The mean and median values of Disp_Ratiot+1 aregreater than one (1.4619 and 1.0720, respectively), indicating that on average the forecast dispersiondecreases over time.12 The summary statistics indicate that the distributions of Rev_Prct+1, FE_Prct+1, Dis-p_Ratiot+1 and RND_MVEt are highly skewed.

10 Zero revisions are reiterations of prior forecasts. Excluding these observations does not change our results. The results areunchanged if we alternatively define UPt+1 to be 1 for positive revisions and zero for non-positive revisions.

11 We also calculate dispersions over the two-month period both before and after the revision and find our results to be similar(but the sample size is smaller with the use of a shorter interval for calculating dispersions). In cases where an analyst issuesmultiple forecasts over the 3-month period being considered, we only retain the latest earnings forecast. We require at least threeearnings forecasts in the measurement interval for computing Disp_Ratiot+1.

12 To perform the logarithm transformation, we drop 19 observations with Disp_Ratiot+1 having a zero value (0.4% of allobservations with an available value for Disp_Ratiot+1). In unreported results of the subsequent regressions, we re-do the analysisafter deleting the top 0.5% values of Disp_Ratiot+1 (so as to balance with the deletion of zero values at the bottom end) and findresults unchanged.

Table 2Descriptive statistics.

RND_MVEt Log(RND_MVE)t Rev_Prct+1 FE_Prct+1 ChgNI_MVEt+1 Disp_Ratiot+1 Log(Disp_Ratio)t+1 ARett+1

Panel A: Summary statisticsMean 0.3040 �3.1139 �0.0074 �0.0121 �0.0058 1.4619 0.0198 0.0041Std. err. 0.1526 1.2250 0.0483 0.4189 0.2850 1.8323 0.8596

0.04591% 0.0019 �6.2675 �0.1436 �0.2449 �0.6350 0.0707 �2.3902 �0.115225% 0.0219 �3.8205 �0.0109 �0.0176 �0.0272 0.6346 �0.4512 �0.019450% 0.0451 �3.0993 �0.0009 �0.0016 0.0051 1.0720 0.0741 �0.001975% 0.0914 �2.3928 0.0027 0.0035 0.0206 1.7051 0.5353 0.024499% 1.3116 0.2713 0.0692 0.0785 0.4200 8.2462 2.1098 0.1441Obs. 6641. 6641 6641 6641 6641 4862 4840 6460

Log(RND_MVE)t Rev_Prct+1 FE_Prct+1 ChgNI_MVEt+1 Log(Disp_Ratio)t+1 ARett+1

Panel B: Pearson (below the diagonal) and Spearman (above the diagonal) correlationsLog(RND_MVE)t �0.1466 �0.0906 0.0463 0.0297 0.0389Rev_Prct+1 �0.1740 0.3058 0.3909 �0.0104# �0.0072#

FE_Prct+1 �0.1589 0.2974 0.4827 0.1089 0.1382ChgNI_MVEt+1 0.0147# 0.3407 0.4375 0.1017 0.0856Log(Disp_Ratio)t+1 0.0369 �0.0063# 0.1118 0.0967 �0.0114#

ARett+1 0.0533 �0.0344 0.0875 0.0702 �0.0085#

This table reports summary statistics of main variables (Panel A) and their correlations (Panel B). RND_MVEt is the market-value-scaled R&D expenditures as of the end of year t (#46/(#25 � #199)). Log(RND_MVE)t is the natural logarithm ofRND_MVEt. Rev_Prct+1 is the mean forecast of year t + 1 earnings outstanding in the 4th month after the end of year t minus themean forecast of year t + 1 earnings outstanding one month before the year t earnings announcement, scaled by the stock priceat the end of fiscal year t. FE_Prct+1 is the forecast error computed as the year t + 1 actual earnings per share minus the meanearnings forecast in the 4th month after the end of year t, scaled by the stock price at the end of fiscal year t. ChgNI_MVEt+1 is thechange in income, computed as the net income (#18) in year t + 1 minus the net income in year t, scaled by market value ofequity at the end of year t. Disp_Ratiot+1 is the ratio of the pre-revision forecast dispersion to the post-revision forecastdispersion, with the pre-revision dispersion measured as the standard deviation of individual analyst earnings forecastsavailable over a 3-month period before the revision in month-8 relative to the end of year t + 1 (i.e., months �9, �10 and �11)and the post-revision dispersion as that over a 3-month period after the revision (i.e., months �7, �6 and �5). Log(Disp_Ratio)t+1

is the natural logarithm of Disp_Ratiot+1. ARett+1 is the monthly abnormal return (adjusted for size, book-to-market and prior-year return) from a 12-month holding period beginning from the July of year t + 1. The correlation coefficients in Panel B are allsignificant at least at the 0.1 level except those marked with #.


Panel B of Table 2 presents the correlations among the variables. We find that the R&D level (mea-sured as Log(RND_MVE)t) is significantly positively correlated with subsequent monthly abnormal re-turns (ARett+1), indicating the presence of upward price drifting that increases with R&D intensity,consistent with prior studies such as Chan et al. (2001). Log(RND_MVE)t has a significantly negativecorrelation with Rev_Prct+1, consistent with our conjectured trend of downward revisions that is morepronounced for higher R&D firms. The forecast revision is significantly positively correlated with earn-ings changes ChgNI_MVEt+1, indicating that the former indeed conveys information about changes inearnings performance. However, the correlation between Rev_Prct+1 and Log(Disp_Ratio)t+1 is less clearin our overall sample; both the Pearson and Spearman correlations between the two are insignificant.

4.2. R&D intensity and the relation between forecast revisions and changes in expected earnings (H1)

Before testing H1 with regression analysis, we first confirm the existence of analysts’ forecast opti-mism and downward biases in revisions. We compute the forecast errors and forecast revisions foreach R&D quartile at months �8, �5, �2, and + 1. The forecast error ðFE Prcn

tþ1Þ is calculated as the ac-tual earnings per share for year t + 1 minus the mean earnings forecast in month n, scaled by the stockprice at the end of year t (n = �8, �5, �2, +1), and Rev Prcn

tþ1 is computed as the mean forecast of yeart + 1 earnings outstanding at month n minus the mean forecast of year t + 1 earnings outstanding atone month before the year t earnings announcement, scaled by the stock price at the end of year t.

The (untabulated) Rev Prcntþ1 and FE Prcn

tþ1 are plotted in Figs. 2 and 3. They show that, on average,analysts’ earnings forecasts are optimistic (at least at the 0.1 level) at all the above mentioned

-0.0300

-0.0250

-0.0200

-0.0150

-0.0100

-0.0050

0.0000

-8 -7 -6 -5 -4 -3 -2 -1 0 1

month relative to the end of forecast period

fore

cast

rev

isio

ns

rk_rnd_mve=lowest

rk_rnd_mve=2

rk_rnd_mve=3

rk_rnd_mve=highest

Fig. 2. Mean values of cumulative price-scaled forecast revisions at various months prior to the end of the forecast horizon. Atthe end of each year t, firms are sorted into quartiles (Rk_RND_MVEt) based on the magnitude of RND_MVEt.. The forecastrevision ðRev Prcn

tþ1Þ is calculated as the mean forecast of year t + 1 earnings available in the nth month (n = �8, �7, . . . , 1)relative to the end of fiscal year t + 1, minus the mean forecast of year t + 1 earnings available at one month before the year tearnings announcement, deflated by the stock price at the end of fiscal year t.

-0.0250

-0.0200

-0.0150

-0.0100

-0.0050

0.0000

-8 -7 -6 -5 -4 -3 -2 -1 0 1

month relative to the end of forecast period

fore

cast

err

ors

rk_rnd_mve=lowest

rk_rnd_mve=2

rk_rnd_mve=3

rk_rnd_mve=highest

Fig. 3. Mean values of price-scaled forecast errors at various months prior to the end of the forecast horizon. At the end of eachyear t, firms are sorted into quartiles (Rk_RND_MVEt) based on the magnitude of RND_MVEt. The forecast error ðFE Prcn

tþ1Þ iscalculated as the actual earnings per share for year t + 1 minus the mean forecast available at the nth month (n = �8, �7, . . . , 1)relative to the end of fiscal year t + 1, deflated by the stock price at the end of fiscal year t.


forecasting points (except month + 1), and they are most optimistic in the highest R&D quartile. Foreach R&D quartile, at each forecasting point, the mean forecast revisions are significantly negative(at least at the 0.1 level), and the extent of downward revisions is positively related to the R&D inten-sity. Thus, in the absence of controlling variables, firms with more R&D expenditures experience morenegative forecast revisions characterized by diminishing forecasting optimism over time.

We now test H1 on the relation between forecast revisions and changes in expected earnings per-formance. According to H1, forecast revisions should indicate changes in true earnings expectations,but, due to biases in disclosed forecasts, the two variables are misaligned, especially for high R&Dfirms. As changes in true earnings expectations for year t + 1 earnings (from one month before the yeart earnings announcement to the 4th month after the end of year t) are not observable, we use changesin realized earnings between year t and year t + 1 (ChgNI_MVEt+1) as a proxy. Our regression specifica-tion for testing H1 is as follows:

13 To myear t +unchan

14 In oRevt+1,

15 In athe conunchan


Rev Prctþ1 ¼ a0 þ a1R&DIntensityt þ a2ChgNI MVEtþ1 þX

bjControlj þ utþ1; ð1Þ

The time interval covered by ChgNI_MVEt+1 is much wider than the interval for computing our fore-cast revisions. This means that our proxy for expectation changes is likely to contain considerable noise(and other unwanted information). If it is uncorrelated with the other variables in the model, the noisewill make it more difficult to find a significant coefficient on ChgNI_MVEt+1 in the regression.13 We usetwo alternative measures of R&D intensity (R&DIntensity) for regressions (1): Log(RND_MVE)t andRk_RND_MVEt, which are the natural logarithm and the quartile ranking of RND_MVE, respectively. Theuse of these transformed R&D measures mitigates the issue of skewed distributions. To facilitate interpre-tation, we proportionally adjust the R&D ranking so that its value has a range between 0 and 1. We alsoinclude a number of control variables in regression (1), which are explained below.

Panel A of Table 3 reports the regression results based on the overall sample. When forecast revi-sion is regressed on R&D intensity measures only (columns (a) and (b)), we find a highly significantand negative coefficient on either Log(RND_MVE)t (coef. = �0.0028, t = �4.31) or Rk_RND_MVEt

(coef. = �0.0096, t = �4.87), indicating a negative correlation between forecast revision and R&Dexpenditures.

When we include ChgNI_MVEt+1 in the regression, it has a positive coefficient of 0.0931 in column(c) and 0.0928 in column (d), both significant at the 0.01 level, confirming that forecast revisions in-deed reflect information about changes in earnings performance. At the same time, the coefficient onLog(RND_MVE)t is �0.0028 (t = �4.97) and that on Rk_RND_MVEt is �0.0090 (t = �5.10), both signifi-cant at the 0.01 level. The findings are consistent with H1 that, while useful for conveying changesin expected earnings, forecast revisions are also influenced by a downward bias-correction term themagnitude of which is positively related to the R&D intensity.14

We also run regression (1) with a number of control variables (the sample size is substantially re-duced due to the additional data requirements) as follows. UNI_MVEt is the earnings information arriv-ing during the forecast revision interval, computed as the reported earnings of year t minus the latestanalyst forecast of this earnings available before the earnings announcement scaled by the market va-lue of equity at the end of year t � 1. Firm size (Log(MVE)t, the natural logarithm of the market value ofequity) and the book-to-market ratio (Log(BM)t, the natural logarithm of the book-to-market ratio ofequity) are used to control for the overall firm information environment and expected growth. MMTt

(the 12-month compounded return ending one month before the forecast revision month, adjusted forsize and book-to-market factors) controls for the possibility that analysts may extract informationfrom market prices beyond financial data (Amir et al., 2003). Following Lev and Thiagarajan (1993),Abarbanell and Bushee (1997) and Amir et al. (2003), we also include the following fundamental vari-ables that may be related with forecast revisions: INVt (the percentage annual change in inventory(#78 or #3) minus the percentage change in sales (#12)), ARt (the percentage annual change in ac-counts receivables (#2) minus the percentage change in sales), GMt (the percentage annual changein sales minus the percentage change in gross margin (#12 � #41)), SGAt (the percentage annualchange in selling and administrative expenses (#189) minus the percentage change in sales), and ETRt

(the change in the annual effective tax rate (#16/(#170 + #65)) times pre-tax income).As shown in columns (e) through (f) in Panel A of Table 3, the qualitative results remain unchanged

after including these control variables. Forecast revisions are positively related to changes in earningsexpectation and negatively related to the R&D intensity, both significant at the 0.01 level. The earningsnews disclosed during the forecast revision interval is significantly positive, indicating that analyststend to revise earnings forecast for next period upward when observing a concurrent positive earningssurprise.15

itigate this problem, we also use the change in quarterly earnings (computed as quarterly earnings in the fourth quarter of1 minus that of the third quarter scaled by market value at the end of year t) as a proxy. The results are qualitatively

ged.rder to avoid potential spurious correlations caused by a common deflator, we also use the un-scaled forecast revisions,

as the dependent variable. The results are qualitatively unchanged.ddition to the Fama-MacBeth procedure, we also perform OLS regressions for the pooled sample (both with and withouttrol variables), with t statistics adjusted for heteroscedasticity and clustering at the firm level. The results are qualitativelyged.

Table 3R&D intensity and the relation between forecast revisions and fundamental information.

(a) (b) (c) (d) (e) (f)

Panel A: Fama-MacBeth regression results from overall sampleIntercept �0.0151 (�5.02) �0.0017 (�3.50) �0.0140 (�5.87) �0.0010 (�2.27) �0.0283 (�4.40) �0.0146 (�2.55)Log(RND_MVE)t �0.0028 (�4.31) �0.0028 (�4.97) �0.0031 (�4.88)Rk_RND_MVEt �0.0096 (�4.87) �0.0090 (�5.10) �0.0083 (�4.78)ChgNI_MVEt+1 0.0931 (9.12) 0.0928 (9.14) 0.1098 (10.38) 0.1105 (10.63)UNI_MVEt 0.0895 (4.78) 0.0901 (4.79)Log(MVE)t 0.0012 (4.42) 0.0011 (4.37)Log(BM)t 0.0022 (2.38) 0.0022 (2.20)MMTt 0.0027 (2.41) 0.0027 (2.51)INVt �0.0022 (�2.26) �0.0022 (�2.27)ARt 0.0024 (1.41) 0.0020 (1.19)GMt 0.0027 (0.67) 0.0020 (0.49)SGAt 0.0077 (1.42) 0.0077 (1.40)ETRt 0.0517 (1.31) 0.0483 (1.24)Ave. obs. 301.8636 301.8636 301.8636 301.8636 100.4090 100.4090Ave. adj. R2 0.0264 0.0229 0.1390 0.1341 0.2745 0.2720

Earnings change direction

Positive Negative Positive Negative(a) (b) (c) (d)

Panel B: Regression results from positive (including zeros) and negative earnings change groupsIntercept �0.021 (�2.20) �0.0181 (�0.82) �0.0054 (�0.71) 0.0082 (0.49)Log(RND_MVE)t �0.0028 (�2.54) �0.0058 (�4.00)Rk_RND_MVEt �0.0068 (�2.26) �0.0134 (�3.89)ChgNI_MVEt+1 0.1024 (3.75) 0.126 (3.87) 0.1242 (3.89) 0.1333 (3.54)UNI_MVEt 0.0852 (2.98) 0.0564 (1.62) 0.0844 (2.89) 0.0509 (1.41)Log(MVE)t 0.0015 (3.39) 0.0014 (2.21) 0.0014 (3.18) 0.0014 (2.21)Log(BM)t 0.0015 (1.15) �0.001 (�0.32) 0.0008 (0.60) �0.0013 (�0.44)MMTt 0.001 (0.49) 0.0016 (0.49) 0.0013 (0.64) 0.0017 (0.50)INVt �0.0004 (�0.19) �0.0033 (�0.77) 0 (0.02) �0.0036 (�0.85)ARt 0 (0.01) 0.0065 (1.44) 0.0003 (0.08) 0.0062 (1.38)GMt �0.0024 (�0.32) �0.0252 (�1.75) �0.0052 (�0.62) �0.0287 (�1.93)SGAt �0.001 (�0.18) 0.0062 (0.58) �0.0022 (�0.40) 0.0047 (0.44)ETRt �0.0089 (�0.14) �0.0032 (�0.04) �0.0065 (�0.11) �0.0131 (�0.19)

Ave. obs. 60.2272 40.1817 60.2272 40.1817Ave. adj. R2 0.2929 0.4150 0.2882 0.413

This table reports the results of the following regression: Rev Prctþ1 ¼ a0 þ a1R& DIntensityt þ a2ChgNI MVEtþ1 þP

bjControlj þ utþ1 : ð1Þ R&D intensity is measured by either Log(RND_MVE)t or Rk_RND_MVEt, whereLog(RND_MVE)t is the natural logarithm of market-value-scaled R&D expenditures for year t, and Rk_RND_MVEt is its annual quartile ranking. We adjust the quartile rankings proportionally so that its value ranges between 0and 1. Rev_Prct+1 is the mean forecast of year t + 1 earnings outstanding in the 4th month after the end of year t minus the mean forecast of year t + 1 earnings outstanding one month before the year t earningsannouncement, scaled by the stock price at the end of fiscal year t. ChgNI_MVEt+1 is the net income in year t + 1 minus the net income in year t, scaled by the market value of equity at the end of year t. Panel A reports theregression results on the overall sample and Panel B reports the results on the sub-samples of positive and negative earnings changes.The control variables include UNI_MVEt (unexpected earnings disclosed during the revision interval), Log(MVE)t (the natural logarithm of the market value of equity), Log(BM)t (the natural logarithm of the book-to-marketratio), MMTt (the 12-month compounded return ending one month before the forecast revision month, adjusted for the size- and book-to-market factors), INVt (the percentage annual change in inventory (#78 or #3) minusthe percentage change in sales (#12)), ARt (the percentage annual change in accounts receivable (#2) minus the percentage change in sales), GMt (the percentage annual change in sales minus the percentage change in grossmargin (#12-#41)), SGAt (the percentage annual change in selling and administrative expenses (#189) minus the percentage change in sales), ETRt (the change in the annual effective tax rate (#16/(#170 + #65)) times pre-tax income). All the control variables are computed as of the end of year t except MMTt. The coefficients and t statistics (in parentheses) are calculated following Fama and MacBeth (1973). Ave. obs. is the average number ofobservations in yearly regressions. Ave. adj. R2 is the average of adj. R2s from yearly regressions.

12Y.H

uang,G.Zhang

/J.Account.Public

Policy30

(2011)1–

21


To rule out the possibility that asymmetric informational roles of upward and downward revisionsare caused by similar asymmetries in earnings expectation changes within the overall sample, we par-tition the sample into positive (including zeros) and negative earnings changes and then run regres-sion (1) separately for the two groups. The results are reported in Panel B of Table 3. We find that, inboth partitions, R&Dintensityt has a significantly negative coefficient, indicating that regardless of thedirection of the underlying earnings change, observed forecast revisions are intervened with a nega-tive bias in reflecting expected earnings changes, and that this bias increases with the level of R&D.16

The above findings suggest that upward and downward forecast revisions should not be viewed ashaving symmetric roles in its association with changes in expected earnings. Generally speaking, be-cause of the negative bias-correction term, upward revisions understate the increases in true earningsexpectations, whereas downward revisions overstate the decreases in earnings expectations. Further-more, the extent of the misalignment between forecast revisions and changes in true earnings expec-tations is more severe for firms with higher R&D expenditures.

4.3. Changes in earnings uncertainties associated with upward versus downward revisions (H2)

We now test whether upward and downward revisions also have asymmetric impacts on earningsuncertainties. We use analyst forecast dispersions to proxy for earnings uncertainties. According toH2, if upward revision (of similar magnitude as the downward revision) is indeed trigged by more pre-cise information, there should be a greater reduction of earnings uncertainty following upward revi-sions than following downward revisions of equal magnitudes, especially for high R&D firms. Thefollowing regression specification is used to test H2:

16 Wenegativ

LogðDisp RatioÞtþ1 ¼ a0 þ a1AbsðRev PrcÞtþ1 þ a2UPtþ1 þ a3R&DIntensityt þ a4UPtþ1

� R&DIntensityt þ utþ1; ð2Þ

where Abs(Rev_Prc)t+1 is the absolute value of Rev_Prct+1, which is included to control for the informa-tion content conveyed by the magnitude of a revision, and Log(Disp_Ratio)t+1 is the logarithm of Dis-p_Ratiot+1. Disp_Ratiot+1 represents the proportional change in earnings uncertainty after the revisionversus before the revision; a ratio greater than 1 suggests a reduction in earnings uncertainty, and viceversa. Following H2, we expect the total effect of UPt+1 (a2 + a4*R&DIntensity) to be positive, and moreimportantly, the coefficient on the interaction term to be positive, a4 > 0.

Table 4 shows the result of regression (2). Based on the Fama–MacBeth procedure, when R&Dintensity is measured by Log(RND_MVE)t, we obtain in column (a) a coefficient of 0.2309 (t = 2.53)on UPt+1 and a coefficient of 0.0617 (t = 2.35) on UPt+1*Log(RND_MVE)t, both significant at the 0.05 le-vel, indicating that upward revisions are associated with a greater reduction of earnings uncertaintiesrelative to downward revisions, and this is so mainly for firms with high R&D expenditures. Whenevaluated at 75 percentile of R&D expenditures (�2.3928), the total effect of UPt+1 (a2 + a4*Log(RND_MVE)t) is 0.0833 (t = 2.46). On the other hand, the effect is insignificant when evaluated withmedian value of Log(RND_MVE) (�3.0993).

When R&D intensity is measured by Rk_RND_MVEt, we obtain in column (b) a coefficient of�0.0590 (t = �1.24) on UPt+1 and a coefficient of 0.1773 (t = 2.34) on UPt+1*Rk_RND_MVEt. As the valueof Rk_RND_MVEt is normalized to be between 0 (lowest) and 1 (highest), the total effect of UPt+1

(a2 + a4 � Rk_RND_MVEt) is significantly positive for the two highest R&D quartiles. For example, forthe highest R&D quartile (Rk_RND_MVEt = 1), the combined coefficient on UPt+1 is 0.1183 (t = 2.18), sig-nificant at the 0.05 level. However, for the two lower R&D quartiles, the incremental reduction in earn-ings uncertainty following upward revisions (over that following downward revisions) is notstatistically significant.

Our results are qualitatively unchanged if we employ pooled regressions adjusted for heterosced-asticity and clustering at the firm level, as shown in columns (c) and (d). In a robustness check, we alsouse the earnings news revealed during the revision interval (UNI_MVEt) as an addition control of new

also partition the sample into upward and downward forecast revisions to run separate regressions and similarly find ae intercept that increases with the level of R&D intensity.

Table 4R&D intensity and uncertainty resolutions associated with upward and downward forecast revisions.

Fama–MacBeth regressions Pooled regressions

(a) (b) (c) (d)

Intercept �0.0903 (�1.51) 0.0590 (1.60) �0.1301 (�1.03) �0.1627 (�1.93)Abs(Rev_Prc)t+1 0.8643 (1.07) 0.9337 (1.19) 1.7763 (3.46) 1.9463 (3.79)UPt+1 0.2309 (2.53) �0.0590 (�1.24) 0.3914 (3.93) �0.1005 (�1.39)Log(RND_MVE)t �0.0326 (�2.08) 0.0128 (0.39)Rk_RND_MVEt �0.0970 (�2.21) 0.0101 (0.14)UPt+1*Log(RND_MVE)t 0.0617 (2.35) 0.1211 (3.92)UPt+1*Rk_RND_MVEt 0.1773 (2.34) 0.2121 (2.58)

Year fixed-effect Yes YesFirm fixed-effect Yes Yes

Ave. obs./obs. 220.0000 220.0000 4840 4840Ave. adj. R2/adj. R2 0.0086 0.0091 0.3483 0.3449

This table reports changes in earnings uncertainty (proxied by the dispersion of analyst forecasts) associated with upward anddownward forecast revisions by R&D quartiles. The regression specification is as follows, LogðDisp RatioÞtþ1 ¼ a0þa1AbsðRev PrcÞt þ 1þ a2UPtþ1 þ a3R&DIntensityt þ a4UPtþ1 � R&DIntensityt þ utþ1; ð2Þ where Disp_Ratiot+1 is the ratio of thepre-revision forecast dispersion to the post-revision forecast dispersion, with the pre-revision dispersion measured as thestandard deviation of individual analyst earnings forecasts available over a 3-month period before the revision in month-8relative to the end of year t + 1 (i.e., months �9, �10 and �11) and the post-revision dispersion as that over a 3-month periodafter the revision (i.e., months �7, �6 and �5). Log(Disp_Ratio)t+1 is the natural logarithm of Disp_Ratiot+1. Abs(Rev_Prc)t+1 is theabsolute value of Rev_Prct+1, UPt+1 is a binary variable equal to 1 for a non-negative Rev_Prct+1 and 0 for a negative Rev_Prct+1, andR&D intensity is measured by either Log(RND_MVE)t (the natural logarithm of market-value-adjusted R&D expenditures for yeart) or Rk_RND_MVEt (the annual R&D quartile ranking). We adjust the quartile rankings proportionally so that its value rangesbetween 0 and 1.In columns (a) and (b), the coefficients and t statistics are computed with the Fama and MacBeth (1973) procedure. Ave. obs. isthe average number of observations in yearly regressions. Ave. adj. R2 is the average Adj. R2 from yearly regressions. Columns (c)and (d) are pooled regression results, with the t statistics (in parentheses) adjusted for heteroscedasticity and clustering at thefirm level.


information received by analysts and find the results qualitatively unchanged. Overall, the above re-sults are generally consistent with H2, that upward revisions are associated with a greater resolutionof earnings uncertainty mainly for high R&D firms.

4.4. R&D-related return spread conditional on upward versus downward forecast fevisions (H3)

4.4.1. Results without controlling for pre-revision earnings uncertaintiesWe now test H3 concerning the impact of analyst forecast revisions on stock-price drifts related to

R&D expenditures. Prior studies find that stock prices drift following R&D expenditures and there aresignificant return differentials between firms with high and low R&D expenditures, suggesting thatinvestors are slow to learn the economic consequences of R&D activities. A pertinent issue in our con-text is to what extent analyst forecast revisions help to mitigate R&D-related price drifts. Given theabove findings that upward and downward revisions have differential usefulness for conveying funda-mental information and reducing uncertainties, we expect them to have asymmetric roles in mitigat-ing price drifts as well.

To test H3, we divide the sample firms into quartiles based on prior-year R&D intensity and thencalculate return differentials between firms in the highest R&D quartile and those in the lowest quar-tile. Measurement of abnormal returns requires a benchmark asset pricing model. We use two alter-native proxies for abnormal returns, as explained below.

The first proxy for abnormal returns is monthly returns, adjusted for size, book-to-market andprior-year return, from a calendar portfolio approach of Fama (1998), denoted ARett+1. For reference,we also report the results on Raw_Rett+1, the monthly raw returns from the same portfolio. This ap-proach lines up firms by calendar time instead of fiscal time, thereby eliminating the look-ahead biasin R&D rankings. The procedure for forming R&D-based portfolio is as follows: at June of each year


t + 1, we sort firms into quartiles by the magnitude of RND_MVEt as of the latest fiscal year (with aminimum 6-month lag between the fiscal year end and the portfolio formation month) and then inde-pendently assign firms to two sub-samples based on the direction of forecast revisions. For each R&Dfirm in a month, the adjusted return is the raw return minus that of a control portfolio (out of 5*5*5portfolios) and the matching is on the basis of market value at June of year t + 1, book-to-market atDecember of year t and momentum return in the past 11 months. Raw_Rett+1 and ARett+1 are the aver-age monthly raw return and adjusted return computed from July of year t + 1 to the end of the holdingperiods (6, 9 or 12 months). The hedge return equals Raw_Rett+1 (ARett+1) in the highest R&D quartileminus that in the lowest R&D quintile.

Our second proxy for abnormal returns, denoted Alphat+1, is the intercept of the following factormodel:

17 Theconvenintensivreturns(Berk e

Rpt � Rft ¼ a0 þ a1MKTRFt þ a2SMBt þ a3HMLt þ a4UMDt þ a5WMLt þ et ð3Þ

where Rpt� Rft is the monthly return on investment portfolio p (the portfolios partitioned on the basisof R&D intensity quartiles and the directions of forecast revisions) in excess of the T-bill rate in montht; MKTRFt is the return on the market portfolio in excess of the T-bill rate; SMBt and HMLt are the re-turns on the Fama and French (1993) factor-mimicking portfolios for size and book-to-market; UMDt

is the return difference in month t between past winners and losers, with winners (losers) defined asthe top (bottom) quintile of stocks ranked by past returns over the period from 7 months to 1 monthbefore the portfolio formation (Carhart, 1997) and WMLt is the return difference in month t betweenpast winners and losers, with winners (losers) defined as the top (bottom) quintile of stocks ranked bypast returns over the period from 60 months to 12 months before the portfolio formation. We calcu-late the monthly portfolio return (Rpt� Rft) from July of year t + 1 to the end of holding periods (either6, 9 or12 months), match it with the five factor returns, and then estimate Eq. (3) separately for eachportfolio within the sample period to obtain intercept (Alphat+1). The hedge return is calculated asAlphat+1 in the highest R&D quartile minus that in the lowest R&D quintile.

Table 5 reports returns on R&D-based hedge portfolios (buying the highest R&D-quartile and sell-ing the lowest R&D-quartile), both in the pooled sample and in sub-samples conditional on the direc-tion of forecast revisions. In the pooled sample (combining both upward and downward revisions), thereturns on the hedge portfolio are significantly positive over the 6-, 9- and 12-month investment peri-ods, suggesting delays in price reactions to past R&D expenditures. The average monthly abnormal re-turn (ARett+1) on the hedge portfolio is 0.85% (t = 2.32) over the 6-month period, 1.13% (t = 3.78) overthe 9-month period, and 0.80% (t = 3.19) over the 12-month period. The abnormal returns based onfactor model (3) give similar results; the alphas are 1.33% (t = 3.37) over the 6-month period, 1.16%(t = 3.61) over the 9-month period, and 0.75% (t = 2.75) over the 12-month period. These results areconsistent with the findings of Chan et al. (2001).17

However, after partitioning the sample into upward- and downward-revision sub-samples, we findthat significant abnormal returns on the hedge portfolio exist only following downward revisions, notfollowing upward revisions. As shown in Table 5, in the downward-revision subsample, the hedgeportfolio earns significant abnormal returns over all three investment periods. The average adjustedmonthly return on the hedge portfolio (ARett+1) is 1.53% (t = 3.79) over the 6-month period, 1.55%(t = 4.87) over the 9-month period, and 1.20% (t = 4.34) over the 12-month period; similarly, theabnormal returns from the factor model are significantly positive for the 6-month period(Alphat+1 = 2.21%, t = 4.82), 9-month period (Alphat+1 = 1.48%, t = 4.00), and the 12-month period(Alphat+1 = 1.19%, t = 3.78). In contrast, in the upward-revision subsample, the abnormal return onthe hedge portfolio is not statistically significant for any of the three investment periods considered,whether we use the adjusted returns or the abnormal returns implied by the factor model.

fact that the results on ARett+1 and Alphat+1 are similar provides justification for using different risk-adjustment from thetionally accepted risk factors, as is consistent with several studies arguing that the conventional risk adjustment for R&De firms are insufficient (Skinner, 2008; Chambers et al., 2002). We also notice that the differences between abnormaland raw returns are mild, suggesting that the traditional risk factors may not explain a large portion of total price changes

t al., 2004).

Table 5Abnormal returns on the R&D-based hedge portfolio without controlling for the pre-revision forecast dispersion.

Sample Holding period

Six-month Nine-month 12-month

Raw_Rett+1 ARett+1 Alphat+1 Raw_Rett+1 ARett+1 Alphat+1 Raw_Rett+1 ARett+1 Alphat+1

Pooled sample 0.0098 (2.41) 0.0085 (2.32) 0.0133 (3.37) 0.0139 (4.02) 0.0113 (3.78) 0.0116 (3.61) 0.0107 (3.67) 0.0080 (3.19) 0.0075 (2.75)Down 0.0182 (3.98) 0.0153 (3.79) 0.0221 (4.82) 0.0189 (5.12) 0.0155 (4.87) 0.0148 (4.00) 0.0153 (4.81) 0.0120 (4.34) 0.0119 (3.78)Up �0.0016 (�0.31) �0.0018 (�0.39) 0.0013 (0.25) 0.0073 (1.69) 0.0052 (1.33) 0.0052 (1.23) 0.0053 (1.46) 0.0027 (0.83) 0.0016 (0.44)Down–up 0.0197 (4.13) 0.0171 (3.86) 0.0208 (4.25) 0.0116 (2.92) 0.0102 (2.70) 0.0096 (2.18) 0.0100 (2.93) 0.0093 (2.82) 0.0103 (2.72)

This table reports abnormal returns on the R&D-based hedge portfolio, for the pooled sample results (which duplicates the analysis of Chan et al. 2001) and for sub-samples with upwardand downward revisions. We sort stocks at June of each year t + 1 into quartiles based on RND_MVEt as of the end of fiscal year t and then independently assign stocks into two sub-samplesbased on the direction of forecast revisions; in both the upward (UPt+1 = 1) and downward (UPt+1 = 0) revision sub-samples, we form the R&D-based hedge portfolio by buying the highestR&D quartile and selling the lowest R&D quartile. Average monthly returns on portfolios are calculated from July of year t + 1 for holding periods of 6, 9 and 12 months. Raw_Rett+1 is themonthly total return, ARett+1 is the monthly return adjusted for size, book-to-market and prior-year return, and Alphat+1 is the intercept of the five-factor model (5) as in Chan et al.(2001):R pt � R ft ¼ a0 þ a1MKTRF t þ a2SMB t þ a3HML t þ a4UMD t þ a5WML t þ et ; ð3Þ where Rpt � Rft is the return in month t on portfolio p in excess of the T-bill rate; MKTRFt is thereturn on the market portfolio in excess of the T-bill rate; SMBt and HMLt are the returns on the Fama and French (1993) factor-mimicking portfolios for size and book-to-market; UMDt isthe return difference between past winners and losers, with winners (losers) defined as the top (bottom) quintile of stocks ranked by past return from 7 months to 1 month before theformation of portfolio p (Carhart 1997); and WMLt is the return difference between past winners and losers, with winners (losers) defined as the top (bottom) quintile of stocks ranked bypast return from 60 months to 12 months before the formation of portfolio p. Portfolios are rebalanced each year. The t statistics are in parentheses.

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Table 6Return performance of the R&D-based hedge portfolio controlling for the pre-revision forecast dispersion.

Forecast revision direction Mean pre-revision dispersion Median pre-revision dispersion

Panel A. Mean and median forecast dispersions for upward and downward revision groupsOverall sample Down 0.0092 0.0038

Up 0.0064 0.0023Down–up 0.0028 (2.79) 0.0015 (3.04)

Low pre-revision dispersion Down 0.0019 0.0015Up 0.0018 0.0014Down–up 0.0001 (0.42) 0.0001 (0.79)

High pre-revision dispersion Down 0.0151 0.0080Up 0.0130 0.0065Down–up 0.0021 (1.21) 0.0015 (0.98)

Forecastrevisiondirection

Six-month Nine-month 12-month

Raw_Rett+1 ARett+1 Alphat+1 Raw_Rett+1 ARett+1 Alphat+1 Raw_Rett+1 ARett+1 Alphat+1

Panel B. Abnormal returns of the R&D-based hedge portfolioLow pre-

revisiondispersion

Down 0.0216 (2.62) 0.0200 (2.57) 0.0203 (2.37) 0.0218 (3.42) 0.0190 (3.23) 0.0154 (2.41) 0.0167 (3.13) 0.0137 (2.81) 0.0100 (1.86)

Up 0.0002 (0.03) �0.0031 (�0.45) 0.0006 (0.07) 0.0041 (0.63) 0.0020 (0.37) 0.0024 (0.37) 0.0053 (0.97) 0.0019 (0.40) 0.0043 (0.77)

High pre-revisiondispersion

Down 0.0210 (2.88) 0.0221 (2.84) 0.0261 (3.40) 0.0178 (3.13) 0.0169 (2.91) 0.0180 (3.09) 0.0152 (3.13) 0.0143 (2.89) 0.0161 (3.25)

Up 0.0094 (1.41) 0.0051 (0.74) 0.0123 (1.73) 0.0108 (1.84) 0.0069 (1.15) 0.0099 (1.61) 0.0057 (1.13) 0.0043 (0.82) 0.0040 (0.74)

This table reports the abnormal returns on the R&D-based hedge portfolio conditional on upward and downward revisions after controlling for the pre-revision forecast dispersion. At Juneof each year t + 1, we sort stocks into quartiles (Rk_RND_MVEt) based on RND_MVEt as of the end of fiscal year t, and independently sort stocks into low and high groups by the level of pre-revision forecast dispersion and assign them to upward and downward revision groups. Hedge portfolios are formed by buying the highest R&D quartile and selling the lowest R&D quartilewithin the sub-samples formed by the level of pre-revision forecast dispersion and the direction of forecast revisions. Panel A compares the pre-revision dispersions between upward anddownward revisions in the overall sample and in the low- and high-dispersion groups, and Panel B shows abnormal returns on the R&D-based hedge portfolios in the sub-samples.Average monthly returns on a portfolio are calculated from July of year t + 1 for holding periods of 6, 9 and 12 months. Raw_Rett+1 is the monthly total return, ARett+1 is the monthly returnadjusted for size, book-to-market and prior-year return, and Alphat+1 is the intercept of the five-factor model (5) as in Chan et al. (2001):R pt � R ft ¼ a0 þ a1MKTRF t þ a2SMBt þ a3HMLt þ a4UMDt þ a5WMLt þ et ; ð3Þ where Rpt � Rft is the return in month t on portfolio p in excess of the T-bill rate; MKTRFt is the return onthe market portfolio in excess of the T-bill rate; SMBt and HMLt are the returns on the Fama and French (1993) factor-mimicking portfolios for size and book-to-market; UMDt is the returndifference in between past winners and losers, with winners (losers) defined as the top (bottom) quintile of stocks ranked by past return from 7 months to 1 month before the formation ofportfolio p (Carhart 1997); and WMLt is the return difference between past winners and losers, with winners (losers) defined as the top (bottom) quintile of stocks ranked by past returnfrom 60 months to 12 months before the formation of portfolio p. Portfolios are rebalanced each year. The t statistics are in parentheses.

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We also find that the differences in the abnormal return earned on the R&D-based hedge portfolioare statistically significant between the downward-revision and upward-revisions sub-samples. Basedon the calendar-month portfolio approach, the differences in abnormal returns are 1.71% (t = 3.86),1.02% (t = 2.70) and 0.93% (t = 2.82) for the 6-, 9- and 12-month investment periods, respectively.Based on the factor model, the differences in abnormal returns are 2.08% (t = 4.25), 0.96% (t = 2.18),and 1.03% (t = 2.72) for the 6-month, 9-month, and 12-month holding periods.

Overall, the above results are consistent with H3 that upward forecast revisions better mitigate thereturn differentials between high- and low-R&D firms than do downward revisions.

4.4.2. Results after controlling for the pre-revision dispersionThe above analysis does not control for possible differences in earnings uncertainties prior to

revisions. To address the potential concern that hedge-portfolio return differences between upward-and downward-revision sub-samples found above might be driven by the difference betweenthem in earnings uncertainty facing analysts before revisions, we now partition firms first by fore-cast dispersions that exist prior to the revisions into two groups, low and high, and then indepen-dently sort the firms into R&D quartiles. We re-examine, within each pre-revision forecastdispersion group, the R&D-based hedge returns conditional on upward and downward revisions.

Panel A of Table 6 shows that, on average, pre-revision dispersions indeed are higher in the sub-sample of downward forecast revisions than in that of upward revisions. After independently sortingfirms into low- and high-dispersion groups, however, there are no significant differences in pre-revi-sion dispersions between upward and downward revisions in either group.18

Panel B of Table 6 provides results after controlling for the pre-revision dispersion. In both the lowand the high (pre-revision) dispersion group, following downward revisions, the R&D-based hedgeportfolio earns significant abnormal returns over all the periods of 6, 9, and 12 months, measuredeither in terms of size-, book-to-market and momentum-adjusted returns or in terms of the interceptof the factor model; on the other hand, following upward revisions, the abnormal returns becomeinsignificantly different from zero in either the low or the high dispersion group. Thus, the general re-sult—that upward revisions are more effective in mitigating the return spread between high and lowR&D firms than downward revisions—continues to hold after controlling for the level of the pre-revi-sion dispersion, consistent with H3.

5. Robustness checks

5.1. Selection bias in analyst coverage

We address the endogeneity issue related to analyst-coverage choices using Heckman’s (1979)two-stage procedure. Prior studies document that analyst-coverage choices are determined by suchfactors as firm size, growth potential, industry membership, and the exchange of listing. In applyingthe Heckman test, we perform a Probit regression in the first stage for the analyst-coverage decision.Guided by prior literature, we model the determinants of analyst coverage as a function of firm size,exchange listing (Hong et al., 2000), and R&D intensity (Barth et al., 2001). We adopt this simplifiedstructure to maximize the number of observations in the Probit regression (and in the second-passregressions).19 We include the inverse Mills ratio in the regressions shown in Tables 3, 5 and 6, and findthat the coefficients on the inverse Mills ratios are insignificant and that our results are qualitatively un-changed; this suggests that the potential endogeneity issue related to the analyst-coverage choice causesno problems.

18 Also, we find no significant difference in the range of R&D intensity between upward- and downward-revision sub-samples ineither the low and the high dispersion group.

19 Hong et al. (2000) use various factors to model the analyst-coverage decision, including book-to-market ratio, beta, share price,return variance, past stock returns, prior turnover, option listing, and industry dummies. They find that firm size has the largestexplanatory power.


5.2. Constant sample

In the above analysis, we have computed forecast revisions based on all analyst forecasts available.A potential concern is that revisions can arise simply because of changes to the set of analysts coveringa particular firm between two forecast points. To address this concern, we construct a constant sampleretaining only the firms that are covered by the same set of analysts at the two points in time for com-puting forecast revisions. This requirement reduces the sample size from 6641 to 4238. We find thatour results remain qualitatively unchanged.

5.3. Sub-period analyses

Since 2000, the Enron scandal and other similar events have brought about a number of legisla-tive and regulatory changes.20 To address the potential concern that our results might be sensitive tochanges in legal and regulatory environments, we re-do the analyses after restricting the sample per-iod to 1983–2000, during which a firm’s information environment can be viewed as relatively stable.We also repeat the analyses for the sub-period before and after 1995 to examine whether our resultsare driven by omitted variables related to the state of the market; this is motivated by the evidenceemerging in the literature showing that the market sentiment was substantially different during thelate 1990s (the bubble period), when information was often disclosed by management to selectedgroups of market players (such as sell-side analysts). We find that the results from these sub-periodsdo not change our conclusion that upward revisions are more informative than downward revisionsabout future earnings and that they have asymmetric roles for mitigating the return spread betweenhigh and low R&D firms.

6. Conclusions

This study examines the role of analyst earnings-forecast revisions for conveying informationabout a firm’s performance and facilitating the informational efficiency of stock prices for firms inR&D-intensive industries. Based on prior findings that analysts tend to conceal unfavorable earningsforecasts, which leads to an optimistic bias in the population of disclosed forecasts, we predict thatupward and downward forecast revisions should have asymmetric roles in (i) reflecting changes inearnings expectations and resolving earnings uncertainties and (ii) mitigating R&D-related stock-pricedrifts.

Our empirical results are consistent with the predictions both about the role of forecast revisions inearnings forecasting and about the impact on the informational efficiency of stock prices. The mainmessage of this study is that, while analyst forecast revisions are useful for updating future earningsperformance, the link between forecast revisions and changes in true earnings expectations is compli-cated by optimistic biases in disclosed forecasts as introduced by (but not limited to) analysts’ with-held of unfavorable opinions. Investors should recognize the asymmetric usefulness of upward anddownward revisions and that the degree of this asymmetry increases with a firm’s R&D intensity. Cor-rect interpretation of forecast revisions is also important for researchers who use forecast data as asource of investor information (say, to explain stock returns).

Our results offer a different view from previous studies on whether divergence of opinions neces-sarily leads to stock over-pricing. Miller (1977) and Diether et al. (2002) argue that stocks on whichinvestors have more divergent opinions tend to be over-priced and subsequently earn lower returns.Our study casts doubt on the generality of this argument. We find that firms with higher R&D expen-ditures are associated with more divergent analyst opinions, but their stocks do not under-perform insubsequent years relative to those of firms with lower R&D expenditures. It will be useful to conductfurther research to illuminate this issue.

20 The most important changes are the ‘‘fair disclosure” rules, which took effect in October 2000, and the Sarbanes-Oxley Act of2002.


Acknowledgements

We thank Martin Loeb (the editor), two anonymous referees, Allaudeem Hameed, Gilles Hilary,Charles Hsu, Mark Jackson, Li Jiang, Clive Lennox, Steve Matsunaga, John Wei, and seminar participantsat the City University of Hong Kong, the Hong Kong Polytechnic University, the Hong Kong Universityof Science and Technology, the National University of Singapore, the University of Oregon, and the2006 American Accounting Association Meeting for helpful comments. We gratefully acknowledgethe financial support by the Research Grants Council of Hong Kong (Project No.: HKUST644708).

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