Value Line and I/B/E/S Earnings Forecasts

Value Line and I/B/E/S Earnings Forecasts Sundaresh Ramnath McDonough School of Business Georgetown University Ramnath@msb.edu Steven Rock Leeds School of Business The University of Colorado at Boulder Steven.Rock@Colorado.edu Philip Shane * Leeds School of Business The University of Colorado at Boulder Phil.Shane@Colorado.edu November 8, 2001 Abstract: This paper compares Value Line and I/B/E/S analyst earnings forecasts in terms of accuracy, rationality, and as proxies for market expectations. Using more recent data and forming consensus forecasts from the I/B/E/S detail files, we reach different conclusions than Philbrick and Ricks [1991], who found that Value Line provided more reliable actual EPS data, but forecasts from the two databases were of similar quality. We find that I/B/E/S actual quarterly EPS data are no longer less reliable than Value Line EPS data, and I/B/E/S quarterly earnings forecasts significantly outperform Value Line in terms of accuracy and as proxies for market expectations. We find that I/B/E/S forecasting superiority can be explained by the combination of I/B/E/S's timing advantage and the mitigation of idiosyncratic error through consensus building. We also evaluate long-term forecasts from both databases and find that I/B/E/S forecasts are less biased and more accurate. Our results have implications for research evaluating the information content of quarterly earnings announcements, research using longterm forecasts in valuation models, and research seeking to generalize evidence regarding Value Line analysts' forecasting behavior. We are grateful for helpful comments from Dave Guenther, Dov Fischer, John Jacob, Rick Morton, Jana Raedy, Srini Rangan, Frank Selto, Naomi Soderstrom and participants at a University of Colorado workshop. We also gratefully acknowledge the contribution of Thompson Financial Corporation for providing earnings per share forecast data available through the Institutional Brokers Estimate System (I/B/E/S). This data has been provided as part of a broad academic program to encourage earnings expectations research. * Address correspondence to Philip Shane; 419 UCB; Leeds School of Business; University of Colorado; Boulder, CO 80309.

Value Line and I/B/E/S Earnings Forecasts 1. Introduction This paper compares the earnings forecasts disseminated by The Value Line Investment Survey and Thompson Financial Corporation's International Brokerage Estimate System (I/B/E/S). In a prior study comparing these two premier sources of analysts forecasts, Philbrick and Ricks [PR, 1991, p. 397-8] conclude that Value Line and I/B/E/S are comparable in terms of their forecast data, but Value Line is a better source of actual EPS (earnings per share) data for the purpose of measuring earnings surprise. In our more recent sample period, we find just the opposite; that is, Value Line and I/B/E/S are comparable as sources of actual EPS data, but consensus forecasts derived from I/B/E/S outperform Value Line forecasts, both in terms of accuracy and as proxies for market expectations. We reexamine PR s conclusion for two reasons. First, while there has been no obvious change in compilation of the Value Line database, the number of analysts and brokerage firms contributing forecasts to the I/B/E/S database has expanded greatly between PR s sample period (1984-86) and ours (1993-96). This expansion provides the opportunity for more individual analysts to contribute to consensus forecasts built from the I/B/E/S database. We expect forecast accuracy to improve with the number of forecasts in the consensus and, therefore, PR s conclusions may not apply in more recent years. Second, while PR use I/B/E/S s summary file of consensus forecasts, we use the I/B/E/S detail files of analyst-by-analyst forecasts to compile a consensus that excludes stale forecasts. Prior research (e.g., O Brien 1988) has shown that staleness of forecasts included in the consensus obtained from the I/B/E/S summary file decreases forecast accuracy. 1

Our tests provide evidence that the superiority of I/B/E/S relative to Value Line forecasts stems from the two factors described above. First, we find evidence of increased I/B/E/S forecast accuracy (relative to Value Line) as the number of forecasts in the consensus increases. Second, I/B/E/S's superiority increases with the staleness of the Value Line forecast (trading days between the Value Line publication date and earnings announcement date). In tests controlling for these two factors, evidence of I/B/E/S's forecasting superiority disappears. In addition to evaluating forecasts of current quarter earnings, our study extends PR by evaluating long-term earnings forecasts (forecasts made one year-ahead or longer). Long-term forecasts have become increasingly relevant since PR, because studies involving residual income valuation have gained prominence in the accounting literature (e.g., Liu and Thomas [2000], Frankel and Lee [1998], Francis, et al. [2000]). These studies require long-horizon earnings forecasts. Therefore, we compare the accuracy of Value Line and I/B/E/S forecasts over a fouryear forecast horizon. The results suggest that, relative to Value Line, I/B/E/S produces less biased and more accurate long-term forecasts. Finally, in addition to their forecast accuracy and representativeness of market expectations, we extend PR by comparing the rationality of I/B/E/S and Value Line earnings forecasts (where rationality is defined as freedom from bias). 1 We find some evidence of optimism bias in the means of the quarterly earnings forecasts from both databases, although the median forecasts are significantly pessimistic. One interpretation is that analysts issue pessimistically biased quarterly forecasts most of the time, but some large bad news earnings surprises (possibly due to big bath behavior) cause forecasts, on average, to be optimistic (Brown [2001]). We also find that both databases produce positively autocorrelated quarterly forecast errors, suggesting the possibility of analyst underreaction to information in their past 2

forecast errors (Mendenhall [1991], Raedy and Shane [2000], Shane and Brous [2001]). Tests comparing the rationality of the current quarter earnings forecasts from the two databases reveal no significant differences in the optimism or pessimism bias and no significant differences in underreaction. The latter result is surprising, considering prior evidence that Value Line's success in recommending stocks is due to its ability to detect earnings momentum (Affleck-Graves and Mendenhall [1992]). 2 Overall, our results suggest that, relative to consensus I/B/E/S earnings forecasts, Value Line forecasts are less accurate and poorer proxies for market expectations. However, Value Line and I/B/E/S forecasts do not differ significantly in terms of their rationality with respect to information about the upcoming quarter s earnings. Our results suggest that any advantage Value Line's forecasts have due to Value Line's independence as a research company (without investment banking business) is outweighed by an I/B/E/S consensus that includes more timely forecasts and more effectively purges idiosyncratic error in analysts' individual earnings forecasts. Our results also suggest that care should be taken in drawing inferences about the behavior of analysts in general from studies relying strictly on Value Line data. However, Value Line data have unique features that researchers may value. Value Line forecasts appear reasonably representative in terms of their rationality and Value Line is interesting in its own right due to its claims that Value Line timeliness ranks consistently predict abnormal returns (Affleck-Graves and Mendenhall [1992]). Furthermore, Value Line is the only database with a long history of forecasts of detailed items on firms' balance sheets and income statements. These more detailed components may be relevant for studies that require forecasts of non-earnings items in financial statements. Finally, Value Line provides long-term price forecasts that may be useful for estimating an implied cost of equity capital (Botosan and Plumlee [2001]). 3

The remainder of the paper proceeds as follows. The next section describes our sample selection criteria. Section 3 reports the results of our tests comparing the accuracy of I/B/E/S and Value Line current quarter earnings forecasts. Section 4 explores possible explanations for I/B/E/S forecasting superiority in our sample time period. Section 5 compares the rationality of Value Line and I/B/E/S quarterly earnings forecasts. Section 6 reports the results of our comparison of Value Line and I/B/E/S forecasts as proxies for the market's current quarter earnings expectations. Section 7 contains the results of tests comparing long-term I/B/E/S and Value Line earnings forecasts, and section 8 concludes. 2. Sample Value Line assigns each firm that it covers to one of 13 editions and publishes each edition once every 13 weeks. We collected data from the 208 Value Line reports dated between January 29, 1993 and January 17, 1997 (16 reports for each of Value Line's 13 editions), providing Value Line forecast and actual EPS data for the 15 quarterly periods ending between March 1993 and September 1996. We did not collect Value Line data for firms traded only on foreign stock exchanges, firms that Value Line refers to as "investment companies" or firms with fiscal years that do not conform to the calendar year. 3 We further eliminate all firm-quarter observations for which we do not have all of the following: I/B/E/S and Value Line actual EPS; both Value Line and I/B/E/S forecasted EPS; previous quarter stock price (from COMPUSTAT); earnings announcement dates from COMPUSTAT and I/B/E/S; and three-day CRSP returns centered on the COMPUSTAT earnings announcement date. 10,839 firm-quarter observations spanning the period from the first quarter of 1993 through the third quarter of 1996 meet these criteria. 4

Both I/B/E/S and Value Line produce actual EPS data that adjust reported earnings for items that analysts claim they do not include in their forecasts. PR document serious reliability issues with the I/B/E/S actual EPS data during their 1984-86 sample period. However, PR also note that discussion with I/B/E/S personnel suggests that the problems with actual EPS have been mitigated recently (fn 10). We find evidence consistent with this claim. For example, PR found no negative I/B/E/S actual EPS observations in their sample period; whereas, they found that 6.5% of the Value Line actuals were negative. In contrast, we find that 7.9% of the Value Line and 7.2% of the I/B/E/S actual EPS observations are negative in our sample period. PR found that Value Line (I/B/E/S) and COMPUSTAT agreed on actual EPS 69% (33%) of the time; whereas, we find that Value Line (I/B/E/S) and COMPUSTAT agree on actual EPS 66% (61%) of the time. Similarly, PR found a 93% (68%) rank correlation between COMPUSTAT and Value Line (I/B/E/S) actual EPS data; whereas, we find a 91% (89%) rank correlation between COMPUSTAT and Value Line (I/B/E/S) actual EPS. Most importantly, we find that when Value Line and I/B/E/S disagree on firms actual EPS, market-adjusted returns accumulated over the three days centered on firms earnings announcements are at least as correlated with earnings surprise measured with reference to I/B/E/S actuals as with earnings surprise measured with reference to Value Line actuals. 4 Given our evidence suggesting that I/B/E/S actuals are no longer less reliable than Value Line actuals, we have chosen to hold the source of actuals constant and concentrate our study on the quality of the forecasts from the two databases. Table 1 shows how we derive our sample of 930 firms and 6,903 firm-quarter observations from the 10,839 firm-quarter observations that meet the criteria described above. First, for the reasons given above, we eliminate 26.3% of the 10,839 observations where Value Line and 5

I/B/E/S disagree on the actual earnings per share number. 5 Second, we obtain earnings announcement dates from COMPUSTAT, and to reduce measurement error in the computation of abnormal returns around earnings announcements, we eliminate all observations where COMPUSTAT and I/B/E/S disagree by more than one day on the firm's earnings announcement date. This requirement reduces the sample by an additional 5.24%. 6 Third, we omit 31 observations where the Value Line quarterly earnings forecast comes from a report dated more than seven trading days after the firm's earnings announcement for that quarter. 7 Finally, to minimize the effect of extreme observations, we eliminate all observations in the 1% tails of the distributions of any of the forecast error or returns variables used in our tests. This final screen reduces the original set of 10,839 observations by an additional 4.48% to a sample of 930 firms and 6,903 firm-quarter observations. The number of observations per firm ranges from one to fifteen, with a mean of 7.42 and median of 8. 3. I/B/E/S and Value Line Current Quarter Earnings Forecast Accuracy Our evaluation of I/B/E/S versus Value Line quarterly earnings forecast accuracy differs from PR in three ways. First, our Value Line database spans 15 quarters over the years 1993 through 1996; whereas, PR focuses on 10 quarters of data spanning the third quarter of 1984 to the fourth quarter of 1986. This is an important distinction, since I/B/E/S has greatly expanded its database since 1986. Second, as noted above, we require that I/B/E/S and Value Line agree on the actual EPS that is the target of the forecast. Third, we build consensus I/B/E/S forecasts from the detailed files of analyst-by-analyst forecasts rather than the summary I/B/E/S files used by PR. This allows us to extend the comparison of Value Line and I/B/E/S to the I/B/E/S detailed 6

files and also allows us better control over the timing of the I/B/E/S forecasts entering the consensus. 8 Our primary variable of interest is the analyst's quarterly earnings forecast error, defined as follows. 9 FE qjs = (X qj F qjs ) / P q-1,j (1) where: X qj is firm j s quarter q earnings per share, as reported by both I/B/E/S and Value Line; F qjs is a forecast of firm j s quarter q earnings per share, where the source and timing of the forecast is indicated by the subscript, s; P q-1,j is firm j s stock price as of the end of quarter q-1; and FE qjs is forecast error type s, distinguished by the source and timing of the forecast. The Value Line forecast is defined as the forecast from the last Value Line report with both a forecast of firm j's quarter q earnings and an actual value of firm j's quarter q-1 earnings. We compare the accuracy of this Value Line forecast to three definitions of the I/B/E/S forecast: (1) the most recent forecast dated between the firm's quarter q-1 and quarter q earnings announcements (s=ibmr); (2) the mean of all forecasts dated between the firm's quarter q-1 and quarter q earnings announcements (s=ibmn); and (3) the median of all forecasts dated between the firm's quarter q-1 and quarter q earnings announcements (s=ibmd). 10 Table 2 shows the results of our comparison of Value Line and I/B/E/S quarterly earnings forecast accuracy. To overcome statistical dependence issues related to having multiple observations from the same firm, we first determine forecast accuracy at the firm level and then aggregate these results across firms. For each forecast definition, s, we compute QFE js, the mean of the absolute values of FE qjs across all quarters available for each firm j. n QFE js = (1/n) Σ FE qjs (2) q=1 7

where firm j has n quarters of data available in our sample period, s indicates that the forecast error is derived from either the Value Line forecast (s=vl) or one of the I/B/E/S forecast definitions and FE qjs is defined in (1) above. To compare the accuracy of Value Line and I/B/E/S forecasts, we derive a measure of firm-level I/B/E/S forecasting superiority, IBSUP js, which can be positive or negative. IBSUP js = QFE j,vl - QFE js (3) where QFE js is defined in (2) above. Table 2 Panel A shows that the mean of QFE j,vl is 0.2595%; i.e., the across-firms mean of the mean absolute value Value Line forecast error equals 0.26% of stock price. For a firm with a $30 stock price, this translates into a forecast that misses actual earnings by approximately 8 cents per share. By comparison, the means of QFE j,ibmd, QFE j,ibmn and QFE j,ibmr are 0.1883%, 0.1919% and 0.1967%, respectively. Table 2 Panel B shows that, relative to Value Line, all three I/B/E/S forecast definitions produce significantly more accurate forecasts. 11 For a firm with a $30 stock price, these results imply that the Value Line forecast is, on average, 2 per share less accurate than the I/B/E/S median forecast. This evidence differs from PR s results that Value Line forecasts paired with Value Line's reported actuals produce the smallest forecast errors. In our sample period, the mean, median and most recent I/B/E/S current quarter earnings forecasts are all significantly more accurate, on average, than the Value Line forecast. 12 The next section explores possible explanations for this I/B/E/S forecasting superiority. 4. Explanations for I/B/E/S Forecasting Superiority This section explores two possible reasons for the greater accuracy of I/B/E/S current quarter earnings forecasts. First, I/B/E/S has a timing advantage in that I/B/E/S analysts can update their forecasts up to the date of the earnings announcement. On the other hand, by construction, Value 8

Line forecasts, occur on average about six and one-half weeks prior to the earnings announcement. 13 Second, I/B/E/S includes forecasts from various analysts and brokerage firms and the I/B/E/S consensus may mitigate idiosyncratic analyst error; whereas, Value Line forecasts reflect a single forecaster s perspective. We use the following cross-sectional quarterly regression model to test whether these two factors explain the relatively greater accuracy of I/B/E/S earnings forecasts. IBSUP qj = α 0q + α 1q (TIMELY qj ) + α 2q (NFCSTS qj ) + u qj (4) where IBSUP qj = FE qj,vl - FE qj,ibmd [see (1) above for forecast error definitions]. IBSUP qj represents I/B/E/S s superiority in forecasting firm j s quarter q earnings per share. As in (3) above, IBSUP qj can be positive or negative. However, in (4), we measure IBSUP qj at the firmquarter level, so that we can estimate (4) cross-sectionally in each of the 15 quarters in our sample and summarize results across quarters; whereas, in (3) we measure IBSUP js at the firmlevel and summarize results across firms. Also, we report results for (4) based on only one definition of the I/B/E/S forecast, the median of the distribution of I/B/E/S forecasts released between quarterly earnings announcements (i.e., the definition producing the smallest I/B/E/S forecasting errors in Table 2). 14 TIMELY qj proxies for I/B/E/S s timing advantage and equals the number of trading days between the publication date of the Value Line report from which we obtain the Value Line forecast and the corresponding earnings announcement date. NFCSTS qj proxies for the probable advantages of consensus building and equals the number of I/B/E/S analysts issuing forecasts between firm j s quarter q-1 and quarter q earnings announcements. Table 3 Panel A describes the distribution of the three variables, IBSUP qj, TIMELY qj and NFCSTS qj. The timeliness variable ranges from -7 to 60, with a median value of 25 trading days between the date of the Value Line report and the corresponding earnings announcement date. 15 9

NFCSTS qj ranges from 1 to 36, with a median value of 6, indicating I/B/E/S s potential to diversify idiosyncratic error through formation of a consensus. Panel B shows that, consistent with our expectations, TIMELY and NFCSTS are each significantly correlated with IBSUP. Table 3 panel C contains the results of estimating the regression model in (4) above. The intercept in model (4) measures I/B/E/S s residual forecasting superiority after controlling for the staleness in Value Line forecasts and the number of forecasts in the I/B/E/S consensus measure. The regression results are presented as the mean of the coefficients across 15 quarterly periods. T-statistics and significance levels are derived under the null that the mean of the coefficient distributions across quarters is zero. Table 3 panel C shows that both TIMELY and NFCSTS are positively and significantly related to IBSUP, confirming that timing and consensus building advantages contribute significantly to I/B/E/S s relatively greater forecast accuracy. Furthermore, the intercept is not significantly different from zero, indicating that no residual I/B/E/S forecasting superiority remains after controlling for these two factors. 16 Given the results above that the I/B/E/S forecasting superiority appears to be explained by the number of forecasts in the I/B/E/S consensus and the staleness of Value Line forecasts, one possible explanation for the difference between our results and PR s is the expansion of the I/B/E/S database between the time of the two studies. From a random sample of firms followed by both Value Line and I/B/E/S during the PR time period, we find that on average there are only 3.6 I/B/E/S forecasts available to enter the I/B/E/S consensus as compared to 7.12 forecasts during our time period. Therefore, we suggest that the difference between our results and PR's is due, at least in part, to relatively more analysts issuing forecasts on the I/B/E/S database during our sample period. 17 Overall, our results suggest that in recent years I/B/E/S s current quarter consensus forecasts are more accurate than Value Line's, and this forecasting superiority can be 10

explained by the I/B/E/S consensus including more timely forecasts, as well as the mitigation of idiosyncratic error through building a consensus from a variety of analysts forecasts. 5. Rationality of I/B/E/S and Value Line Earnings Forecasts Many studies have documented systematic bias (optimism or pessimism) and underreaction to information in analysts' forecasts (e.g., O'Brien [1988], Mendenhall [1991]). In addition, recent studies have evaluated the degree to which analyst underreaction to earnings information can explain the market's underreaction reflected in post-earnings-announcementdrift. Some of these studies rely on Value Line data (e.g., Abarbanell and Bernard [1992], Shane and Brous [2001]), while others derive consensus forecasts from I/B/E/S (e.g., Frankel and Lee [1998], Kang, et al. [1994]). Given that Value Line analysts are not attached to brokerage houses, whereas I/B/E/S analysts typically are, an interesting issue is whether the rationality of analysts forecasts differs across these two databases. 18 Prior research attributes optimism and pessimism bias to analysts catering to management demands due to pressures associated with: (a) analysts' need for private information to guide their forecasts (Brown, et al. [1985]; Francis and Philbrick [1991]); and (b) investment banking relationships between brokerage firms and the companies that analysts in those firms follow (Dugar and Nathan [1995]. Value Line does not have investment banking relationships with the firms its analysts follow, but Value Line analysts may have similar pressure to obtain private information from management. Value Line claims to incorporate information about earnings momentum into its buy-sell-hold recommendations, and empirical evidence supports this claim (Affleck-Graves and Mendenhall [1992]). If Value Line also incorporates this information into 11

its earnings forecasts, then we expect to find less underreaction in Value Line earnings forecasts relative to the underreaction reflected in the forecasts of I/B/E/S analysts. Research demonstrates that analysts optimism bias decreases with the forecast horizon (e.g., Kang, et al. [1994]; Raedy and Shane [2000]). Therefore, to avoid biasing our tests in favor of I/B/E/S, for this analysis, we compare Value Line forecast errors to comparably timed I/B/E/S forecasts computed as the median of all I/B/E/S forecasts dated between the quarter q-1 earnings announcement date and the Value Line report date. For each of the 875 firms with available data, we compute mean and median forecast errors across quarters (up to 15 quarters per firm). Panel A of Table 4 reports the across-firm means (medians) of these firm-level mean (median) forecast errors. Panel A indicates that the mean forecast error across firms is negative for both Value Line and I/B/E/S (-0.021% and 0.032%, respectively) reflecting previously documented average analyst optimism (e.g., Abarbanell [1991], Kang, et al. [1994]). In contrast, the distribution of medians across firms indicates overall pessimism with median Value Line (I/B/E/S) forecast errors of 0.015% (0.014%). Brown [2001] documents a similar pattern in I/B/E/S forecasts and attributes it to a few extreme negative forecast errors (bath-taking) driving the mean. As indicated in the last row of panel A, we find a marginally significant difference between the mean Value Line and I/B/E/S signed forecast errors (p-value=0.095), but the median signed forecast errors do not differ significantly between the two databases (p-value=0.63). Overall, across the 875 firms in our sample, it does not appear that analyst optimism/pessimism regarding the upcoming quarter s earnings differs significantly between Value Line and I/B/E/S analysts. To compare the underreaction in Value Line and I/B/E/S forecasts, we compute forecast errors as described in (1) above and estimate the parameters of the following regression. 12

FE qjs = β 0q + β 1q (FE q-1,j,s ) + u qj (5) where the subscript, s, indicates either a forecast error defined with reference to Value Line forecasts (s=vl) or a forecast error defined with reference to median I/B/E/S consensus forecasts (s=ibmd). We estimate regressions on a quarterly basis across firms and then summarize coefficient estimates across the fourteen quarters with available data. Model (5) regresses firm j's current quarter forecast error against firm j's prior quarter forecast error, holding constant the source of the forecast. A significantly positive slope in this model is consistent with evidence in prior literature that analysts underreact to information in prior earnings forecast errors. Panel B of Table 4 reports means of model (5) parameter estimates across the fourteen quarters. The evidence in Panel B is consistent with previous research: both Value Line and I/B/E/S databases generate serially correlated forecast errors, consistent with analyst underreaction. Specifically, the β 1 coefficient estimates in model (5) (0.230 using Value Line forecast errors and 0.232 using I/B/E/S forecast errors, respectively) are both significantly positive. Interestingly, we find no evidence to suggest that Value Line and I/B/E/S differ significantly in their underreaction to the information in their respective earnings forecast errors. This is surprising, considering the emphasis Value Line places on earnings momentum in making its stock recommendations (Affleck-Graves and Mendenhall [1992]). Overall, these results suggest that the two databases provide forecasts that have similar optimistic/pessimistic patterns and are comparable in the underreaction to recent earnings surprises. 6. Quarterly Earnings Forecasts as Proxies for Market Expectations The next issue we address is the degree to which I/B/E/S and Value Line analysts forecasts reflect market expectations immediately before firms announce quarterly earnings. As 13

described in PR, this evaluation is relevant to studies of the information content of quarterly earnings and studies of the information content of other news announced at the same time as quarterly earnings. In both types of studies, researchers need an effective proxy for the market s expectations at the beginning of the return accumulation period to either evaluate directly, or control for, the market s response to quarterly earnings announcements. Using more recent data than PR and building I/B/E/S consensus forecasts from the I/B/E/S detailed files, we evaluate the degree to which I/B/E/S consensus forecasts and Value Line forecasts proxy for market expectations as of the beginning of a three-day return accumulation period centered on the quarterly earnings announcement date. These tests examine the following null hypothesis: ρ(car qj, FE qj,vl ) = ρ(car qj, FE qj,ibmd ) where: ρ indicates correlation (we examine both Pearson and Spearman correlations); CAR qj is the difference between firm j's stock returns and the returns on the value-weighted CRSP market index summed over the three days centered on the firm s quarter q earnings announcement; and, as described in (1) above, FE qj,vl and FE qj,ibmd represent firm j's quarter q forecast errors computed with reference to Value Line's most recent forecast and the median of all I/B/E/S forecasts issued between firm j's quarter q-1 and quarter q earnings announcement dates. We compute these correlations across firms within quarters and obtain 15 observations each (one per quarter) using either Value Line or I/B/E/S forecast errors. For each forecast error definition and across the 15 quarters of observations, panel A of Table 5 reports the means of the Pearson and Spearman correlations, along with the number of times (out of 15 observations) that the correlations are greater than zero at the 1% and 5% significance levels (one-tail). The largest mean correlation comes from the 15 Spearman 14

correlations between returns and the forecast error measured using the median I/B/E/S forecast. This mean correlation is 0.240 as compared to a 0.204 mean correlation between returns and the forecast error using the Value Line forecast. Panel B shows a 4.59% median difference in Spearman correlations between returns and I/B/E/S versus Value Line forecasts, and a Wilcoxon matched-pair signed-rank test of the differences in these correlations across quarters is significant at the 0.011 level. The Pearson returns-earnings correlations are lower than the Spearman correlations, but the mean Pearson correlation computed using the I/B/E/S consensus forecast (0.197) is significantly larger than the mean Pearson correlation computed using the Value Line forecast (0.163). The matched-pair t-test significance level is 0.026. Thus, contrary to PR, we reject the null hypothesis that I/B/E/S and Value Line forecasts serve equally well as proxies for the market's earnings expectations immediately before an earnings announcement. We find that the median I/B/E/S consensus forecast outperforms the Value Line forecast as a proxy for market expectations as of the beginning of the three-day return accumulation period preceding an earnings announcement. 19 7. Comparing the Accuracy of I/B/E/S and Value Line Long-term Earnings Forecasts Since PR, many studies require long-term earnings forecasts primarily for purposes of estimating equity values using the residual income valuation model (e.g., Liu and Thomas [2000], Frankel and Lee [1998], Francis, et al. [2000]). We compare the bias and accuracy of I/B/E/S and Value Line forecasts of both one-year-ahead and four-year-ahead earnings. Both Value Line and I/B/E/S publish forecasts over approximately a four-year forecast horizon. Each database provides one-year-ahead earnings forecasts and longer-term forecasts that we transform into estimates of four-year-ahead earnings expectations as described below. 15

We obtain Value Line forecasts of years y+1 and y+4 earnings from the last Value Line report that also contains a forecast of the current year y earnings. Value Line publishes forecasts of earnings for year y+1 (F y+1,j,vl ) and average earnings for the years y+3 through y+5. We use the Value Line forecast of average earnings over the years y+3 through y+5 to proxy for the Value Line forecast of earnings for year y+4 (F y+4,j,vl ). We derive the I/B/E/S consensus forecast of year y+1 (F y+1,j,ib ) earnings as the median of all I/B/E/S year y+1 earnings forecasts dated within 22 trading days (approximately one month) prior to the Value Line report date. We obtain the I/B/E/S consensus forecast of firm j's earnings for year y+4 as: F y+4,j,ib = (F y+1,j,ib ) * (1+F ltg,j,ib ) 3 where F ltg,j,ib is the median I/B/E/S long-term earnings growth rate forecast obtained from the I/B/E/S summary report dated in the same month as the Value Line report date. 20 Price-deflated forecast errors are computed with reference to firm j's actual primary earnings per share adjusted for net-of-tax special items derived from COMPUSTAT data for years y+1 and y+4. 21 Table 6 reports the results comparing these price-deflated Value Line and I/B/E/S errors in forecasting year y+1 (y+4) earnings across the 778 (550) firms with the necessary data. Panel A shows that both databases produce year-ahead and four-year-ahead earnings forecasts that are significantly optimistically biased, and much more so for the longer-term forecasts. 22 Panel A also shows that I/B/E/S one-year-ahead earnings forecasts are significantly more optimistic than Value Line, but this difference reverses with the longer-term forecasts, where Value Line forecasts are significantly more optimistic than I/B/E/S forecasts. Panel B of Table 6 shows the results of tests for differences in one-year (four-year) horizon forecast accuracy across the 778 (550) firms in the sample. We find that I/B/E/S and Value Line forecasts are not significantly different in terms of their accuracy with respect to one- 16

year-ahead earnings forecasts. However, I/B/E/S produces significantly more accurate four-year horizon earnings forecasts. Overall the results in table 6 suggest that, relative to Value Line forecasts, long-term (four-year-ahead) I/B/E/S forecasts are more accurate and less biased. This result suggests that I/B/E/S is likely the better database choice for researchers seeking longhorizon forecasts to estimate equity valuation models. 8. Conclusion This study provides evidence comparing Value Line and I/B/E/S earnings forecasts in terms of their accuracy, their rationality and as proxies for market expectations. Our evidence suggests that, relative to I/B/E/S consensus earnings forecasts compiled from the detailed analyst files, Value Line issues less accurate current quarter earnings forecasts. We find that I/B/E/S's forecasting superiority is due to both a timing advantage and to a consensus that effectively mitigates individual forecast error. The I/B/E/S consensus is more timely because analysts contributing forecasts to I/B/E/S can revise their forecasts up to the earnings announcement date, whereas Value Line issues one forecast per firm-quarter which, on average, occurs six and onehalf weeks prior to the firm s earnings announcement. Further, I/B/E/S has the advantage of multiple analysts contributing forecasts, while a single analyst makes the Value Line forecast for a given firm. After controlling for these I/B/E/S advantages, we no longer find conclusive evidence that I/B/E/S's current quarter earnings forecasts are more accurate than Value Line's. Our results differ from PR, who did not find evidence of the superiority of I/B/E/S consensus forecasts relative to Value Line forecasts. I/B/E/S significantly expanded its database between the time of PR s study and the time of our study. Correspondingly, the number of forecasts entering the I/B/E/S consensus has increased significantly, and we suspect that this at 17

least partly explains the difference in results between the two studies. Our results suggest that, in more recent years, I/B/E/S current quarter consensus forecasts are more accurate than Value Line's. We also find that, in our sample period, I/B/E/S forecasts offer a better proxy for the market's earnings expectations immediately before a quarterly earnings announcement. Thus, our results have implications for studies that require an effective proxy for the market s expectations either to directly evaluate, or to control for, the information content of quarterly earnings announcements. Our evidence regarding analysts' forecasting rationality suggests that Value Line forecasts are reasonably representative of the forecasts of other analysts in terms of optimism/pessimism biases and in terms of underreaction to the previous quarter's earnings news. We find no significant differences between Value Line and I/B/E/S quarterly earnings forecasts along these dimensions. This evidence is somewhat surprising, considering prior research documenting that Value Line's success in recommending stocks is due to its ability to detect earnings momentum (Affleck-Graves and Mendenhall [1992]). We find no evidence that, relative to I/B/E/S analysts' forecasts, Value Line's earnings forecasts more efficiently incorporate the momentum in earnings shocks. We also find evidence that longer-term earnings forecasts derived from I/B/E/S long-term growth forecasts are more accurate and less biased than Value Line's longer-term earnings forecasts. This evidence has implications for studies requiring long-term earnings forecasts as inputs to residual income valuation models. However, Value Line provides direct forecasts of price-to-book premiums at the end of the forecast horizon. An avenue for further research is to compare Value Line's direct terminal value forecasts to terminal value estimates inferred from I/B/E/S long-term earnings forecasts. 18

Researchers investigating the value relevance of earnings information would like access to unbiased earnings forecasts that fully reflect the expectations of professional analysts at any given point in time. I/B/E/S forecasts are potentially biased by pressures for brokerage firms to obtain or retain investment banking business, and by pressures to please the managers of the firms the analysts follow in order to obtain easier access to the managers information. Value Line analysts have the latter pressure but not the former. However, Value Line has the disadvantage of a preset forecasting schedule that constrains its analysts to accumulate 13 weeks of information between earnings forecasts. Therefore, the most recent Value Line forecast is not likely to fully reflect what the Value Line forecaster knows immediately before a quarterly earnings announcement. I/B/E/S has the advantage of offering analysts the opportunity to update outstanding forecasts at any point in time. Furthermore, the I/B/E/S database offers the opportunity to build a consensus forecast that mitigates idiosyncratic forecaster error. The evidence in this study suggests that these latter two characteristics outweigh any advantage Value Line might have as an independent research service without investment banking business. In the 1993-96 time period of our study, I/B/E/S forecasts outperform Value Line s forecasts both in terms of accuracy and as a proxy for the market s earnings expectations. 19

FOOTNOTES 1 Three types of forecasting bias have been identified in the literature. First, analysts may issue systematically optimistic forecasts of a firm's earnings in order to either curry private information from management (Francis and Philbrick [1991]) or foster an investment banking relationship with the firm (Dugar and Nathan [1995]). Second, as the time of the earnings announcement approaches, analyst earnings forecasts may become systematically pessimistic as management coaxes analysts' forecasts downward to avoid negative earnings surprises (Matsumoto [2001]). Third, analysts may react conservatively to news about a company's earnings prospects, such that they tend to issue systematically pessimistic forecasts following good news and systematically optimistic forecasts following bad news; i.e., analysts tend to underreact to news about future earnings (e.g., Mendenhall [1991]; Raedy and Shane [2000]; Shane and Brous [2001]). 2 One explanation consistent with these results is that Value Line s timeliness ranks and earnings forecasts emerge from two unrelated processes. Future research could test the hypothesis that Value Line timeliness ranks predict Value Line earnings forecast errors. 3 PR also restrict their sample to calendar year-end firms. 4 Specifically, first computing correlations cross-sectionally within quarters and then averaging correlations across the 15 quarters in our sample period, we find that for observations where Value Line and I/B/E/S disagree on actual EPS, the mean (median) Pearson (Spearman) correlation between price-scaled earnings surprise and returns is 0.147% (0.210%) when Value 20

Line is the source of actual and forecasted EPS and 0.213% (0.242%) when I/B/E/S is the source of actual and forecasted EPS. Differences between Pearson (Spearman) correlations are significant at the 0.024 (0.115) level (two-tailed). As reported in table 5 below, we find similar differences when I/B/E/S and Value Line agree on actual EPS. Therefore, we conclude that, in our sample period, I/B/E/S actuals are no longer less reliable than Value Line actuals. 5 Approximately half of the time that I/B/E/S and Value Line disagree on actual EPS, the difference is within a penny per share and 3% of the larger of the two actuals. 6 After eliminating observations where I/B/E/S and COMPUSTAT disagree by more than one day, we rely on COMPUSTAT for the earnings announcement date in all of our tests. 7 We allow Value Line Report dates up to seven trading days after the forecasted quarter s earnings announcement date based on the assumption that there is a publication lag such that the Value Line forecaster is unaware of the actual when making the forecast (Abarbanell [1991]). We assume that lags greater than seven trading days indicate Value Line data errors. 8 Developing a consensus I/B/E/S forecast from the detailed analyst-by-analyst files rather than the summary files used by PR avoids the reporting lag noted by O'Brien [1988] and assures us that all I/B/E/S forecasts entering the consensus are dated between the current and previous earnings announcement dates. PR compare Value Line s forecast of firm j s quarterly earnings to an I/B/E/S consensus taken from the most recent monthly I/B/E/S report prior to the firm s 21

quarterly earnings announcement. In separate tests (not tabled), we find that, relative to the consensus offered by the I/B/E/S summary files, the consensus I/B/E/S forecast that we derive from the detail files is significantly more accurate and a significantly better proxy for the market s earnings expectations. 9 We combine data from CRSP, I/B/E/S and Value Line and carefully adjust all variables to a common denominator that adjusts for stock splits and stock dividends. 10 If an individual analyst issues more than one forecast between earnings announcements, we use only the most recent of those forecasts in computing the I/B/E/S mean and median. If only one I/B/E/S analyst issued forecasts between quarterly earnings announcements, we use that analyst's most recent forecast as the I/B/E/S mean, median, and most recent forecast. 11 We also find that the I/B/E/S median consensus forecast is significantly more accurate than the mean consensus and the mean consensus is significantly more accurate than the most recent I/B/E/S forecast. 12 The results are robust to using the consensus from the I/B/E/S summary files as opposed to forming a consensus from the detailed files. The results are also robust to matching the I/B/E/S (Value Line) forecast with the I/B/E/S (Value Line) actual EPS number and expanding the sample to allow observations in the 1% tails of each variable s distribution and observations where I/B/E/S and Value Line disagree on actual EPS number. Overall forecast accuracy 22

decreases in this expanded sample. Nonetheless, with reference to the expanded sample, we find that overall forecast accuracy has improved since PR s sample period. PR s Table 1, Panel B (p. 405) reports mean absolute price-scaled forecast errors of 0.77% (0.87%) for Value Line (I/B/E/S) as compared to 0.43% (0.32%) in our sample period. Overall, it appears that both Value Line and I/B/E/S forecasting accuracy has improved since PR s sample period, but I/B/E/S has improved relatively more. In any case, we do not attribute the difference between our results and those of PR to our reliance on the I/B/E/S detail files or to excluding observations where I/B/E/S and Value Line disagree on the actual EPS number. 13 Due to its preset forecasting schedule, Value Line has a gap of 13 weeks between forecasts for any given firm. I/B/E/S analysts enjoy the flexibility to publish new forecasts every day. 14 The results are similar and the inferences robust to using the mean or single most recent rather than the median I/B/E/S forecast to represent the I/B/E/S earnings expectation. 15 As previously noted, to allow for publication lag we include forecasts from Value Line reports issued up to seven trading days after a firm s earnings announcement. This causes TIMELY to have negative values in some cases. 16 These results are robust to truncation of outliers and to adding the square of NFCSTS to the model. Adding NFCSTS 2 allows forecast accuracy to increase at a decreasing rate as the number of analysts grows. We find that the coefficient on this variable is negative and significant, as 23

expected. However, the intercept is still insignificant, further supporting our inference that I/B/E/S s forecasting superiority can be explained by a combination of I/B/E/S s timing advantage and the removal of idiosyncratic error by building a consensus I/B/E/S forecast. 17 Comparing our random sample from the PR period to the sample in our period, we find that both the mean and median number of forecasts entering the I/B/E/S consensus have increased significantly (p-values<0.0001). 18 We use the term, rationality, in the classical sense that analysts' forecasts should reflect all current and previously released information. Underreaction refers to situations where forecast errors are predictable due to analysts' underweighting previously disclosed information. In the case of earnings surprise, underreaction implies positive serial correlation in quarterly earnings forecast errors. 19 In pooled cross-section, we find that the stronger correlation between earnings surprise defined with reference to I/B/E/S forecasts also corresponds to a larger earnings response coefficient (ERC). Regressing market-adjusted returns in the 3-day window surrounding an earnings announcement on forecast errors defined with reference to I/B/E/S consensus forecasts results in an ERC estimate of 2.71 (t-statistic=16.704) as compared to an ERC estimate of 1.603 (tstatistic=13.482) using the latest Value Line forecast as the proxy for market expectations. We also conducted Vuong [1989] tests of the explanatory power of the ERC models using the I/B/E/S median versus Value Line forecasts in cross-sectional quarterly regressions, and note 24

that earnings surprise using I/B/E/S forecasts explain significantly more of the cross-sectional variation in market-adjusted returns. 20 During the period of the study, four-year-ahead forecasts are not frequently available on the I/B/E/S database. However, I/B/E/S provides year-ahead and long-term growth forecasts in most cases, which are used to impute the four-year-ahead forecasts. The accuracy of the imputed fouryear ahead forecasts is tested by comparison to actual four-year ahead forecasts in instances where the four-year ahead forecasts are available on I/B/E/S. The Pearson (Spearman) correlation between the imputed and actual forecasts is 93% (88%). 21 The price deflator is the firm's stock price as of the end of year y. We use COMPUSTAT actual earnings per share numbers to avoid the cost of hand-gathering an additional four years of Value Line data and to mitigate the loss of observations that occurs when Value Line or I/B/E/S stops following a firm during the four-year period following the forecast. The Pearson correlation between I/B/E/S (Value Line) actual EPS and COMPUSTAT EPS before extraordinary items and adjusted for net-of-tax special items is 86.7% (88.2%). 22 See Kang, et al. [1994] for evidence of a similar horizon effect on analyst optimism in forecasting quarterly earnings over a two-year forecast horizon. 25

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