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Online Early — Preprint of Accepted ManuscriptThis is a PDF file of a manuscript that has been accepted for publication in an American Accounting Association journal. It is the final version that was uploaded and approved by the author(s). While the paper has been through the usual rigorous peer review process for AAA journals, it has not been copyedited, nor have the graphics and tables been modified for final publication. Also note that the paper may refer to online Appendices and/or Supplements that are not yet available. The manuscript will undergo copyediting, typesetting and review of page proofs before it is published in its final form, therefore the published version will look different from this version and may also have some differences in content.
We have posted this preliminary version of the manuscript as a service to our members and subscribers in the interest of making the information available for distribution and citation as quickly as possible following acceptance.
The DOI for this manuscript and the correct format for citing the paper are given at the top of the online (html) abstract.
Once the final published version of this paper is posted online, it will replace this preliminary version at the specified DOI.
The Accounting Review • Issues in Accounting Education • Accounting HorizonsAccounting and the Public Interest • Auditing: A Journal of Practice & Theory
Behavioral Research in Accounting • Current Issues in Auditing Journal of Emerging Technologies in Accounting • Journal of Information Systems
Journal of International Accounting Research Journal of Management Accounting Research • The ATA Journal of Legal Tax Research
The Journal of the American Taxation Association
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Recommendation-Forecast Consistency and Earnings Forecast Quality
We gratefully acknowledge the helpful suggestions of Jeffrey Callen, Xia Chen, Qiang Cheng, Joel Demski, Yonca Ertimur, Ole-Kristian Hope, Joel Houston, Marcus Kirk, Hai Lu, Gordon Richardson, Jay Ritter, Jenny Tucker, Kent Womack, Franco Wong and the participants of the University of Florida, University of Toronto and University of Wisconsin workshops, and the 20th
Conference on Financial Economics and Accounting.
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Recommendation-Forecast Consistency and Earnings Forecast Quality
Synopsis: We investigate the implications of recommendation-forecastconsistency for the informativeness of stock recommendations and earnings forecasts and the quality of analysts’ earnings forecasts. Stock recommendations and earnings forecasts are often issued simultaneously and evaluated jointly by investors. However, the two signals are often inconsistent with each other. Defining a recommendation-forecast pair as consistent if both of them are above or below their existing consensus, we find that 58.3% of recommendation-forecast pairs are consistent in our sample. We document that consistent pairs result in much stronger market reactions than inconsistent pairs. Weshow that analysts making consistent recommendation-forecasts make more accurate and timelier forecasts than do analysts making inconsistent recommendation-forecasts,suggesting that consistent analysts make higher quality earnings forecasts. We extend the literature on informativeness of analyst research by showing that recommendation-forecast consistency is an important ex ante signal regarding both firm valuation and earnings forecast quality. Investors and researchers can use consistency as a salient, ex ante signal to identify more informative analyst research and superior earnings forecasts.
Recommendation-Forecast Consistency and Earnings Forecast Quality
INTRODUCTION
Stock recommendations and earnings forecasts are often issued simultaneously
and evaluated jointly by investors. While earnings forecasts are a key input for valuing
firms (Ohlson 1995; Frankel and Lee 1998), they are often not used in recommendations
(Bradshaw 2004; Ke and Yu 2009). We show that capital markets treat consistent
recommendation-forecast pairs as more informative than inconsistent ones and analysts
making consistent recommendation-forecasts make both more accurate and timelier
earnings forecasts than do analysts making inconsistent ones.
We define a recommendation-forecast pair as consistent if both the
recommendation and the forecast issued on the same day are above or below their
prevailing consensus.1 Stock valuation is a function of cash flows and discount rates
(Ohlson 1995). Assuming discount rates are stable in the short-term, an upward
(downward) revision of a stock recommendation accompanied by a downward (upward)
revision of an earnings forecast indicates the analyst does not effectively translate his/her
earnings forecast in his/her stock recommendation.2 We show that joint recommendation-
forecasts are consistent 58.3% of the time, and that the two signals are more likely to be
consistent for unfavorable recommendations (i.e., 53.9%, 62%, and 71.8% of buys, holds
and sells, respectively, are consistent with their forecasts).
1 Appendix 1 provides examples of recommendation-forecast consistency. 2 Similar to Hilary and Hsu (2012), we measure the information content in a recommendation (forecast) by comparing it to the existing consensus based on the assumption that the consensus reflects the prevailing expectation, and analysts convey incremental information via the difference between their research outputand the prevailing consensus. We discuss below that when we use an alternative definition, defining arecommendation-forecast pair as consistent if both the stock recommendation and the earnings forecast issued on the same day are above or below the analyst’s own most recent prior recommendation and forecast, we obtain qualitatively similar results.
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Prior research on informativeness of recommendations and forecasts shows that
these two signals convey distinct information (Francis and Soffer 1997). Later studies
examining mapping of forecasts to recommendations suggest that analysts do not
effectively translate forecasts into recommendations due to economic incentives and
behavioral biases (Bradshaw 2004; Ke and Yu 2009).3 In addition, analysts trade off
generating trade through issuing biased reports and building reputation through issuing
useful reports (Hong and Kubik 2003; Jackson 2005). We contend that analysts are less
likely to ignore or distort earnings information and issue inconsistent recommendation-
forecast when they possess superior information. Overall, recommendation-forecast
consistency reflects fewer economic and behavioral biases and superior earnings
information, suggesting that consistent recommendation and earnings forecasts are more
informative. We show that investors react more to consistent recommendations and
forecasts after controlling for other analyst/forecast characteristics affecting investor
responses.
We test whether consistent earnings forecasts are of higher quality directly using
accuracy and timeliness as value-relevant attributes of forecasts (Clement and Tse 2003;
Cooper et al. 2001). Controlling for firm and year effects, we regress accuracy and
timeliness on our consistency indicator variable and control variables, and show that
consistent forecasts are more accurate and timelier. Our evidence suggests that the
stronger capital market reaction to consistent recommendation-forecasts is justified by the
higher quality of analysts’ earnings forecast research outputs.
3 For example, analysts bias recommendations upward to please managers and increase stock sales, and they bias forecasts downward to allow managers to meet or beat earnings benchmarks (e.g., Dugar and Nathan 1995; Lin and McNichols 1998; Ke and Yu 2006). Analysts’ use of investor sentiment leads them to rely too little on their forecasts for their recommendations.
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Our study has important implications for investors and researchers. We introduce
consistency as a simple, ex ante signal to identify more reliable stock recommendations
and higher quality earnings forecasts. Unlike recommendation-forecast consistency
measures based on residual income models (Ke and Yu 2009), our measure only requires
knowledge of the individual analyst’s one-year-ahead forecast and recommendation and
their prevailing consensus. In contrast to accuracy and timeliness, consistency is an ex
ante measure. We show that consistency is positively related to both accuracy and
timeliness, and informative after controlling for these two ex post measures.
We extend the literature on signal informativeness of analyst outputs by
demonstrating that stronger (weaker) capital market reactions associated with consistent
(inconsistent) recommendation-forecasts do not merely reflect an additive (offsetting)
effect of the two signals, but interact with each other to convey information beyond their
additive effects.4 We offer potential explanations for evidence documented in the extant
literature. Prior research on stock recommendations (Womack 1996; Kadan et al. 2009)
shows that hold and sell recommendations elicit stronger reactions than buy
recommendations. Our findings suggest that some of this stronger reaction is due to the
fact that holds and sells are relatively more likely than buys to be consistent with
analysts’ earnings forecasts. Our evidence adds to the literature on analysts’ conflict of
interests. Prior research suggests that recommendations are more optimistically biased
than earnings forecasts (Dugar and Nathan 1995; Lin and McNichols 1998), and that
affiliated analysts issue more optimistic recommendations and more pessimistic earnings
4 Similarly, Brav and Lehavy (2003) provide evidence that market reactions to recommendations arestronger when recommendations and target prices are revised in the same direction relative to the same analyst’s prior recommendations and target prices but they do not show that consistent analysts are of higher quality.
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forecasts than unaffiliated analysts (Malmendier and Shanthikumar 2007). Our results
suggest that capital markets unravel differential biases in forecasts and recommendations
by assessing their consistency.
RELATED STUDIES AND HYPOTHESES
Analysts’ reports include multiple outputs: earnings forecasts, recommendations,
target prices, and quantitative and qualitative narrative analyses. Prior research examining
informativeness of analyst research shows that each element contains distinct information
(Francis and Soffer 1997; Brav and Lehavy 2003; Asquith et al. 2005). In theory,
earnings forecasts provide information regarding expectations of future cash flows, and
stock recommendations reflect assessment of intrinsic values relative to stock prices.
Within traditional security valuation frameworks, firm value is positively related to future
cash flows and negatively related to future discount rates (Ohlson 1995). Therefore,
earnings forecasts reflect a subset of information used to generate stock recommendations.
Concordant with this view, Francis and Soffer (1997) show that stock recommendations
provide incremental information to earnings forecasts. In line with their argument that
investors can use earnings forecasts to assess the degree of mispricing (which is not well-
captured by recommendations due to their discrete nature), Francis and Soffer (1997) find
that earnings forecasts are informative after controlling for recommendations.
Later studies examining the mapping of forecasts to recommendations provide
further explanation for the incremental information in forecasts, suggesting that analysts
do not effectively translate forecasts into recommendations. Bradshaw (2004) shows that
stock recommendations are inconsistent with valuation estimates based on residual
income models using analysts’ earnings forecasts, and he argues that analysts have
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incentives to bias recommendations upward to please managers and increase stock sales.
This notion conforms to findings that analysts are more biased in their recommendations
than their forecasts (e.g., Dugar and Nathan 1995; Lin and McNichols 1998).5
Ke and Yu (2009) propose that behavioral biases, such as analyst use of investor
sentiment, lead analysts to rely too little on their forecasts for their recommendations.
Bagnoli et al. (2009) show analysts issue inferior recommendations when using investor
sentiment rather than firm fundamentals. In sum, analysts’ economic incentives and
behavioral biases lead them to sub-optimally use value relevant information in making
recommendations.6 These prior studies assess whether stock recommendations properly
incorporate future earnings information by comparing stock recommendations and stock
intrinsic values estimated from residual income models, which use one-to-five year ahead
earnings, book value and discount rate as inputs. Our measure of consistency using
changes in stock recommendations and changes in one-year ahead earnings is simpler and
captures the essence of the underpinnings of residual income models as one-year ahead
earnings changes are highly correlated with longer-horizon earnings changes
(Bandyopadhyay et al. 1995), and dividend payout ratios and discount rates change little
over short-horizons. Therefore, we expect investors to perceive stock recommendations
that are consistent with earnings forecasts to be more reliable, thus paying them more
heed. More formally, our first hypothesis is:
5 Ertimur et al. (2007) find that analysts facing conflicts of interest do not translate more accurate forecasts into more profitable recommendations. Barniv et al. (2009) and Chen and Chen (2009), who examine how recent regulations have impacted analysts’ use of forecasts, conclude that recent regulations have mitigated the influence of investment banking relationships and strengthened analysts’ translational effectiveness. 6 Kesckes et al. (2009) argue earnings-based recommendation changes are less likely to be subject to analysts’ incentive and behavioral biases if they are characterized by hard information that is relatively more ex post verifiable than discount rate-based recommendation changes. In accordance with their argument, they find that investors react more strongly to earnings-based recommendation changes than to discount rate-based recommendation changes.
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H1: The market reaction to an analyst’s stock recommendation is greater if it isconsistent with the same analyst’s earnings forecast.
While recommendations that are consistent with forecasts have fewer behavioral
and incentive biases, earnings forecasts that are consistent with recommendations contain
superior (more precise) information. Analysts face conflicting incentives to generate
trade commissions and to build their reputations through issuing valuable forecasts and
recommendations (Hong and Kubik 2003; Jackson 2005; Simon and Curtis 2011).7 We
argue that the benefit of building reputations is greater when analysts possess superior
earnings information because they can use it to make more informative
forecasts/recommendations than other analysts. The cost of issuing biased research
becomes greater because it is easier to observe such biases ex post when analysts
disregard valuable earnings information. As a result, analysts are less likely to ignore or
distort earnings information and to issue inconsistent recommendation-forecasts. In
addition to mitigating the influence of analysts’ economic incentive biases, precise
earnings information reduces the influence of behavioral biases as the effect of investor
sentiment on security prices is low when uncertainty regarding firm fundamentals is low
(Baker and Wurgler 2006). Therefore, consistency indicates that analysts’ earnings
forecasts are more reliable and of higher quality. More formally, our second set of
hypotheses is:
H2A: The market reaction to an analyst’s earnings forecast is greater if it is consistent with the same analyst’s stock recommendation.
H2B: An analyst’s earnings forecast which is consistent with the same analyst’s recommendation is of higher quality.
7 Simon and Curtis (2011) propose that more accurate analysts are more concerned about their reputationsand are less likely to bias their stock recommendations. They find that accurate analysts are more likely to use rigorous valuation models rather than optimistic growth-based heuristics in making recommendations.
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DATA DESCRIPTION AND RESEARCH DESIGN
Classifications of Recommendation-Forecast Consistency
We classify recommendation-forecast consistency based on the information in the
recommendation and the forecast relative to their respective consensus. Our measure is
based on the assumption that analysts know both the consensus recommendation and
consensus forecast, and convey their incremental information through their individual
recommendations and forecasts. Consistency (CON) exists if both the recommendation
and the forecast are above (below) their consensus. To calculate the consensus, we use
the mean of all recommendations (forecasts) issued during the 90-calendar days prior to
the joint recommendation-forecast date. Appendix 1 provides illustrations of consistency.
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Insert Appendix 1 here
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Data Selection Procedures
We obtain earnings forecasts and stock recommendations from Thomson Reuters
I/B/E/S and stock returns from CRSP. Because IBES initiated recommendations in 1993,
our sample is for the 15-year period, 1993-2007. IBES codes recommendations using a
five-point scale, ranging from 1 (strong buy) to 5 (strong sell).8 To make interpretation
of our results more intuitive, we reverse this coding so that higher numbers indicate more
favorable recommendations (i.e., 1 for strong sell and 5 for strong buy). We require our
sample observations to meet several requirements. First, analysts must make earnings
8 Ljungqvist et al. (2009) report that recommendations data in the I/B/E/S were altered and differed between datasets downloaded at different times between 2000 and 2007. They state that Thomson Financialhas reinstated missing analyst names in the recommendation history file as of February 12, 2007. Our dataset uses reinstated data.
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forecasts and recommendations on the same day so consistency can be defined
unambiguously. Second, firms must be followed by at least three analysts during a
calendar year to allow for reliable estimates of the earnings forecast and the stock
recommendation consensus. Third, an analyst must issue at least two forecasts for a firm
during a given year to ensure that our sample is comprised of analysts who follow a firm
actively in a given year. Fourth, we omit cases where either the forecast or the
recommendation equals its consensus to ensure consistency is defined unambiguously.
Fifth, we require appropriate stock return data to examine market reactions to forecasts
and recommendations.
These requirements result in 92,764 observations for our market reaction tests.
Sample sizes vary for our forecast quality tests due to data requirements and
methodological choices so we report sample sizes in all tables for clarity. Table 1
summarizes the selection procedures for our market reaction test sample.
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Insert Table 1 here
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Market Reaction Tests
Following prior research examining informativeness of analyst research (Francis
and Soffer 1997; Asquith et al. 2005; Kadan et al. 2009), we focus on short-window
market reactions to information contained in recommendations and forecasts by
estimating the following model for both our full sample and for buy, hold, and sell
where LFR = Analyst i’s forecast timeliness for firm j in year t, calculated as raw leader-
follower ratio for analyst i following firm j in year t minus the minimum raw leader-
follower ratio for analysts following firm j in year t, with this difference scaled by the
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range of the raw lead-follower ratio for all analysts following firm j in year t. The raw
leader-follower ratio is the cumulative number of days between the two immediately
preceding forecasts and the forecast of interest dividend by the cumulative number of
days between the two immediately succeeding forecasts and the forecast of interest. 10 A
positive coefficient on CON (our key variable) indicates that consistent earnings forecasts
are timelier than inconsistent ones.
RESULTS
Consistency Frequency
Table 2 reports the consistency frequency (CON%) between stock
recommendations and earnings forecasts. Panel A shows that the two signals are
consistent 58.3% of the time. Panel B shows consistency by recommendation level. For
brevity and for conformance with other studies (for example, Francis and Soffer 1997),
we combine strong buys with buys and refer to them hereafter as buys; and we combine
strong sells with sells and refer to them hereafter as sells. Similar to Barber et al. (2006),
the number of buys dominates the number of sells (50,281 versus 6,602). Consistency is
more evident for sells than for holds, and holds than for buys (71.8% for sells, 62.0% for
holds, and 53.9% for buys). This finding suggests that analysts are more likely to be
consistent when they make less favorable recommendations. Panel C reports consistency
frequency by year. There is little variation during the 15-year sample period, 1993-2007,
ranging from a low of 55.2% in 1993 to a high of 61.0% in 2002.
10 Consistent with Cooper et al. (2001), we exclude any additional forecasts the analyst in question makes during the pre- or post-release periods. When more than one analyst releases a forecast revision on a given day, we exclude the additional forecasts from our computations of the cumulative lead and follow ratio for the analyst.
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Insert Table 2 here
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Market Reactions to Joint Recommendation and Forecast Announcements
Panel A of Table 3 reports mean three-day cumulative abnormal returns,
recommendation revisions, and forecast revisions partitioned by recommendation level.
Returns to buys, holds, and sells decrease monotonically from 1%, -3.9%, and -5.1% in
the three day period. This result conforms to prior research (Womack 1996) showing that
buys (holds and sells) yield positive (negative) returns. The mean three-day abnormal
returns to consistent buys, holds, and sells are 1.6%, -5.5%, and -6.2%, which are an
order of magnitude larger than the 0.3%, -1.3%, and -2.4% returns to inconsistent buys,
holds, and sells. The last column in Table 3 shows that these differences are significant at
the 0.01 level.
On average, more favorable recommendations are associated with more favorable
recommendation revisions and forecast revisions. Recommendation (forecast) revisions
for buys, holds, and sells are 0.694, -0.731, and -1.749 (-0.002, -0.006, and -0.011).
Moreover, consistent buys (holds and sells) are more likely than inconsistent buys (holds
and sells) to be accompanied by favorable (unfavorable) recommendation and forecast
revisions. The last column in Table 3 shows the mean recommendation (forecast)
revision is 0.061 (0.007) higher for consistent buys than for inconsistent buys, the mean
recommendation (forecast) revision is -0.184 (-0.011) lower for consistent holds than for
inconsistent holds, and the mean recommendation (forecast) revision is -0.039 (-0.023)
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lower for consistent sells than for inconsistent sells. All differences are significant at the
0.01 level.
Our results in a univariate setting are not definitive regarding whether consistent
recommendations and forecasts are more informative because the observed greater
returns may simply reflect the additive effects of the two signals. In order to provide
more reliable evidence, we use a multivariate approach. Panel B of Table 3 reports
multiple regression analyses. Column 1 of panel B reports that in our baseline model the
coefficients on REC_REV×CON and EARN_REV×CON are positive and significant,
suggesting that consistent recommendations and forecasts receivable stronger market
reactions than inconsistent ones. More specially, consistent recommendations (forecasts)
result in market reactions that are nearly four times the magnitude of those to inconsistent
ones, i.e., (0.019+0.005)/0.005 and (0.796+0.223)/0.223. Column 2 of panel B reports
that in our full model with additional analyst and forecast controls, the coefficients on
REC_REV×CON and EARN_REV×CON remain positive and significant, suggesting
that stronger market reactions related to consistency cannot be fully explained by other
factors that affect market response.11 Overall our findings are consistent with H1 and
H2A, suggesting that consistent recommendations and forecasts are more informative
than inconsistent ones.
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Insert Table 3 here
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11 The coefficients on REC_REV and EARN_REV become negative when we control for other analyst and forecast characteristics, consistent with market reactions being explained by proxies for analyst and forecast qualities. We are interested in the interaction effect of these variables with CON not their main effects.
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Quality of Consistent Earnings Forecasts
Panel A of Table 4 compares consistent and inconsistent analyst-firm-years with
respect to their other forecast characteristics. Relying on standardized, relative measures
(Clement and Tse 2003), our descriptive analyses of forecasting characteristics indicate
that consistent earnings forecasts are more accurate and timelier than inconsistent ones.
Consistent forecasts tend to be made by those analysts who are more experienced, issue
more forecasts for the firm, work for larger brokerage houses, forecast earlier in the
period, and have better track records in forecasting the firm’s earnings, attributes the
literature has shown are indicative of higher quality analysts (Mikhail et al. 1997;
Clement 1999; Jacob et al. 1999; Brown 2001; Cooper et al. 2001)
Panel B of table 4 reports results from estimating equation (2) to evaluate the
accuracy of consistent versus inconsistent forecasts. The estimation reveals a positive and
significant coefficient on the consistency indicator variable, CON = 0.007 (t = 2.16).
Panel B reports results from estimating equation (3) to evaluate the timeliness of
consistent versus inconsistent forecasts. The estimation also reveals a positive and
significant coefficient on CON = 0.257 (t = 7.90). In accordance with H2B, consistent
analysts make more accurate and timelier forecasts than inconsistent ones.12
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Insert Table 4 here
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12 We examine if our results are contaminated by earnings information disclosed around the time of the joint recommendation-forecast date since it is plausible that our previous results capture investors’ response to earnings announcements rather than to recommendation-forecast consistency. To examine this potential validity threat, we omit all recommendation-forecast pairs issued within the three day period (-1, +1) surrounding earnings announcements. Our findings regarding H1 and H2A are robust to this potential validity threat.
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An alternative measure of consistency
Another way to define a recommendation-forecast pair as consistent is that the
recommendation and the forecast issued on the same day are above or below the same
analyst’s prior recommendation and forecast13. Because most analyst recommendations
are reiterations and analysts do not always update their earnings forecasts in a timely
manner, this definition leads to considerable loss of sample size, limiting the
generalization of our results and the practical use of the consistency measure we used
above.
Using this alternative measure, we obtain 41,659 recommendation-forecast pairs
for the years 1993-2007. 14 When we estimate Equation (1) using the full sample of
13,737 recommendation-forecast pairs with nonmissing analysts and forecast controls,
we find that the coefficient on EARN_REV×CON is 0.033 (t = 26.5) and the coefficient
on EARN_REV×CON is 0.463 (t = 5.0). When we estimate Equations (2) and (3), we
find that the coefficient on CON is 0.022 (t = 3.05) when ACCUR is the dependent
variable and 0.030 (t = 3.71) when LFR is the dependent variable. Thus, our results are
robust to using this alternative measure of consistency.
CONCLUSIONS
Analysts often issue stock recommendations and earnings forecasts
simultaneously. We investigate how investors evaluate these two signals conditional on
their consistency. We define a recommendation-forecast pair as consistent if both the
analyst’s stock recommendation and earnings forecast are above or below their prevailing
13 This measure assumes that analysts convey new information through the difference between their current and prior recommendation (forecast) (Stickel 1992).14 We require the prior recommendation and forecast to be issued no more than 1 year before the current recommendation-forecast announcement day. To reduce further sample attribution, we do not require the prior recommendation and forecast to be issued on the same day.
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mean consensus, and inconsistent otherwise. We document that consistent
recommendations (forecasts) result in much stronger market reactions than inconsistent
ones after controlling for other analyst and forecast characteristics that affect market
responses. We also show that consistent forecasts are more accurate and timelier than
inconsistent forecasts. When combined with our market reaction tests, our results suggest
that consistent recommendation-forecasts are qualitatively different from inconsistent
ones.
Our study extends the literature on informativeness of analyst research by
showing that recommendation-forecast consistency is an important ex ante signal
investors appear to use to react to analyst research reports. 15 We add to the analyst
forecast quality literature by documenting that consistency is positively associated with
forecast accuracy and timeliness, two heavily researched ex post measures of earnings
forecast quality.
We close with some suggestions for further research. We have confined our
analyses to a particular type of consistency and to one-year-ahead earnings forecasts.
Future research should consider other types of consistency, such as short horizon versus
long horizon forecasts, forecasts by managers versus analysts, and forecasts of other
value-relevant metrics such as book values or dividend payout ratios (Ohlson 1995).
Other types of consistency than those considered in this study may provide insights into
the valuation relevance and quality of other signals.
15 The observed reaction to our ex ante measure is incremental to the ex post measures of forecast accuracy and timeliness.
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APPENDIX 1Examples of Recommendation-Forecast Consistency and Inconsistency
An analyst made a buy recommendation and earnings forecast of $1.60 per share for a company on 1/21/2003. The actual EPS was $1.45 per share.
IBES codes strong buy, buy, hold, sell, and strong sell as 1, 2, 3, 4, and 5. To make interpretation more intuitive, we reverse the IBES coding so higher numbers indicate more favorable recommendations (i.e., 5 for strong buy). To define consistency, we first determine the recommendation consensus and the earnings consensus by using all recommendations and earnings forecasts issued 90 days prior to but excluding the joint recommendation-forecast date.
Stock Recommendations Earnings ForecastsIssue Date Code Text Issue Date Value10/28/2002 3 Hold 12/20/2002 1.5512/19/2002 1 Strong Sell 12/20/2002 1.5912/23/2002 3 Hold 12/19/2002 1.5312/24/2002 3 Hold 12/6/2002 1.56
11/13/2002 1.61Mean
Recommendation Consensus
Mean Forecast Consensus2.5 1.568
The analyst's buy recommendation (coded 4) is above the recommendation consensus of 2.5 and his/her earnings forecast of $1.60 per share is above the forecast consensus of $1.568. This analyst is consistent (CON). If his/her recommendation was a sell and his/her forecast was $1.60/share, the analyst would be inconsistent (INCON).
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TABLE 1Sample Selection
All I/B/E/S stock recommendations issued for U.S. firms with nonmissing analyst codes from January 1993 – December 2007 446,331
Less: Recommendations unaccompanied by a one-year ahead earnings forecast issued on the same calendar day (250,952)
Less: Duplicate observations (4,692)
Less: Firms followed by fewer than three analysts in a year (56,032)
Less: Analysts issuing only one forecast for the firm in a year (21,239)
Less: Recommendations equal to the recommendation consensus orearnings forecasts equal to the earnings forecast consensus (19,620)
Less: Firms with missing stock return data from CRSP (1,032)
Market reaction test sample 92,764
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TABLE 2Consistency between Analysts' Recommendations and Forecasts
Panel A: Consistency for the full sampleNo. of Total
ObservationsNo. of Consistent
Observations CON%Full Sample 92,764 54,061 58.3%
Panel B: Consistency by recommendation level Recommendation
Notes: This table shows the number of recommendation-forecast pairs in our sample, partitioned by recommendation level and fiscal year. Data are from I/B/E/S for the 15 years, 1993-2007. CON% indicates the percent of recommendations that are consistent with the same analyst’s earnings forecast. An analyst is defined as consistent if both his/her recommendation and earnings forecast are above or below their respective prevailing consensus. CON is coded 1 if the recommendation-forecast is consistent and 0 otherwise. The recommendation (forecast) consensus is the mean of the distribution of all recommendations (forecasts) issued for the firm during the 90-day period prior to (excluding)the joint recommendation-forecast issuance date. Buy includes both buy and strong buy recommendations. Sell includes both sell and strong sell recommendations.
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TABLE 3Market Reaction to Stock Recommendations and Earnings Forecasts Conditional on Consistency
Panel A: Univariate comparison of abnormal returns, recommendation revisions, and forecast revisionsRecommendation Variable ALL CON INCON DIFF
Controls No Yes Yes Yes YesYear Dummies Yes Yes Yes Yes YesTotal number of observations 92,764 44,537 23,620 17,750 3,167Adjusted R2 12.82% 14.60% 5.23% 15.10% 13.64%
Notes: This table reports market reaction to analysts’ stock recommendations and forecasts conditional on consistency. Panel A shows mean cumulative abnormal returns, recommendation revisions, and earnings forecast revisions by recommendation level. Panel B reports results of 3-day abnormal stock returns regressed on joint recommendation and forecast revisions. CON is defined in Table 2. CAR (-1, +1) = 3-day cumulative abnormal returns, calculated as cumulative raw returns minus cumulative value-weighted market returns from days -1 to +1, where day 0 is the joint recommendation-forecast announcement date by analyst i for firm j in year t. REC_REV = recommendation revision, measured as the difference between analyst i’s recommendation and the mean recommendation consensus defined in Table 2. EARN_REV = earnings forecast revision, measured as the difference between analyst i’s forecast and the mean forecast consensus defined in Table 2, scaled by stock price two days before the joint announcement date of the forecast and recommendation by analyst i. Controls consist of ACCUR (forecast accuracy), LFR (timeliness), FEXP (firm experience), NFIRMS (number of firms followed), FREQ (forecast frequency), BSIZE (brokerage size), HORIZON (forecast horizon), and PACCUR (past accuracy). Detailed definitions of these control variables are provided in Table 4. T-statistics based on standard errors adjusted for both heteroskedasticity and intra-analyst error correlation. * (**) indicates significance at the 0.05 (0.01) level.
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TABLE 4Analyst Forecast Quality Conditional on Consistency
Panel A: Comparison of scaled forecast and analyst characteristics for consistent and inconsistent forecasts
Total number of observations 49,879 44,560Adjusted R2 23.47% 0.81%
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Notes: This table reports results on analysis of analyst forecast quality. Panel A compares characteristics of analysts who make consistent versus inconsistent forecasts. Panel B reports regression analyses of earnings forecast quality on forecast consistency and control variables. CON isdefined in Table 2. ACCUR = Analyst i’s forecast accuracy for firm j in year t, calculated as the maximum absolute forecast error for analysts who follow firm j in year t minus the absolute forecast error of analyst i following firm j in year t, with this difference scaled by the range of absolute forecast errors for analysts following firm j in year t; LFR = Analyst i’s forecast timeliness for firm j in year t, calculated as the raw leader-follower ratio for analyst i following firm j in year t minus the minimum raw leader-follower ratio for analysts following firm j in year t, with this difference scaled by the range of raw leader-follower ratios for analysts following firm j in year t. Raw leader-follower ratio is the cumulative number of days between the two immediately preceding forecasts and the forecast of interest divided by the cumulative number of days between the two immediately succeeding forecasts and the forecast of interest. FEXP = Analyst i’s firm experience, calculated as the number of prior forecasting years for analyst i following firm j in year t minus the minimum number of prior forecasting years for analysts following firm j in year t, with this difference scaled by the range of prior forecasting years for analysts following firm j in year t. NFIRMS = Number of firms followed, calculated as the number of firms followed by analyst i in year t minus the minimum number of firms followed by analysts covering firm j in year t, with the difference divided by the range of the number of firms followed by all analysts covering firm j in year t. FREQ = Analyst i’s forecast frequency, calculated as the number of firm j forecasts made by analyst i following firm j in year t minus the minimum number of firm j forecasts for analysts following firm j in year t, with this difference scaled by the range of number of firm j forecasts issued by analysts following firm j in year t. BSIZE = Analyst i’s brokerage size, calculated as the number of analysts employed by the brokerage house employing analyst i following firm j in year t minus the minimum number of analysts employed by a brokerage house for analysts following firm j in year t, with this difference scaled by the range of brokerage size for analysts following firm j in year t. HORIZON = Analyst i’s forecast horizon, calculated as days from the forecast date to the earnings announcement date for analyst i following firm j in year t minus the minimum forecast horizon for analysts following firm j in year t, with this difference scaled by the range of forecast horizons for analysts following firm j in year t. PACCUR = Analyst i’s past accuracy, calculated as the maximum absolute forecast error for analysts followingfirm j in year t-1 minus the absolute forecast error of analyst i following firm j in year t-1, with this difference scaled by the range of absolute forecast errors for analysts following firm j in year t-1. T-statistics are based on standard errors adjusted for both heteroskedasticity and intra-analyst error correlation. * (**) indicates significance at the 0.05 (0.01) level.
