Financial Analyst Journal

The Impactof Stock Recommendation-Earnings Forecast Consistency on Forecast Accuracy and Recommendation Profitability

Lawrence D. Brown

GeorgiaStateUniversity

and

Kelly Huang

GeorgiaStateUniversity

October 2009

We gratefully acknowledge the helpful suggestions of Jeffrey Callen, Xia Chen, Qiang Cheng, Ole-Kristian Hope, Hai Lu, Gordon Richardson, Kent Womack, Franco Wong and the participants of the University of Torontoand University of Wisconsinworkshops.

The Impact of Stock Recommendation-Earnings Forecast Consistency on Forecast Accuracy and Recommendation Profitability

Abstract

We examine how the consistency between an analyst’sstock recommendation and her one-year-ahead earnings forecast impacts forecast accuracy and stock recommendation profitability. Defining consistency as the analyst’sstock recommendation and earnings forecast areboth above (below) theprevailing consensus, weshow analysts areconsistentless than 60% of the time. We find consistent analysts to make relatively more accurate forecasts and more profitable recommendations. We benchmark the importance of consistency on recommendation profitability in two ways. First, we compare ex ante consistency with ex post earnings forecast accuracy. We show that consistency is more valuable for recommendation profitability than is forecast accuracy, and that perfect foreknowledge of earnings forecast accuracy is more valuable when it is combined with consistency. Second, we compare consistency with ex anteboldness. We demonstrate that consistency is more valuable for recommendation profitability than is boldnessand that boldness is more valuable when it is combined with consistency.

The Impact of Stock Recommendation-Earnings Forecast Consistency on Forecast Accuracy and Recommendation Profitability

1. Introduction

We investigate howstock recommendation-earnings forecast consistency impacts earnings forecast accuracy and recommendation profitability. We define consistency as thesame analyst’s stock recommendationandearnings forecast are above (below)the respective consensus. Otherwise, the analyst is said to be inconsistent regarding the stock recommendation-earnings forecast in question.[1]

We show that consistent analysts, who constitute nearly 60% of our sample, make more accurate earnings forecasts and more profitable recommendations. Our evidence is consistent with the notion that consistent analysts are more likely than inconsistent analysts to focus on firm fundamentalsand/or possessbetter information regarding firms’future prospects, making their earnings forecasts and recommendations more precise.[2]

The literature examining the relation between stock recommendation profitability and earnings forecast accuracy (Loh and Mian 2006; Ertimur, Sunder and Sunder 2007) shows analysts who make more accurate earnings forecastsex postmake more profitable stock recommendationsex ante. We benchmark the impact of ex anteconsistency on stock recommendation profitability by comparing it with ex post accuracy’s impacton stock recommendation profitability.[3] We find consistency is more valuable than accuracy for recommendation profitability, and accuracy ismuch more useful when it is employed in conjunction with consistency.

The literature examining the relation between stock recommendation profitability and recommendation boldness (Jagadeesh and Kim 2009, Loh and Stulz 2009) has shown that bold recommendations are more profitable than non-bold recommendations. As a second way to benchmark the impact of consistency on recommendation profitability, we examine boldness, anotherex ante measure for improving recommendation profitability. We show consistency is more valuable than boldness for recommendation profitability, and that boldness is far more useful when it is used in conjunction with consistency.

In sum, stock recommendation-earnings forecast consistency is important for both accuracy of analysts’ earnings forecasts and profitability of their stock recommendations. Indeed, it is more important for accuracy than most factors heretofore shown to explain accuracy, and it is a more important determinant of recommendation profitability than eitherforecast accuracy or recommendation boldness. Moreover, forecast accuracy and boldness are far more valuable when they are used in conjunction with consistency.

Our study hasseveral important implications. First, we add to the forecasting literature by showing that consistency is an important determinant of analyst forecast accuracy. Second, we add to the stock recommendation literature by providing a simple ruleinvestors can use to increase their expected returns from stock recommendations, and a benchmark academics can use to comparethe profitability of other trading rules.Third, we add toearnings forecast accuracy-recommendation profitability literature by showing that investors should heed the advice of accurate analysts who are consistent, but ignore advice of accurate analysts who are inconsistent. Fourth, we supplement the literature on profitability of bold recommendations by showing that investors are far better off heeding the advice ofbold, consistent analyststhan bold, inconsistent analysts.

Weproceed as follows. Section 2 discusses the related literature and puts forth our hypotheses. Section 3 describesour data selection and research design. Section 4 presents our results. Section 5 reports our additional analyses and section 6 concludes.

2. Related Studies and Hypotheses

Early studies showed that stock returns are associatedwith both analysts’ stockrecommendations (e.g., Womack 1996) and their earnings forecasts (e.g.,Lys and Sohn 1990). Later studies investigatedthe informativeness of analyst reports (e.g., Francis and Soffer 1997, Brav and Lehavy 2003, Asquith, Mikhail, and Au 2005). These studies generally concluded that each output of an analyst’s report contains distinct information (e.g. recommendations, earnings forecasts, and target prices) and the market’s reaction to each output depends on the nature of the other outputsin the report.

More recent studies consider the role of earnings forecasts as inputs in generating recommendations and assessif analysts translate their earnings forecasts into profitable recommendations. Bradshaw (2004) shows analysts rely on simple heuristics, e.g., price-earnings-growth ratios, for their recommendations, andinvestors can earn higher returns by using present value models incorporating analysts’ short-term and long-term earnings forecasts.Barniv, Hope, Myring, and Thomas (2009) and Chen and Chen (2009) examinehow recent regulationshave impacted analysts’ use of earnings forecasts, and conclude they have mitigated the influence of investment banking relationships and strengthened analysts’ translational effectiveness.

Prior research suggests that analysts are more biased intheir stock recommendations than their earnings forecasts(e.g. Dugar and Nathan 1995, Lin and McNichols 1998).More recently, Malmendier and Shanthikumar(2007) find that analysts with conflicting interests may distort recommendations upwards to trigger small-investor purchases and to please management, but may distort forecasts downwards before earnings announcements to allow managers to report earnings that meet or beat the earnings forecast. Ke and Yu (2009) confirm that conflicts of interest, such as investment banking and intuitional ownership pressure, lead analysts tonot use their forecasts in their recommendations, and behavioral biases, such asanalysts’ use of investor sentiment, lead analysts torely too little on their earnings forecastsfortheirrecommendations. Bagnoli, Clement, Crawley, and Watts(2009)show analysts do not effectively utilize investor sentiment, and issue less profitable recommendations when they use sentiment, ignoring firm fundamentals.In sum, both analysts’ economic incentives and cognitive biases lead them to sub-optimally use value relevant information in making their stock recommendations. We suggest that consistency between two information signals, earnings forecasts and recommendations,indicatesthat analysts make more efficient use of value-relevant information in both their earnings forecasts and their stock recommendations.

Apart from analyst biases, the precision of the information analysts receive affects their use of the information. All signals contain a mixture of information and noise. The lower the amount of noise relative to the amount of information, the greater is the signal’s precision and thusits potential usefulness. Consistency between an analyst’s earnings forecast and her recommendation reflects the strength and precision of her information. Information signalsthat are stronger and/or more precise are more likely to be translated into consistent recommendations and earnings forecasts. If an analyst’s earnings forecast is more precise, it should be more accurate. If an analyst’s stock recommendation is more precise, it should be more profitable. More formally,our firsttwo hypotheses are:

H1:Consistent analysts’ earnings forecasts are more accurate.

H2:Consistent analysts’ stock recommendations are more profitable.

Loh and Mian (2006) demonstrate that analysts who make more accurate earnings forecasts ex postmake more profitable stock recommendationsex ante. Ertimur, Sunder, and Sunder (2007) extend this literature by considering analysts’ conflicts of interest, value relevance of earnings, and regulatory regime. They showthe relation between earnings forecast accuracyand recommendation profitabilityis related to these three factors, but they do notexaminewhether recommendation-earnings forecast consistency impacts the relation between forecast accuracy and recommendation profitability.

Ball and Brown (1968) and scores of subsequent studies show that pre-knowledge of one-year-ahead earnings is value relevant. The “Ball and Brown effect” suggests that investors with advance knowledge of ex postforecast accuracy should take long (short) positions in stocks that accurate analysts contendwill have earnings increases (decreases). Thus, investors should heed stock recommendations of accurate analystsif their stock recommendations are consistent with their earnings forecasts. On the other hand, when accurate earnings forecasters make recommendationsthat are inconsistent with their earnings forecasts, stock priceswill move in the direction of theirearnings forecasts so investors should pay less heed to theirstock recommendations. More formally, our third hypothesis, whichpertains to accurate earnings forecasters, is:

H3: Stock recommendations of accurate earnings forecasters are more relatively more profitable when they are consistent with their earnings forecasts.

Analyst herding has received considerable attention in the forecasting literature (Hong, Kubik, and Solomon 2000, Clement and Tse 2005). Some studies conclude that analysts herd towards consensus based on little information (e.g. Welch 2000). Jagadeesh and Kim (2009) and Loh and Stulz(2009) find recommendation changes that are further away from the consensus recommendation generate higher returns. Other studies (e.g., Chen and Jiang 2006) argue that herding is attributable to analysts’ responses to common information and, after controlling for common information, analysts move away from the consensus (i.e., they “anti-herd”). We expect that when analysts are consistent, their bold recommendations are more likely to be based on more precise information regarding firm fundamentals. Our fourth hypothesis, which pertains to bold analysts, is:

H4: Stock recommendations of bold analysts are relativelymore profitable when they are consistent with their earnings forecasts.

3. Data Description and Research Design

We obtain stock recommendations and earnings forecasts from I/B/E/S and stock returns from CRSP. Because IBES initiated its 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). We require our sample to meet several requirements. First, analysts must make their recommendations andearnings forecasts on the same day so we can define recommendation-forecast consistency unambiguously. Second, firms must be followed by at least three analysts during a calendar year to allow reliable estimates of both the recommendation consensus and the earnings forecast consensus. Third, we only include analysts who issue at least three forecasts for the firm during a particular calendar year to ensure that the analyst follows the firm actively. Fourth, we eliminate cases when the earnings forecast equals the forecast consensus or the recommendation equals the recommendation consensus.[4] Fifth, we require appropriate stock return data forexamining the capital market’s reaction to recommendations. These requirements result in 67,447 analyst recommendation-forecast observations for our primary univariate tests. Sample sizes differ for different tests due to various data requirements and research design choices so we report sample sizes in all tables for purposes of clarity.

We classify recommendation-forecast consistency based on the information in the forecast (recommendation) relative to the respective consensus. This measure is based on the assumption that analysts know the consensus earnings forecast and the consensus recommendation, and convey their incremental information through their own earnings forecasts and recommendations. Consistency (hereafter CON) exists if both the stock recommendation is above (below) itsconsensus and the earnings forecast is above (below) its consensus. To calculate consensus, we use the mean of forecasts (recommendations) issued during the 90-calendar days prior to the joint recommendation forecast issue date.

Consistent with Loh and Mian (2006) and Ertimur et al. (2007), we classify a forecast as accurate (hereafter ACC) if it is ex post more accurate than the consensus. We investigate our first hypothesis by regressing ACC on CON and other determinants of analyst forecast accuracy (Mikhail, Walther and Willis 1997; Clement 1999; Jacob, Lys and Neale 1999; Brown 2001), i.e., firm experience (FEXP), number of firms (NFIRMS) followed, number of forecasts the analyst makes for the firm (FREQ), size of brokerage house the analyst works for (BSIZE), forecast horizon (HORIZON), and past accuracy (PACC).[5]To allow for comparisons of regression coefficients and to control for firm and year effects, we scale all variables (except ACC and HORIZON) to range from 0 to 1 using the method in Clement and Tse (2005). The transformed variables for analyst i take the form:

Characteristics i,j,t = Raw-Characteristic i,j,t - Min-Characteristic j,t

Range of Characteristicj,t

where Raw-characteristic is the value for analyst i and Min-characteristic (Range of characteristic) is the minimum value (range) of all analysts following firm j in year t. For forecast horizon, we rank the raw valuesof horizon into deciles and scale them into a range of (0, 1).[6]We model accuracy (ACC = 1 if accurate and 0 if INACC) with the following logistic regression with the scaledindependent variables defined above.

Prob (ACC=1) = α0 + α1CON+α2 FEXP+ α3NFIRMS+α4 FREQ+α5BSIZE+α6HORIZON+α7PACC (1)

If consistent analysts are more likely to be accurate(our first hypothesis), the coefficient on CON should be positive and significant.

We perform our return analyses over the 32-trading day period, the day before the recommendation-earnings forecast day through the 30th day after it. We start with the day before the recommendation-earnings forecast as information in analyst research often leaks to the capital markets before its official announcement (Irvine, Lipson, and Puckett 2007).[7] Recommendation profitability (PFT)is based onthe cumulative raw returns minus cumulative value-weighted market returns from day (-1, 30), where day 0 isthe joint recommendation-earnings forecast day.We measure recommendation profitability based on two alternative trading strategies. The first strategy takes long positions in buy recommendations, and short positions in hold and sell recommendations. The second strategy takes long positions in recommendations that are above the recommendation consensus and short positions in those that are below the recommendation consensus.

We assess the incremental effect of consistency on recommendation profitability (H2) by running the following multiple regression:

PFT = α0 + α1CON+α2 FEXP + α3NFIRMS +α4FREQ+α5BSIZE+α6LFR(2)

All the independent variables with the exception of LFR, the leader-follower-ratio, have been defined above. LFR represents the timeliness of analyst forecasts (Cooper, Day, and Lewis 2001) and is defined as the cumulative number of days between the two immediately preceding forecasts and the particular analyst’s forecast divided by the cumulative number of days between the two immediately succeeding forecasts and the particular analyst’s forecast. We include it for comparability with Ertimur et al. (2007). In model (2), α0 is the average return to inconsistent recommendations and α1 is the incremental return to consistent recommendations. If consistent analysts make more profitable recommendations (our second hypothesis) the coefficient on CON should be positive and significant.

We assess the impact of CONon recommendation profitability by benchmarking it againstthe impact of ACC on recommendation profitability. We run the following regression, which is identical to equation (2) except that it substitutes ACC for CON.

PFT = α0 + α1ACC+α2 FEXP + α3NFIRMS +α4FREQ+α5BSIZE+α6LFR (3)

The evidence in Loh and Mian (2006) and Ertimur et al. (2007) suggests that ACC should be positive and significant. We expect this result with our sample. In order to test our third hypothesis, we run the following regression, which contains CON, ACC and CON_ACC and the other control variables in equations (2) and (3).[8]

PFT = α0 + α1CON+α2 ACC + α3 CON_ACC + α4FEXP + α5NFIRMS +α6FREQ+α7BSIZE+α8LFR (4)

α0representsprofitability torecommendations issued by inconsistent, inaccurate analysts;α1 represents profitability to recommendations issued by consistent, inaccurate analysts;α2 representsprofitability to recommendations issued by accurate, inconsistent analysts; and α3 representsprofitability to recommendations issued by consistent,accurate analysts. If accuracy is more useful for recommendation profitability when analysts are consistent (our third hypothesis), α3 should exceedα2.

We also assess the impact of consistency for recommendation profitability by benchmarking it against boldness’s impactonrecommendation profitability. We classify recommendations as bold if the distance between the individual recommendation and the prevailing consensus recommendation is greaterthan one level (e.g., strong buy relative to a consensus of hold). If so, BOLD is a dummy variable equal to one 1 (otherwise 0). We run the following regression, which is identical to equation (3) except ituses BOLD in lieu of ACC.

PFT = α0 + α1BOLD+α2 FEXP + α3NFIRMS +α4 FREQ+α5BSIZE+α6LFR (5)

The findings ofJegadeesh and Kim (2009) and Loh and Stulz (2009)suggestthat BOLD should be positive and significant. We expect to find this result in our sample. In order to test our third hypothesis, we run the following regression, which is similar to equation (4)except it containsBOLDand CON_BOLD in lieu of ACC and CON_ACC.[9]

PFT = α0 + α1CON+α2 BOLD + α3 CON_BOLD + α4FEXP + α5NFIRMS +α6FREQ+α7BSIZE+α8LFR (6)

α1 is profitability to inconsistent, non-bold recommendations;α1 is profitability to consistent, non-bold recommendations;α2 is profitability to bold, inconsistent ones; and α3 is profitability to consistent,bold recommendations. If boldness is more useful for recommendation profitability when analysts are consistent (our fourth hypothesis), α3 should exceedα2.