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Journal of Accounting Research

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Journal of Accounting Research Vol. 40 No. 1 March 2002 Printed in U.S.A.

The Association Between Trading Recommendations and Broker-Analysts’ Earnings Forecasts M I C H A E L E A M E S ,∗ S T E V E N M . G L O V E R , † A N D J A N E K E N N E D Y‡ Received 9 February 1999; received in revised form 17 September 2001

ABSTRACT

This study examines analyst forecast errors within the context of stock recommendations. We predict positive forecast error (i.e., optimism) for buy recommendations and negative forecast error (i.e., pessimism) for sell recommendations. We offer two explanations for this prediction: (1) the unconscious tendency to process information in a manner that supports one’s goal, which we refer to as the “objectivity illusion” hypothesis, and (2) the economic incentive to boost trade, which we refer to as the “trade boosting” hypothesis. The pattern of analyst forecast bias we predict (i.e., optimism for buys and pessimism for sells) is opposite in direction to that predicted by the management relations hypothesis—a commonly cited hypothesis for analyst forecast bias. We find broker-analyst earnings forecast errors are significantly optimistic for buy recommendations and significantly pessimistic for sell

∗ Santa Clara University; † Brigham Young University and PricewaterhouseCoopers; ‡ University of Washington. We are grateful for the comments of Sudipta Basu, Robert Bowen, Dave Burgstahler, James Jiambalvo, Laureen Maines, Dawn Matsumoto, James Meyers, Eric Noreen, Kay Stice and participants at the 1999 Universities of British Columbia, Oregon, and Washington (UBCOW) Conference, the 1999 European Accounting Conference, the 1999 Western Regional Meeting of the AAA, the 1999 AAA Annual Meeting, the 2000 Globalization Conference, and accounting workshops at Brigham Young University, Santa Clara University, and the University of Utah. We thank the PricewaterhouseCoopers Foundation, the Mary and Ellis Fund at Brigham Young University and the Accounting Development Funds at the University of Washington and Santa Clara University for financial support. We thank Ralph Arvizu for his excellent research assistance. 85 C , University of Chicago on behalf of the Institute of Professional Accounting, 2002 Copyright

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M . EAMES , S . M . GLOVER , AND J . KENNEDY

recommendations, consistent with the objectivity illusion and trade boosting hypotheses. Our study indicates that the pattern of results reported in prior research (i.e., increasingly optimistic earnings forecasts as the stock recommendation becomes less favorable) is likely driven by a correlated omitted variable, actual earnings. Results of an analysis to distinguish between trade boosting and objectivity illusion appear more consistent with the objectivity illusion.

1. Introduction This paper investigates the influence of two factors on the accuracy of analyst earnings forecasts: the incentive to boost trading, and motivated reasoning—the tendency to process information in a manner that supports one’s goal. We predict these factors lead to forecasts that are greater than actual earnings (i.e., positive forecast errors or optimistic forecasts) in association with buy recommendations and forecasts that are less than actual earnings (i.e., negative forecast errors or pessimistic forecasts) in association with sell recommendations. The pattern of forecast error we predict is opposite to that predicted by the “management relations” hypothesis—a commonly cited hypothesis used to explain analyst forecast bias (Francis and Philbrick [1993]). The management relations hypothesis suggests that analysts intentionally issue optimistic earnings forecasts in order to curry favor with management, and this forecast optimism is particularly pronounced in association with sell recommendations because analysts wish to counter negative effects on the analystmanagement relation generated by sell recommendations. The empirical evidence supporting the management relations hypothesis (i.e., increasingly optimistic forecasts as the stock recommendation becomes less favorable) appears to be driven by a correlated but previously omitted variable—actual earnings. It is important to include earnings in the analysis of forecast errors and recommendations because both forecast errors and recommendations are significantly correlated with earnings. When we control for the relation between earnings and forecast errors we obtain results strikingly different from those reported in prior research. Based on an extensive broker-analyst data set, we find evidence that analyst earnings forecast errors are significantly optimistic for buy recommendations and significantly pessimistic for sell recommendations, consistent with both the incentive to boost trading and a form of motivated reasoning we label the objectivity illusion. Motivated reasoning, or the tendency to process information in a manner that makes the desired outcome more likely, is a well-known concept in the behavioral decision literature (e.g., Kunda [1990]).

2. Background and Hypotheses Research generally finds that analyst earnings forecasts are: (1) more accurate than time-series predictions of earnings (e.g., Affleck-Graves, Davis,


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TRADING RECOMMENDATIONS

87 Au:

and Mendenhall [1991], Brown, Griffin, Hagerman, and Zmijewski [1987]), (2) imperfect proxies for market expectations of earnings (Brown et al. [1987], O’Brien [1988]), (3) less accurate in predicting future earnings than a simple model incorporating analyst forecasts and past time-series properties of earnings (Ali et al. [1992]), and (4) optimistic on average (e.g., O’Brien [1988], Lys and Sohn [1990], Mendenhall [1991], Brown [1993], Dugar and Nathan [1995], Das, Levine, and Sivaramakrishnan [1998]). Attempts to explain this forecast optimism generally focus on analysts’ responses to institutional incentives.

2.1

MANAGEMENT RELATIONS AND TRADE BOOSTING

Francis and Philbrick [1993] hypothesize that analysts intentionally issue optimistic earnings forecasts to curry favor with management and thereby gain greater access to management’s private information (the “management relations hypothesis”). Francis and Philbrick contend that such intentional optimism is particularly pronounced in association with sell recommendations because analysts wish to counter negative effects on the brokermanagement relation generated by the sell recommendation. Consistent with their hypothesis, Francis and Philbrick find that Value Line analysts’ earnings forecasts are optimistic on average and that the extent of optimism in earnings forecasts increases as the Value Line timeliness ranking becomes less favorable. Several subsequent studies rely on the management relations hypothesis (e.g., Das et al. [1998], Lim [1998], Dugar and Nathan [1995]) to generate predictions of intentional analyst forecast bias. Kim and Lustgarten [1998] suggest that broker-analysts have incentives to bias their forecasts to both maintain favorable relations with management and to stimulate stock trading. Their “trade boosting” hypothesis asserts that broker-analysts’ trade boosting incentives dominate management relations incentives, and result in more optimistically biased forecasts for buy than for hold stocks and more pessimistically biased forecasts for sell than for hold stocks. However, they find that broker-analysts’ earnings forecasts are more optimistically biased for sell and hold stocks than for buy stocks and conclude that, for broker-analysts, management relations incentives dominate trade boosting incentives. While the management relations hypothesis appears to be supported in the extant literature, this hypothesis conflicts with important institutional factors and recent academic research. Once actual earnings are announced, forecast errors for all publicly issued forecasts are easily computed. If reputation is important in the broker-analyst business, the negative reputation effects associated with inaccurate forecasts should reduce the incentive to issue intentionally biased forecasts.1 The management relations hypothesis also fails to consider the fact that managers have incentives to avoid negative 1 Analyst pay is often based, in part, on analyst position on the Institutional Investor All-American Research Team. Forecast accuracy is one of the four criteria used to determine membership on the team.

change R.R.H. ok?


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earnings surprises (i.e., managers seek to meet or beat analyst forecasts) and thus may be displeased with optimistic earnings forecasts. Burgstahler and Eames [2000] present evidence that managers alter both earnings and forecasts to avoid negative earnings surprises. Matsumoto [2001] argues that managers prefer relatively pessimistic forecasts for reasons including the market’s reaction to negative earnings surprises, and presents results consistent with this position. Numerous articles in the popular financial press (e.g., Ip [1997a, b], McGee [1997]) reiterate that optimistic earnings forecasts are not an effective way to curry management favor.

2.2

MOTIVATED REASONING, FORECAST ERROR, AND RECOMMENDATIONS

Individuals often make judgments in an environment in which they are motivated to reach a particular conclusion, such as to support a project or oppose a merger with another company. Normatively, an individual should consider all relevant information in an unbiased manner and make an accurate judgment without being influenced by motives to reach a particular conclusion. In reality, it is difficult to set aside existing preferences when processing information. Prior research suggests that motivation for a particular conclusion (i.e., directional motivation) may bias the judgment process. For example, individuals tend to evaluate information that is consistent with the desired conclusion less critically than contradictory information (Festinger [1957], Ditto and Lopez [1992], Snyder and Swann [1978]). Directionally motivated individuals adopt information-processing strategies most likely to yield the desired conclusion. This phenomenon has been labeled motivated reasoning (Kunda [1990]). Research on motivated reasoning suggests that, because individuals want to construct rational justifications for conclusions they wish to draw, they search for relevant information and construct beliefs that logically support their desired conclusion (Kunda [1990], and Boiney, Kennedy, and Nye [1997]). If they succeed in finding such information, they draw the desired conclusion while maintaining an illusion of objectivity. The objectivity of this process is considered illusory because individuals do not realize that the process is biased by their goals. They do not realize that they are accessing only a subset of their relevant knowledge and that they would probably access different beliefs and rules in the presence of different directional goals. They are unaware that they constructed their decision process to make the desired conclusion more likely. Security analysis provides the basis for selecting stocks to buy and sell. The ultimate judgment within a sell-side financial analyst’s formal report evaluating a firm’s securities is a “buy, sell, or hold” recommendation. Givoly and Lakonishok [1984] contend that “next to stock recommendations, earnings forecasts are perhaps the most notable output of financial analysts,” but forecasting earnings appears subordinate to the task of issuing stock recommendations and writing reports supporting these recommendations. Although a stock recommendation may follow a carefully prepared earnings


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forecast, there is evidence that earnings forecasts play a relatively minor role as input to analysts’ recommendation decisions (Balog [1991], Biggs [1984]). We posit that, in generating earnings forecasts, analysts tend to process information in a manner that biases forecasts in the direction that supports their stock recommendation. Therefore, we predict that analyst earnings forecasts will be optimistic for buy recommendations and pessimistic for sell recommendations. We do not expect hold recommendations to result in significant bias. We refer to the behavior leading to this pattern of bias as the objectivity illusion, consistent with the idea that while analysts may perceive their earnings forecasting behavior as objective, this objectivity is illusory. Our hypotheses focus on analyst forecast errors conditioned on stock recommendations. The null hypothesis is: H1:

Analyst earnings forecast errors are independent of stock recommendations.

Our discussion of prior research on intentional biases (i.e., management relations and trade boosting) and the objectivity illusion suggest the following two competing alternative hypotheses:2 H1a:

Analysts’ earnings forecast optimism increases as the stock recommendation becomes less favorable (consistent with the management relations hypothesis).

H1b:

Analyst earnings forecasts are optimistic for favorable stock recommendations and pessimistic for unfavorable stock recommendations (con-sistent with the objectivity illusion and trade boosting hypotheses).

3. Data We obtain individual analyst recommendations and actual and forecasted earnings-per-share (EPS) values for the years 1988 to 1996 from the Zacks Investment Research database.Recommendations in the database are expressed on a scale of 1.0 to 6.0 where: 1.0 = strong buy, 1.1 to 2.0 = moderate buy, 2.1 to 3.0 = hold, 3.1 to 4.0 = moderate sell, 4.1 to 5.0 = strong sell, and 6.0 denotes that a recommendation is not available for the particular analyst and time period. Zacks adds records to the database when an analyst initiates coverage of a firm and issues a first recommendation, when an analyst changes a previous recommendation, and when an analyst issues a recommendation and the previously recorded recommendation in the database is at least a year old.

2 None of the alternative hypotheses specifically mentions hold recommendations. However, we anticipate that earnings forecast errors associated with such intermediate recommendations will lie between observations for more extreme recommendations.


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The Zacks earnings forecast and earnings surprise databases include analyst forecasts of EPS and actual EPS, in conformance with Zacks’ proprietary definition of operating earnings-per-share before extraordinary and nonrecurring items. For each fiscal period, the earnings forecast database includes numerous forecasts released at various dates by a number of analysts. Each annual EPS forecast record in the Zacks earnings forecast database represents a forecast associated with the initiation of coverage, a change in an analyst’s forecast for a fiscal period, or the reiteration of a previous forecast that is at least 120 days old. We scale total earnings and total forecast values by beginning of the year market value of equity.3 Scaling by beginning of the year market value of equity provides for consistency with prior research (e.g., Francis and Philbrick [1993] and Kim and Lustgarten [1998]) and controls for firm size. We obtain annual forecasted and actual earnings by multiplying Zacks annual EPS forecasts and Zacks actual EPS values by the number of common shares used to calculate EPS (Compustat annual data item # 54 × Compustat item # 27). We obtain beginning of the year market value of equity by multiplying common shares outstanding (Compustat item # 25) and price per share (Compustat item # 199), both obtained for the end of the prior fiscal year. We ensure comparability of forecasted and actual earnings by obtaining both of these numbers from Zacks. We use only December fiscal year-end firms for the years 1988 to 1996, and eliminate financial (SIC codes 4400 to 5000) and utility (SIC codes 6000 to 6500) firms. We match each individual analyst annual earning forecast recorded in January, February, and March with the contemporaneously reported recommendation by the same analyst if this is available. If there is no such contemporaneous recommendation then we match the forecast to the most recent preceding recommendation by the same analyst.4 For example, suppose an analyst issues a buy recommendation in August, a sell recommendation in the following December, and a month later in January issues an earnings forecast without an accompanying recommendation. Then the January forecast is matched to the December recommendation. However, had the analyst issued a recommendation on the same date as the January forecast then the contemporaneous recommendation, rather than the December recommendation, would have been matched to the forecast. We calculate scaled forecast errors, defined as the difference between forecasted and realized earnings divided by beginning of the year market value of equity, for 62,908 observations. We further limit the data to the 35,482 observations of annual earnings forecasts for the fiscal year 3 We

obtain essentially equivalent results when we scale by beginning of the year book value of equity and end of the year market value of equity. 4 We limit the sample to December year-end firms and January through March forecasts for consistency with prior research and to obtain more powerful tests, since annual earnings forecast accuracy is known to improve over time within the fiscal year (Burgstahler and Eames [2000]).


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ending from nine to eleven months after the date of forecast release (i.e., we eliminate forecasts issued after fiscal year-end and for years beyond the current fiscal year). For all analyses requiring analyst recommendations, we eliminate the 870 observations with unavailable recommendations (i.e., Zacks recommendation = 6) and employ a final sample size of 34,612.5 Sample distributions for observations by month, year and recommendation are presented in table 1 (panels A, B, and C). Panels A and B illustrate that sample observations are relatively evenly distributed between the months of the first quarter and approximately three quarters of the total observations come in the last five years of the nine years included in the sample. Panel C indicates that strong and moderate buy recommendations are much more frequent than strong and moderate sells. Table 1, panel D presents distribution statistics for calendar days between earnings forecasts and the most recently preceding or contemporaneous recommendation, firm market value of equity, scaled earnings, and scaled forecast error. Only 15.7% of the earnings forecasts represent a joint issuance of a recommendation and forecast, all other earnings forecasts are issued in the context of a previously reported recommendation. Approximately 75% of our earnings forecasts follow the currently outstanding recommendation report date by more than 30 days. These results confirm that earnings forecasts are typically issued in the presence of an existing recommendation that has been outstanding for at least one month. The second and third columns of panel D indicate that our sample is comprised of predominately large firms with an average return on equity of 6%. The last column of panel D reports that the mean and median scaled forecast errors are .75 and .21 percent of market value of equity respectively, consistent with the findings of analyst forecast optimism reported in numerous prior studies.

4. Results 4.1

EARNINGS FORECAST ERROR AND THE LEVEL OF EARNINGS

Accumulating evidence shows that forecast errors are not the same across levels of earnings scaled by market value of equity. For example, research has documented different forecast error patterns for positive and negative earnings (Brown [1999], Butler and Saraoglu [1999], Hwang, Jan, and Basu [1996]). Brown [1999] suggests that forecast errors should be investigated separately for positive and negative earnings firms because the distributions of earnings forecast errors differ substantially. Hwang et al. [1996] identify an inverse relationship between analyst earnings forecast errors and reported earnings. Similarly, Eames, Glover, and Stice [2001] report that earnings forecasts exhibit greater optimism as earnings decline below the mean earnings for all firms and greater pessimism as earnings increase

5 We also conducted analyses on samples that deleted forecast errors greater than 20% of market value, and on samples that excluded the largest 5 and 10 percent of scaled forecast errors (ranked using absolute values). The results were virtually identical to the results presented here.


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TABLE 1 Sample Characteristics: First Quarter Forecasts of Annual Earnings, Realized Annual Earnings, Recommendations, and Earnings Forecast Errors, and Market Value of Equity Based on Zacks Investment Research Values for December Fiscal Year End Firms Panel A: Sample Distribution by Month Month Observations

% of Total

January February March

13,391 11,369 10,722

38% 32% 30%

Total

35,482

100%

Panel B: Sample Distribution by Year Year Observations 1988 2,009 1989 1,627 1990 2,713 1991 3,159 1992 4,010 1993 4,433 1994 5,147 1995 5,701 1996 6,683 Total

% of Total 5.7% 4.6% 7.6% 8.9% 11.3% 12.5% 14.5% 16.1% 18.8%

35,482

100.0%

Panel C: Sample Distribution by Recommendation Recommendation Observations

% of Total

Strong Buy (1) Moderate buy (2) Hold (3) Moderate Sell (4) Strong Sell (5) Not Available (6)

11,047 7,886 13,916 960 803 870

31.1% 22.2% 39.2% 2.7% 2.3% 2.5%

Total

35,482

100.0%

Panel D: Days Between Recommendation and Earnings Forecast, Market Value of Equity, Actual Earnings, and Earnings Forecast Error Distribution Statistics Earnings Number of Days Market Value Earnings as a Forecast Errors Recommendation of Equity Percent of as a Percent of Precedes Forecastb ($ millions) Market Value Market Value Mean Median Quartile 1 Quartile 3 Minimum Maximum Standard Deviation

Au: Pls. ref.

109 92 29 178 0 360 91

6,434 1,797 418 5,738 6 119,989 12,214

5.93 6.11 3.80 8.56 −83.97 70.38 7.41

.75 .21 −.73 2.02 −68.12 93.29 5.46

a Sample observations comprise forecasts matched with the contemporaneous or the most recent preceding recommendation by the same analyst, provided the recommendation does not precede the forecast by more than one year. Forecast errors are defined as forecasted earnings less actual earnings. b Zacks adds new recommendation records to the database when an analyst issues a first recommendation or changes a recommendation or when the analyst reiterates a prior recommendation that is at least a year old.


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above the mean earnings of all firms. They conclude this relation is due, in part, to unanticipated earnings shocks. Unexpected positive (negative) earnings shocks result in higher (lower) unanticipated earnings and will generally be associated with more negative (positive) forecast errors. More formally, suppose that reported scaled earnings at time t for firm i, Eit , can be decomposed into a predictable component, EPit , and an unpredictable component or earnings shock, eUit ∼ N(0, σ ). Then Eit = EPit + eUit . We can model the analysts’ scaled forecasts, Fit , of Eit as a function of the predictable component: Fit = EPit + fit , where a non-zero mean value for the error term, fit , is consistent with forecast bias (either intentional or unintentional). We define forecast error as: FEit = Fit − Eit = (EPit + fit ) − (EPit + eUit ), or FEit = fit − eUit . Thus, assuming no forecast bias, unpredictable earnings shocks will result in an inverse relation between forecast error and reported earnings.6 To plot the relationship between scaled forecast error and scaled earnings we divide our sample of 35,482 observations (including the 870 observations with unavailable recommendations) into 20 equally-sized portfolios based on the magnitude of scaled earnings. Figure 1 illustrates portfolio mean and median scaled forecast errors plotted by portfolio median scaled earnings. The inverse relationship depicted in figure 1 is consistent with results in Eames, Glover, and Stice [2001] that, on average, firms experiencing large positive (negative) earnings shocks are more likely to have positive (negative) earnings and negative (positive) forecast errors.7 Regressing scaled analyst forecast errors on scaled actual earnings results in an R-squared of 48 percent and a negative slope significant at the .001 level (table 2, panel B). If earnings are also associated with analyst recommendations, earnings could be an important correlated omitted variable in prior studies examining the relationship between forecast errors and recommendations. Figure 2 plots mean and median scaled earnings by recommendation and 6 Because earnings is included on both the right and left-hand side of the equation, i.e., FEit = Fit − Eit = B0 + B1 Eit + eit , there may be concern that this linear model will force an algebraic inverse association between FEit and Eit . However, an algebraic association between forecast error and earnings does not necessitate an observed inverse association. Fit is a function of Eit ,thus we do not necessarily expect a negative value for B1 , and only obtain a negative value under restrictive assumptions regarding the behavior of Fit with respect to Eit . To see this, we differentiate both sides of the preceding equation with respect to Eit , obtain ∂∂ FE − 1 = B1 , and note that B1 is negative only if ∂∂ FE < 1, and B1 = −1 only if ∂∂ FE = 0. There is no basis for asserting either of these conditions a priori. For our sample ∂∂ FE = .48 and is significantly different from zero at the 1% level. Thus, the inverse association between forecast error and earnings is not algebraic. Rather, a systematic inverse relationship between forecast errors and earnings requires systematic analyst behavior. For example, the inability of analysts to forecast some component or portion of earnings. Because we focus on scaled earnings, we assume that the scaled earnings shock has constant variance across firms. 7 The optimistic forecast errors associated with lower earnings are consistent with the management relations hypothesis given that sell recommendations are commonly associated with low earnings. Although the management relations hypothesis could be contributing to the observed relation between forecast error and earnings, it cannot explain the pessimism observed at higher earnings observations because the management relations hypothesis predicts only optimism.


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FIG. 1.—Mean and median scaled earnings forecast error by mean scaled earnings for observations of first quarter forecasts of annual earnings with a contemporaneous or recently preceding recommendation by the same analyst. Forecasts of annual earnings and actual annual earnings values are from Zacks Investment Research. Forecasts are reported in first fiscal quarter for December year-end firms. With 35,482 observations 20 portfolios are formed based on earnings rank. The highest earnings portfolio has 1,776 observations and all others have 1,774 observations. We scale earnings and forecast values by beginning of the year market value of equity and measure forecast error as forecast earnings less actual earnings. Similar results are obtained if beginning of the year book value and ending market value of equity are used as the scaler.

illustrates the relationship between scaled earnings and recommendations. The depicted relationship is a monotonic increase in mean scaled earnings from sell to buy recommendations.8 Panel A of table 2 reports the correlation matrix for scaled earnings, scaled forecasts, scaled forecast errors, and recommendations. The observed negative significant correlation between scaled forecast error and scaled earnings and the negative significant correlation between scaled earnings and recommendation (where 1 = buy and 5 = sell) are consistent with figures 1 and 2. Collectively, the figures and significant correlations suggest that earnings is a potential explanatory variable in the relation between forecast errors and recommendations. A further reason to consider earnings as an explanatory variable in analyzing the relationship between forecast errors and recommendations stems from the potential for analyst and managerial behaviors to differ for profit and loss firms. Analyst behavior may differ as a result of such factors as the selective non-reporting of results for anticipated loss firms (McNichols and O’Brien [1997]), while managerial differences may be associated with such 8 All paired comparisons of mean scaled earnings by recommendation category, with the exception of the comparison of strong and moderate buy recommendations, are significant at the .01 level. While the sample used to plot figure 2 is essentially the same as that used to plot figure 1, there are no negative observations of scaled earnings in figure 2 because the mean and median scaled earnings are positive for all categories of recommendations.


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FIG. 2.—Mean and median scaled earnings by stock recommendation for observations of first quarter forecasts of annual earnings with a contemporaneous or recently preceding recommendation by the same analyst. Actual annual earnings values and recommendations are from Zacks Investment Research. We limit observations to forecasts reported in first fiscal quarter for December year-end firms and scale earnings values by beginning of the year market value of equity. We match each forecast with the most recent preceding or contemporaneous recommendation by the same analyst, provided the recommendation does not precede the forecast by more than one year. Recommendations are available for 34,612 observations. Forecast errors are defined as forecasted earnings less actual earnings. Mean and median earnings levels are plotted by recommendation with the following samples: Strong Buy = 11,047, Moderate Buy = 7,886, Hold = 13,916, Moderate Sell = 960, Strong Sell = 803.

factors as the propensity of loss firms to substantially lower earnings with a big bath (Brown [1999]).

4.2

EARNINGS FORECAST ERROR AND RECOMMENDATIONS

The positive correlation between scaled forecast errors and recommendations reported in panel A of table 2 is consistent with the results reported in Francis and Philbrick [1993] and Kim and Lustgarten [1998]. However, the analysis reported in panel B of table 2 highlights the importance of including earnings as an explanatory variable. Table 2, panel B, reports simple and multiple regression analyses of recommendations and scaled earnings on scaled forecast errors. We find that the sign of the coefficient on recommendation reverses from a significant positive value (t = 6.32, p < .001) in the simple regression to a significant negative value (t = −4.70, p < .001) when scaled earnings is added as an independent variable in the multiple regression.9 The coefficient estimates for scaled earnings in the 9 Before pooling the data across years we obtained regression estimates by year. Our results were substantially similar to those presented above. For example, in equation 3 the coefficient on earnings was negative and significant in all years, and the coefficient on recommendation was negative and significant in six of the nine years, and not significant in the remaining 3 years.


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TABLE 2 Correlations, Regression Analysis and Distribution Statistics for Recommendations and Market Value of Equity Scaled Measures of Forecasts, Earnings, and Forecast Errors Based on Zacks Investment Research Values for December Fiscal Year End Firms a Panel A: Correlation Matrix (Pearson (Spearman) Correlation Coefficients Below (Above) Diagonal, n = 34,612) Scaled Forecast Error Recommendation Scaled Earnings Forecast Scaled Forecast Error Recommendation Scaled Earnings Forecast

.034∗∗∗ −.696∗∗∗ .056∗∗∗

.025∗∗∗

−.584∗∗∗ −.066∗∗∗

−.075∗∗∗ −.069∗∗∗

.663∗∗∗

Panel B: Regression Analyses (n = 34,612) Forecast Error = β 0 + β 1 Recommendation + e Forecast Error = β0 + β2 Earnings + e Forecast Error = β0 + β1 Recommendation +β2 Earnings +e β0 Equation 1 Equation 2 Equation 3

.0034 .0378 .0400

β1 .002 −.001

t-value

(1) (2) (3)

β2

t-value

R2

−.511 −.512

−180.33 −180.23

.001 .484 .485

6.32 −4.70

.047∗∗∗ −.072∗∗∗ .673∗∗∗ .

Panel C: Market Value of Equity Scaled Forecast Error Statistics ((Forecast Earnings-Actual Earnings)/(Beginning of Fiscal Period Market Value of Equity)) Unadjusted Mean Earnings Median Earnings Central Region Data Scaled Adjusted Scaled Adjusted Scaled Scaled Forecast Forecast Error Forecast Errorb Forecast Errorb Errorc Mean Median Quartile 1 Quartile 3 Minimum Maximum Standard Deviation

.75 .21 −.73 2.02 −68.12 93.29 5.46

.00 −.28 −1.45 1.21 −74.71 82.58 4.19

.36 .00 −1.05 1.46 −72.92 84.37 4.21

.24 .06 −.51 .96 −18.75 19.37 2.08

a Sample observations comprise forecasts matched with the contemporaneous or the most recent preceding recommendation by the same analyst, provided the recommendation does not precede the forecast by more than one year. Forecast errors are defined as forecasted earnings less actual earnings. Recommendation data are coded 1 = strong buy, 2 = moderate buy, 3 = hold, 4 = moderate sell, 5 = strong sell, and 6 = not available. b Mean (median) earnings adjusted forecast errors are obtained by identifying 20 equal sized portfolios of observations based on ranks of market value of equity scaled earnings, measuring forecast error as the forecast less actual earnings, again scaled by beginning-of-the-year market value of equity, and then subtracting the relevant portfolio mean (median) forecast error from the observed forecast error. c The central region includes all observations with earnings scaled by beginning-of-the-year market value of equity ranging from 5% to 10%. ∗∗∗ Significant at the .001 level.

simple and multiple regressions are nearly identical and not significantly different. Equation 3 in panel B of table 2 assumes linearity. However, inspection of figure 1 suggests the relation between scaled earnings level and forecast error is non-linear. To control for the relation between scaled earnings and forecast errors without assuming linearity, and to limit the impact of


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outliers, we obtain earnings-portfolio adjusted scaled forecast errors using the same 20 equal-sized portfolios formed to plot figure 1.10 We obtain mean (median) adjusted scaled forecast errors by subtracting the relevant portfolio mean (median) scaled forecast error from observed scaled forecast errors. The resulting adjusted scaled forecast errors are similar to residuals from a regression of scaled forecast error on actual scaled earnings, however the portfolio adjustment does not assume linearity and is not significantly impacted by outliers.11 A third method of “controlling” for the relationship between earnings and forecast errors is to restrict our analysis to the 15,701 observations in a “central region” of scaled earnings where visual inspection of figure 1 suggests the relation between scaled earnings and scaled forecast error is relatively weak. We define this central region to be all observations with earnings of 5 to 10 percent of beginning-of-year market value of equity.12 We select this 5 to 10 percent region for analysis because: (1) there is a high density of forecasts in this area (15,701 of the 34,612 observations), (2) dispersion in analysts forecast errors is relatively low for the region, (3) this region includes the mean and median scaled earnings (6% of market value of equity) and excludes extreme values of earnings, and (4) earnings level is only moderately related to forecast error in this region.13 Regressing scaled forecast error on scaled earning in the central region results in an R-squared of only 6%, although the relation is still negative and significant ( p < .001). Table 2, panel C presents distribution statistics for the mean and median earnings-portfolio-adjusted and central region forecast errors. For ease of comparison, this panel also repeats the values for unadjusted scaled forecast

10 Another advantage of the portfolio adjustment technique over regression is that it lends itself to the analysis of absolute forecast optimism and pessimism across recommendation categories. Analysis via regression coefficients focuses only on the trend in forecast error across recommendations. 11 Our results are robust to alternative methods of controlling for earnings. All reported results examining the portfolio adjusted forecast errors are similar to results obtained examining residuals from a regression of forecast error on actual earnings. Because the plot illustrated in figure 1 suggests the relationship between forecast error and earnings is nonlinear, we also used a three-part piecewise regression with breaks in the earnings value at 5 and 10 percent of market value. The results of analyzing residuals from the piecewise regressions are also similar to analysis of portfolio earnings level adjusted forecast errors. Because we believe that our earnings-portfolio adjusted forecast errors are superior to regression residuals, we limit our discussion to analysis of the earnings-portfolio adjusted forecast errors. 12 We also analyzed various central regions with ranges of actual earnings of from as small as 7 to 8 percent to as large as 1 to 13 percent. Results for all these ranges displayed qualitatively similar relationships between forecast error and recommendation to the relationship reported for the 5 to 10 percent range. 13 As an alternative measure of how extreme large (small) observations in figure 1 are, data points can be converted to a ratio approximating PE ratios (recall that we scale by beginning market value). In other words, firms reporting realized earnings that are 20% of beginning market value have a PE ratio of 5.


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errors that are reported in table 1. Both mean and median adjusted scaled forecast errors and central region scaled forecast errors are significantly less optimistic than unadjusted scaled forecast errors. Note that while we replicate previous studies (e.g., Francis and Philbrick [1993]) by analyzing unadjusted scaled forecasts errors, our primary focus is on adjusted scaled forecast errors and observations in the central region because we argue these two latter data sets appropriately consider the association between forecast error and earnings. Table 3, panel A presents mean and median unadjusted scaled forecast errors (i.e., no control for the relation between actual earnings and forecast errors) by analyst recommendation. The mean scaled forecast error for each recommendation is significantly greater than zero, and the means are increasingly positive as the recommendation moves from buy to sell. The mean scaled forecast error is significantly more optimistic for strong sell than for strong buy (t = 4.14, p = .000), for hold than for moderate buy (t = 3.86, p = .000), and for strong sell than for moderate sell recommendations (t = 1.99, p = .047).14 Thus, when we omit earnings from the analysis, we are able to replicate prior findings of increasing analyst forecast optimism as recommendations become less favorable (e.g., Francis and Philbrick [1993], Kim and Lustgarten [1998]). Median scaled forecast errors, reported in panel A, generally follow a similar pattern. However, the medians do not follow a monotonic increase moving from strong buy to strong sell recommendation. While the unadjusted data reject H1 and support H1a, the management relations hypothesis, these data do not appropriately consider the association between earnings and both forecast errors and recommendations. Panel B presents a similar analysis using mean and median adjusted scaled forecast errors. The analysis of adjusted scaled forecast errors (i.e., controlling for the relationship between actual earnings and forecast error) yields dramatically different results from those reported in panel A. The mean adjusted scaled forecast error for the strong buy recommendation is positive (t = 1.65, p = .098) while the mean adjusted scaled forecast errors for both moderate and strong sell recommendations are negative (t = −1.91, p = .056; t = −2.49; p = .013, respectively). Mean-adjusted scaled forecast errors shift consistently and monotonically from optimism to pessimism as recommendations move from strong buy to strong sell. The mean-adjusted scaled forecast error is significantly more optimistic for strong buy than for strong sell (t = 2.75, p = .006), for moderate buy than for moderate sell (t = 1.98, p = .048), and for hold than for moderate sell recommendations (t = 1.77, p = .077). Analysis of median-adjusted scaled forecast errors yields a similar pattern of forecast optimism in association 14 All p values relating to t statistics in the text represent the probability of a larger absolute t value. The t values for comparisons are approximate t values under the assumption of unequal variances. The p values represent the probability via the Cochran and Cox [1950] approximation for the approximate t test.


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99

TABLE 3 Market Value of Equity Scaled Annual Earnings Forecast Errors by Analyst Recommendation for the Sample of Forecast Errors with Contemporaneous or Recently Preceding Recommendation by the Same Analyst a Analyst Recommendation

Sample Size

Mean Scaled Forecast Error in % (t-value)b

Median Scaled Forecast Error in %b

Panel A: Scaled Earnings Forecast Errors using Unadjusted Data Strong Buy (SB) 11,047 .587 (12.52)∗∗∗ Moderate Buy (MB) 7,886 .599 (10.79)∗∗∗ Hold (H) 13,916 .886 (18.03)∗∗∗ Moderate Sell (MS) 960 .982 (4.24)∗∗∗ Strong Sell (SS) 803 1.668 (6.49)∗∗∗ Panel B: Adjusted Scaled Forecast Errorsd Strong Buy 11,047 Moderate Buy Hold

7,886 13,916

Moderate Sell

960

Strong Sell

803

.061 (1.65)∗ .022 (0.49) −.018 (−.49) −.325 (−1.91)∗ −.484 (−2.49)∗∗

Panel C: Central Region Scaled Forecast Errorse Strong Buy 5,195 .349 (12.44)∗∗∗ Moderate Buy 3,650 .231 (6.89)∗∗∗ Hold 6,261 .185 (6.93)∗∗∗ Moderate Sell 336 .105 (0.92) Strong Sell 259 −.296 (−1.94)∗∗

.191∗∗∗

Mean Comparisons (t-value)c

.243∗∗∗

SB v. MB (.17) SB v. SS (4.14)∗∗∗ MB v. H (3.86)∗∗∗ MB v. MS (1.61) H v. MS (.41)

.403∗∗∗

MS v. SS (1.99)∗∗

.172∗∗∗

.340∗∗∗

.040∗∗∗

−.015

SB v. MB (.66) SB v. SS (2.75)∗∗∗ MB v. H (.69) MB v. MS (1.97)∗∗ H v. MS (1.77)∗

−.167∗∗

MS v. SS (.62)

.005

−.314∗∗∗

.115∗∗∗

.042∗∗∗

SB v. MB (2.69)∗∗∗ SB v. SS (4.15)∗∗∗ MB v. H (1.09) MB v. MS (1.06) H v. MS (.68)

.015

MS v. SS (2.10)∗∗

.043∗∗∗

−.277∗∗∗

a Forecasts, realized earnings, and recommendations are from the Zacks Investment Research database. The sample is based on first quarter forecasts of annual earnings for December fiscal year end firms. We match each forecast with the most recent preceding or contemporaneous recommendation by the same analyst, provided the recommendation does not precede the forecast by more than one year (n = 34,612). Forecast error is defined as forecasted earnings less actual earnings. Both forecasts and actual earnings are scaled by beginning of the year market value of equity. b Mean and median tests from zero are based on a parametric t-test and a nonparametric sign test, respectively. c SB = strong buy, MB = moderate buy, H = hold, MS = moderate sell, SS = strong sell. d Forecast errors are adjusted to control for the observed association between earnings and forecast errors (see figure 1). Mean (median) earnings adjusted forecast errors are obtained by identifying 20 equal sized portfolios of observations based on ranks of earnings scaled by market value of equity, measuring forecast error as the forecast less actual earnings, again scaled by beginning-of-the-year market value of equity, and then subtracting the relevant portfolio mean (median) forecast error from the observed forecast error. e The central region includes all observations with earnings scaled by beginning-of-the-year market value ranging from 5% to 10% (see section 4.1). ∗ ∗∗ ∗∗∗ ( / ) Designate two-tailed statistical significance at the 10% (5%/1%) level.


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with buy recommendations and pessimism for sell recommendations. Consequently, when we analyze scaled forecast errors, adjusted to control forecast error to control for the relationship between earnings level and forecast error, we again reject hypothesis H1, but now the results support the objectivity illusion and trade boosting hypotheses (H1b) and not the management relations hypotheses (H1a). These results also provide evidence that the relation between recommendation and forecast error is not driven solely by the relation between scaled earnings and forecast error. If that were the case, we would expect no relation between recommendations and adjusted forecast errors. Results for scaled forecast errors in the central region (i.e., where the relation between scaled earnings and scaled forecast error is weak) are presented in panel C, and are consistent with results presented in panel B for mean and median adjusted scaled forecast errors. Again we observe optimistic mean scaled forecast error for strong buy recommendations and pessimistic mean scaled forecast error for strong sell recommendations, with scaled forecast errors for intermediate recommendations monotonically aligning between the extremes. The mean scaled forecast error is significantly more optimistic for strong buy than for strong sell (t = 4.15, p = .000), for strong buy than for moderate buy (t = 2.69, p = .007), and for moderate sell than for strong sell recommendations (t = 2.11, p = .036). Median scaled forecast errors decrease in magnitude going from strong buy to strong sell recommendations, and exhibit optimism for buy recommendations and pessimism for strong sell recommendations. Results in panel C provide a control for the association between earnings and forecast errors by examining a subset of the unadjusted data reported in panel A.15 These results support the trade boosting and objectivity illusion hypotheses (H1b), but not the management relations hypotheses (H1a). A comparison of panels A and C also highlights the key role extreme earnings observations likely play in prior research. Panels B and C suggest that evidence in previous studies supporting the management relations hypothesis (panel A) is driven by the relation between analyst forecast error and scaled earnings levels outside the central region.

15 Additional evidence that we have not “adjusted away” management relations is obtained by investigating the relation between forecast errors and forecasts. If recommendations induce biased forecast errors and if recommendations are associated with the level of forecast (i.e., buy (sell) recommendations are generally associated with higher (lower) forecasted earnings), we expect to observe a significant relation between forecast error and forecast. For trade boosting and the objectivity illusion (management relations) we expect a positive (negative) association between forecast error and forecast. As expected, the relation between forecast error and forecasts is positive and highly significant (R-squared = 67%). Just as earnings are significantly correlated with recommendations (see figure 2), we find a similar correlation ( p < .001, see also panel A, table 2) between forecasted earnings and recommendations. The observed positive relationship is consistent with the pattern predicted by objectivity illusion and trade boosting but not management relations and provides additional evidence that analysts’ earnings forecasts are systematically biased. We thank the reviewer for suggesting this analysis.


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FIG. 3.—Mean scaled earnings forecast error by recommendation for observations of first quarter forecasts of annual earnings with a contemporaneous or recently preceding recommendation by the same analyst. We obtain individual analyst recommendations, actual earnings, and forecasted earnings from the Zacks Investment Research database. All forecast error observations are scaled by beginning-of-the-year market value of equity. “Unadjusted” forecast errors are defined as forecasted earnings minus actual earnings. The unadjusted and earnings-adjusted plots are based on 34,612 observations. The “Earnings Adjusted” data are obtained by identifying 20 equal-sized portfolios of observations based on ranks of market-value scaled earnings and then subtracting the relevant portfolio mean forecast error from the observed forecast error. The “Central Region” plot is based on 15,701 unadjusted-forecast-error observations where earnings range from 5 to 10 percent of market value of equity. We obtain individual analyst recommendations, actual earnings, and forecasted earnings from the Zacks Investment Research database. All forecast error observations are scaled by beginning-of-the-year market value of equity. “Unadjusted” forecast errors are defined as forecasted earnings minus actual earnings. The unadjusted and earnings-adjusted plots are based on 34,612 observations. The “Earnings Adjusted” data are obtained by identifying 20 equal-sized portfolios of observations based on ranks of marketvalue scaled earnings and then subtracting the relevant portfolio mean forecast error from the observed forecast error. The “Central Region” plot is based on 15,701 unadjustedforecast-error observations where earnings range from 5 to 10 percent of market value of equity.

The bar chart in figure 3 summarizes results presented in table 3 and illustrates the relation between forecast error and stock recommendation for the three data sets we analyze (i.e., unadjusted data, earnings adjusted data and central earnings region data). Figure 3 illustrates that when we examine the data in a manner consistent with prior studies (i.e., using the entire range of earnings with no control) we find results consistent with the management relations hypothesis. When we control for the relation between


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earnings and forecast errors by examining the “adjusted” and “central region” data we find strikingly different results; the relation between forecast errors and recommendations no longer supports the management relations hypothesis.16

4.3

OBJECTIVITY ILLUSION AND TRADE BOOSTING

Both the objectivity illusion and trade boosting hypotheses predict optimistic forecast errors for buys and pessimistic forecast errors for sells. While both factors could certainly exist simultaneously, we examine whether our results are more supportive of the trade boosting or objectivity illusion hypothesis. Analysts’ incentives to intentionally bias earnings forecasts to boost trade are likely greater for strong recommendations than for moderate recommendations, since analysts can boost trading on stocks that do not have an extreme recommendation with a shift to a more extreme recommendation, as well as by intentionally biasing the earnings forecast. Analysts likely prefer recommendation-related trade boosting over earnings-forecast-based trade boosting because recommendation shifts are a stronger signal to investors than an unspecified level of forecast error at the time of forecast issuance and because forecast errors are more readily measurable than are recommendation errors. Therefore, for the trade-boosting hypothesis we expect no significant relation between forecast error and recommendations for intermediate recommendation levels, significant optimistic forecast bias for strong buy recommendations, and significant pessimistic forecast bias for strong sell recommendations. The objectivity illusion predicts forecast errors associated with both moderate and strong recommendations and the degree of bias should increase as recommendations become stronger. Thus, greater absolute forecast error for strong buy and strong sell recommendations and a significant relation between forecast bias and recommendation for intermediate recommendations would lend support for the objectivity illusion. Panel B of table 3 reports that mean and median adjusted scaled forecast errors are significantly optimistic for strong buy recommendations, significantly pessimistic for strong sell recommendations, and exhibit a continuum in forecast bias across all levels of recommendations. Consistent with the objectivity illusion hypothesis and not with trade boosting, mean adjusted forecast errors differ significantly between moderate buy and moderate sell recommendations (t = 1.97, p = .048) and between moderate sell and hold recommendations (t = 1.77, p = .077).

16 To preclude the possibility that a correlated error structure in our data might be driving the significant results we obtain in panels B and C of table 3, we also examined our data on a year-by-year basis. Although significance was generally reduced due to the smaller samples employed, the year-by-year results were highly consistent with results obtained for the entire sample.


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5. Summary and Conclusion We examine the relation between analysts’ earnings forecast errors and stock recommendations. Prior research has generally found both broker and non-broker-analyst earnings forecasts to be optimistic on average, and increasingly optimistic as the stock recommendation becomes less favorable. Prior research posits that analysts intentionally issue optimistic forecasts in order to curry management favor (the management relations hypothesis). However, this explanation contradicts conventional wisdom reported in the financial press as well as recent academic research suggesting managers may actually prefer slightly pessimistic forecasts. We believe that prior results supporting the management relations hypothesis are driven by omitting an important correlated variable, earnings. When we control for the relation between forecast error and earnings, we find that analyst forecast errors are optimistic for buy recommendations and pessimistic for sell recommendations. These results are not consistent with the management relations hypothesis but are consistent with both the trade boosting and objectivity illusion hypotheses. The trade boosting hypothesis contends that analysts intentionally bias their forecasts to boost trade. The objectivity illusion hypothesis contends that analysts unintentionally bias their forecasts to achieve consistency with their stock recommendations. Results of an analysis to distinguish between trade boosting and objectivity illusion appear more consistent with the objectivity illusion. REFERENCES AFFLECK-GRAVES, J.; L. DAVIS; AND R. R. MENDENHALL. “Forecasts of Earnings Per Share: Possible Sources of Analyst Superiority and Bias.” Contemporary Accounting Research (Spring 1990): 501–17. ALI, A.; A. KLEIN; AND J. ROSENFELD. “Analysts’ Use of Information about Permanent and Transitory Earnings Components in Forecasting Annual EPS.” The Accounting Review 67 (1992): 183–98. BALOG, S. “What an Analyst Wants from You.” Financial Executive ( July/August 1991): 47–52. BIGGS, S. “Financial Analysts’ Information Search in the Assessment of Corporate Earnings Power.” Accounting, Organizations and Society 9 (1984): 313–23. BOINEY, L.; J. KENNEDY; AND P. NYE. “Instrumental Bias in Motivated Reasoning: More When More is Needed.” Organizational Behavior and Human Decision Processes 72 (October 1997): 1–24. BROWN, L. “Earnings Forecasting Research: Its Implications for Capital Markets Research.” International Journal of Forecasting 9 (1993): 295–320. BROWN, L. “Managerial Behavior and the Bias in Analysts’ Earnings Forecasts.” Working paper, Georgia State University, 1999. BROWN, L.; P. GRIFFIN; R. HAGERMAN; AND M. ZMIJEWSKI. “An Evaluation of Alternative Proxies for Market’s Assessment of Unexpected Earnings.” Journal of Accounting and Economics 11 (1987): 159–93. BURGSTAHLER, D., AND M. EAMES. “Management of Earnings and Analyst Forecasts.” Working paper, University of Washington, 2000. BUTLER, K. C., AND H. SARAOGLU. “Improving Analysts’ Negative Earnings Forecasts.” Financial Analysts’ Journal (May/June 1999): 48–56. COCHRAN, W., AND G. COX. Experimental Designs. New York. John Wiley and Sons, Inc.


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