Firms Relative Operational Efficiency and Analysts Earnings Forecasts

Transcription

1 Firms Relative Operational Efficiency and Analysts Earnings Forecasts Donal Byard Stan Ross Department of Accounting Zicklin School of Business Baruch College -- City University of New York One Bernard Baruch Way, Box B New York, NY (646) Donal_Byard@baruch.cuny.edu Fatma Cebenoyan* Department of Economics Hunter College -- City University of New York 695 Park Avenue New York, NY (212) Fatma.Cebenoyan@hunter.cuny.edu December 2002 * Corresponding author. We thank Sinan Cebenoyan, Neal Galpin, Hongtao Guo, Ying Li, Devra Golbe, Kevin Sachs, Ping Zhou, and two anonymous referees for their comments. We also gratefully acknowledge the contribution of IBES International Inc. for providing earnings per share forecast data, available through the Institutional Brokers Estimate System. These data have been provided as part of a broad academic program to encourage earnings expectation research.

2 Firms Relative Operational Efficiency and Analysts Earnings Forecasts Abstract Relatively more efficient firms tend to maintain more stable levels of output and operating performance compared to their industry peers (Mills and Schumann 1985). Such firms also tend to have more sustainable performance (Berger et al. 1993). Given their more stable and sustainable levels of operating performance, we test the related prediction that financial analysts find the earnings of relatively more efficient firms to be more predictable. Using a stochastic frontier approach to measure firms relative operational efficiency, we find that analysts face less earnings uncertainty for relatively more efficient firms, compared to other firms in the same industry. We find that this broader more encompassing measure of firms relative operational efficiency yields stronger results than comparable accounting ratios (ROA and ROE). These results indicate that analysts behave as if they factor into their forecasts an understanding of the underlying economics of a business of an industry. Furthermore, the users of these forecasts can also benefit from this information in terms of assessing the level of reliability of forecasts. Keywords: Relative Operational Efficiency, Return on Assets, Return on Equity, Analysts Earnings Forecasts, Earnings Uncertainty. Data Availability: All data are available from public sources.

3 Biographies Donal Byard is an Assistant Professor of Accounting at the Zicklin School of Business of Baruch College, City University of New York. His research focuses on the role of financial analysts as information intermediaries in financial markets. Professor Byard s research focuses on financial analysts forecasts for high-tech firms with relatively large amounts of intangible assets, financial analysts reactions to earnings announcements, and analysts role as processors of public disclosures. Fatma Cebenoyan is an Assistant Professor of Accounting in the Department of Economics of Hunter College, City University of New York. Her research interests include the information content of reported accounting earnings and its components. Professor Cebenoyan is also investigating the determinants of takeover probability, and the effects of deregulatory and technical changes in the banking industry.

4 Firms Relative Operational Efficiency and Analysts Earnings Forecasts 1. Introduction Relatively more efficient firms tend to have more stable levels of output compared to other firms within the same industry (Mills and Schumann 1985). These firms also tend to have more sustainable levels of operating performance, as indicated by the lower firm-specific timeseries standard deviations of their profitability ratios (Berger et al. 1993; Berger and Mester 1997). Given their more stable and sustainable levels of performance, we expect that more efficient firms will also have relatively more predictable levels of performance compared to their industry peers. We test this prediction using analysts forecasts of annual earnings. In this study, we examine the relationship between analysts level of earnings uncertainty, defined as the average squared error in the analysts forecasts for a firm, and a proxy for firms relative operational efficiency, calculated using a stochastic frontier estimation technique. Due to their intuitive interpretation as measures of efficiency, and their ease of measurement, we also conduct our analysis using two accounting ratios, Return on Assets (ROA) and Return on Equity (ROE), as alternative proxies for firms operational efficiency. We compare the performance of our measure of firms relative operational efficiency to that of these two accounting ratios. Because the measure of relative efficiency is based on a broader set of data (including separate data on sales output, cost of goods sold, general and administration expenses, and physical capital cost) regarding a firm s inputs and outputs than either ROA or ROE, and involves a more sophisticated estimation controlling for randomness in performance, we expect it to proxy for a broader and more sophisticated level of knowledge regarding a firm s relative efficiency within its industry. The more efficient firms within an industry have, perhaps through better management, achieved a competitive advantage vis-à-vis their rivals. This competitive advantage results in a more sustainable level of profitability, and more stable earnings for these firms. A sophisticated level of knowledge regarding operational efficiencies within an industry is, thus, useful when 1

5 predicting earnings for firms within that industry. Analysts who possess such knowledge will be able to identify firms with relatively more stable (and predictable) earnings within the industry segment they follow. We adopt the Barron et al. (1998) measure of individual analysts earnings uncertainty as a proxy for the average level of uncertainty individual analysts face when forecasting earnings for a firm. This variable is essentially the average squared error in the individual forecasts of analysts following a firm. We model a firm s relative operational efficiency using a stochastic frontier methodology, which is used to develop a measure of firm-specific relative operational efficiency. This relative operational efficiency measure is the independent variable in our study. We perform rank regressions of our measure of the quality of individual analysts information on this explanatory variable, with control variables for firm size and industry effects. For comparison, we also perform the same analysis using two accounting ratios, ROA and ROE, as alternative proxies for firms efficiency level. We then compare the performance of these two ratios to that of the broader and more encompassing relative efficiency measure. Consistent with our expectations, results indicate that, controlling for a firm s industry and size (market capitalization), the average level of uncertainty regarding earnings is lower for firms that are relatively more efficient that their competitors.. This association is significantly stronger for the measure of relative operational efficiency than for the two comparable accounting ratios, ROA and ROE. These results indicate that analysts behave as if they factor into their forecasts an understanding of the underlying economics of specific industries. Furthermore, this useful within-industry knowledge regarding operational efficiencies that analysts seem to factor into their forecasts appears to be broader than just the information reflected in accounting ratios. These results, thus, provide indirect empirical evidence supporting one potential reason why individual analysts specialize in only following firms from a very limited number of industries. Specifically, by specializing by industry, analysts develop indepth knowledge regarding within-industry operational efficiencies, which are, in turn, useful in forecasting earnings more accurately for the firms within that industry. These results may also 2

6 be of benefit to the users of forecasts. Investors who rely on analysts earnings forecasts could use an analysis of operational efficiency to identify the firms within particular industries that are likely to have more reliable earnings forecasts, resulting from the reduced uncertainty in analysts forecasting process for these firms. The paper is organized as follows. Section 2 discusses the background, our measures of firms operational efficiency and analysts level of uncertainty, and our expectations. Section 3 outlines our sample selection procedures, and the variables and empirical models used in the study. This is followed by section 4, which describes our results, including tests of robustness. The paper concludes with a discussion in section Background 2.1 Theoretical Background Relative operational efficiency is viewed in both the Industrial Organization and Strategic Management literatures as the product of firm-specific factors such as: management skill, innovation, cost control, and market share, which determine current firm performance, and critically, the sustainability of this level of performance (McWilliams and Smart 1993). In essence, relative operational efficiency characterizes a firms competitive strength relative to its competitors. Prior studies indicate that more efficient firms have more stable performance. For example, several studies within the banking industry report a negative correlation between banks level of inefficiency and the stability of their level of profitability over time (Berger et al. 1993; Berger and Mester 1997). Berger et al. (1993) and Kwan and Eisenbeis (1996) both argue that this relationship is consistent with the idea that more efficient firms avoid riskier projects in order to avoid the possibility of jeopardizing their relatively more profitable position within their industry. Similarly, Mills and Schumann (1985) conclude that more efficient firms have more stable output levels compared to less efficient firms. In their examination of the effects of demand fluctuations on firms, they argue that firms become relatively more efficient through cost-minimizing strategies. This cost-minimization approach results in more long-term stability 3

7 in the level of output for relatively more efficient firms, but at the cost of a reduction in their flexibility to respond to short-term changes in demand. In a recent study, McGahan and Porter (1999) find evidence that highly profitable firms (profitable due to their efficient operations) have more persistent performance than less profitable firms within the same industries. Collectively, these studies suggest that relatively more efficient firms have more stable earnings and maintain their performance through higher risk avoidance, and/or through the organization of their production processes to yield more stable levels of output. We estimate firm efficiency using a stochastic frontier methodology, which gives a relative industry-based performance measure. The frontier concept builds on the original work of Farrell (1957), who first suggested the use of industry best practice as the benchmark to evaluate firm performance. After its operationalization by Aigner et al. (1977), the concept of relative efficiency estimated using a frontier approach has become a frequently employed firm performance measure in both the Finance and Economics literatures (Allen and Rai 1996; Merger and Mester 1997; Rogers 1998; Wheelock and Wilson 2000, among others). The widespread use of the frontier approach to measure firms performance results from these measures conceptual appeal, as they capture firms relative performance within their industries. In effect, this type of analysis yields a comprehensive benchmarking of a firm s performance relative to its competitors. 1 In its estimation this methodology also removes non-controllable random factors such as luck, climate, and machine performances from the observed level of corporate performance, providing a direct firm-specific measure of the systematic factor(s) affecting a firm s performance, such as managerial effectiveness (Hughes et al. 2002; see Appendix A for technical details). Since analysts care about forecast accuracy (Mikhail et al. 1999), they have an incentive to use any knowledge they may have regarding the relative efficiency of firms within the 1 In fact, both individual firms and industry consultants have used this within-industry relative performance evaluation approach to quantify objective performance rankings within industries (Berger and Humphrey 1997). 4

8 industry sector they follow to generate more accurate forecasts for these firms. 2 As a result, because more efficient firms have more stable earnings, we expect analysts with sophisticated knowledge regarding the firms in the industry they follow to identify relatively more efficient firms and generate more accurate earnings forecasts for these firms. In other words, we expect there to be a negative association between analysts level of earnings uncertainty and a measure of relative operational efficiency. As an additional analysis, we also compare the performance of our measure of relative operations efficiency to that of two simple accounting ratios (i.e., ROE and ROA), which may be viewed as two alternative measures of firm efficiency. Efficiency is a performance measure, and return on investment (ROI) measures such as return-on-assets (ROA) or return-on-equity (ROE) are frequently employed as the best-available criteria to evaluate business performance (Jacobson, 1987). Our measure of relative efficiency, however, captures more information regarding a firm s relative performance than these two accounting ratios. This results from the fact that unlike the ratios ROA and ROE, our measure of relative operational efficiency is based on a separate analysis of each industry, it considers multiple decision variables (multiple inputs), and it adjusts for the effects of random shocks. As a result, our measure of relative operational efficiency is a conceptually more thorough performance measure than either ROE or ROA. We thus expect that if more efficient firms have more predictable earnings, then there will be a stronger association between analysts earnings uncertainty and relative operational efficiency, than between analysts earnings uncertainty and either ROA or ROE. On the other hand, if ROA and/or ROE work equally as well as measures of firms operational efficiency, then we would expect the empirical association between these ratios and analysts earnings uncertainty to be as strong as the association between firms relative operational efficiency and analysts earnings uncertainty. 2 Annual rankings of analysts use forecast accuracy as one of the criteria for choosing the best analysts (see Institutional Investor 2000; and Wall Street Journal 2001). In fact, the Wall Street Journal s annual survey ranking of the best analysts is based only on analysts earnings forecast accuracy. 5

9 More broadly, our study investigates whether analysts can use extensive within-industry knowledge regarding production processes and relative operational efficiencies to forecast earnings more accurately for firms within the industry in which they specialize. Our study, thus, provides some empirical evidence regarding one potential reason why individual analysts tend to specialize in only following firms from a very limited number of industries. 3 Consistent with the observed industry concentration of individual analysts, surveys provide evidence of analysts possessing some from of industry knowledge which they use as part of their forecasting activities. 4 Our measure of relative operational efficiency, because of its industry-based nature, is thus likely to capture knowledge similar to that used by analysts when forecasting earnings for the firms within the industry sector they specialize in following. In addition, users of forecasts can benefit from an understanding of the relationship between efficiency and analysts earnings uncertainty in terms of improving their assessment of the reliability of forecasts. We now outline in more detail our measure of firms relative operations efficiency, which we use as an experimental variable in our analysis. We then outline our measure of the quality of individual analysts information, which we use as the dependent variable in our analysts. 2.2 Operational Efficiency We model firms relative operational efficiency using a stochastic frontier methodology. Standard microeconomic production theory specifies the input-output relationship in terms of a firm s production function (as well as their value-equivalent cost, profit and revenue functions). This function describes an optimal relationship that achieves a maximum or a minimum, under constraints imposed by technology and prices. In other words, it describes a frontier tracing the 3 For example, selecting only forecasts of annual earnings for 1997 from the Institutional Brokerage Estimation System s (IBES) Detail file, we find that for 1997 IBES tracked 4,753 analysts forecasting for 6,171 firms. The median (mean) number of firms each analyst followed was 10 (12.78). Although these 6,171 different firms were spread over 70 different two-digit SIC codes, the median number of two-digit SIC codes representing the firms each individual analyst followed was just 2. Furthermore, over 75-percent of analysts only followed firms from 4 or less two-digit SIC codes. 4 For example, in surveys of institutional investors, industry knowledge is consistently ranked as the most important attribute they ascribe to analysts each year since Institutional Investor magazine began these surveys (Institutional Investor 2000, p. 62). Each year in these surveys investors have ranked an analyst s industry knowledge as the most important attribute from among a set of ten potential attributes of analysts, such as accessibility, independence from corporate finance, timely calls and visits, written reports, financial models, earnings estimates, or stock selection (Institutional Investor 2000) 6

10 best practice of a specified objective such as cost minimization or profit maximization (Färe and Primont 1995). Since the frontier concept is consistent with firms optimizing behavior in microeconomic theory, deviations from the industry-specific best-practice frontier provide an intuitive measure of a firm s level of inefficiency relative to its competitors. 5 We measure operational efficiency in terms of a firm's revenue (output) generating ability, given the resources (inputs) it expends relative to its competitors. To do so, we employ a stochastic frontier methodology that incorporates a two-component error structure. One component represents random, uncontrollable factors affecting a firm s relative efficiency, whereas the second component measures the systematic firm-specific component of a firm s relative (in)efficiency. This systematic component of firm-specific error is transformed to a firm-specific measure of corporate efficiency, EFFIC. EFFIC measures how efficient a firm is relative to other firms in the same industry (this methodology is described in Section 3.3, and in more technical detail in Appendix A). As a result, EFFIC captures a wide spectrum of data relating to a firms performance within a given industrial sector. 2.3 The Barron, Kim, Lim, and Stevens (1998) Measure of Analysts Uncertainty We adopt a measure of the quality of individual analysts information outlined in the model of Barron, Kim, Lim and Stevens (1998; hereafter BKLS). 6 Specifically, we adopt the BKLS measure of earnings uncertainty for a given firm, which they define as the average squared error in the individual analysts forecasts for a given firm. BKLS show how individual analysts uncertainty (U) can be expressed in terms of expected dispersion (D) and squared error in the mean forecast (SE) as follows (see BKLS equation 15, p. 427): 7 1 U = 1 D + SE, (1) N 5 As a result, frontier models have been widely adopted in the economics and finance literatures for the study of production and cost efficiencies in evaluating firm performance (Färe et al. 1985; and Berger and Humphrey 1997). 6 See Barron et al. (2002a and 2002b), and Botosan and Harris (2000), for examples of recent empirical studies using the BKLS model. 7 The primary assumption underlying the BKLS uncertainty measure (U) is that analysts strive to issue the most accurate forecast possible, or analysts issue unbiased forecasts (Barron et al. 2002b). We examine the robustness of our results to this assumption (see section 4.3). 7

11 where: N 1 D = ( Fa F ), N 1 a=1 (2) 2 SE = ( A F ), (3) and where: 2 U is the level of uncertainty of an individual analyst (average squared error in an individual analysts forecast of a firm); N is the number of analysts forecasting; F a is the forecast by analyst a; F is the mean forecast; and A is the actual earnings realization. Use of the BKLS uncertainty measure (U) above is similar to the approach used in prior studies that have also examined observable properties of analysts earnings forecasts with a view to drawing inferences regarding analysts underlying information (Brown and Han 1992). BKLS develop this measure of the quality of individual analysts information in terms of expected (exante) dispersion (D) and squared error (SE) in the mean forecast. We use ex post realizations of D and SE as substitutes, which introduces measurement error into our estimates of the BKLS uncertainty measure (Barron et al. 2002b). This measurement error will, however, be ameliorated when firm-years are average over time, as are used here. 2.4 Control Variables We include size (market capitalization deflated to constant 1993 CPI-dollars) and industry dummy variables as control variables. Firm size has been shown to be an important determinant of the demand for information regarding a firm. Furthermore, analysts may have an incentive to expend more effort forecasting for larger firms, as this is likely to generate more trading volume for their brokerage firm employers. As a result, analysts may have an incentive to expend more effort forecasting for larger firms. Consistent with the greater demand for analysts information for larger firms, prior studies demonstrate an association between firm size and analysts information and forecast accuracy (Lys and Soo 1995). The focus of our analysis is on within-industry differences in the level of earnings uncertainty of individual analysts (U) forecasting earnings. Hence, our inclusion of industry dummy (control) variables. 8

12 3. Study Design and Sample Selection 3.1 Sample Selection Procedures We use the Active and Research files from the 1999 edition of the Compustat database to identify our sample firms. Our final sample includes all firms from selected industries (see below) with sufficient data to estimate industry-specific production-functions in any year of our five-year sample period ( ). We use a five-year sample period as prior studies indicate that efficiency scores vary through time, and as a result, DeYoung (1997) recommends the use of a sample period of about six years in efficiency studies. 8 We exclude financial services and other regulated industries, such as transportation and utilities. We exclude these regulated industries to avoid pooling firms with different operating environments (e.g., regulated versus competitive), which may affect firms behavioral goals. The selection process for the remainder of the potential sample industries is restricted by the availability of the relevant data to estimate the production frontier for that industry in that year. If there are not sufficient data for enough firms to estimate the efficient production frontier for that industry in that year, then all firms from that industry in that year are excluded from the sample. At the frontier estimation stage, the data is tested for possible outliers using the standardized residuals method, and observations with standardized residuals in excess of two are deleted from the final set (Belsley et al. 1980). Data for firm-years with the required efficiency scores (and firm size) are then matched with data for the BKLS uncertainty measure, which is calculated using forecasts of annual earnings sampled over the period 1994 through The efficiency score is lagged one year before the BKLS uncertainty measure derived from analyst forecasts (see below). 3.2 Data Calculation of our measure of individual analyst uncertainty (the average squared error in individual forecasts) requires the selection of a point in time at which to gather our forecast data. 8 If too long a sample period is selected, the concept of "average firm efficiency" looses its meaning. This arises because other factors such as management, technology, and/or regulatory environment may change over time and also affect the stability of the efficiency measures averaged. DeYoung (1997) shows that using a sample period of about six year alleviates this concern. 9

13 We base our analysis on forecasts of annual earnings for year t made during a 30-day forecast window immediately after the announcement of first quarter earnings for year t. This choice of forecast period ensures that the individual forecasts for each firm are conditioned on the same publicly available information, in this case a public earnings announcement. The short 30-day forecast period controls for the potential confounding effects of stale forecasts (Brown and Han 1992). In addition, to target active analysts, we only select forecasts that are updates of previous forecasts, issued in the 60-day period before the announcement of first quarter earnings in year t by the same individual analysts, because these analysts are less likely to be herding (Barron and Stuerke 1998). Our efficiency measure (EFFIC ij,t-1 ) is calculated using accounting data for year t-1, while our BKLS uncertainty measure is based on forecasts of earnings for year t. This is done to avoid a spurious correlation between our accounting data-based efficiency measure (EFFIC ij,t-1 ) and the BKLS measure of earnings uncertainty (U ijt ), because actual earnings is a component of the calculation of both variables. Our sample of firm-years, thus, draws on the IBES forecasts of annual earnings for fiscal year t, and Compustat data for fiscal year t-1, where the sample years are drawn from the period 1994 through Our sample firm-years meet the following four requirements: 1) The quarterly earnings announcement date for first quarter earnings for year t is available from either the Active or Research Compustat quarterly files; 2) At least two forecasts of year t annual earnings from two different individual analysts are issued in the 30-day period immediately following the first quarter earnings announcement date who are updating a previous forecast, made in the preceding 60 day period; 9 3) Actual EPS data for year t annual earnings are available from the IBES Actual Earnings file; and 4) The necessary Compustat data needed to calculate the efficiency score, and data for ROA, ROE, and control variables, is available for firm i in year t In rare cases where multiple forecasts are available from the same analyst in the 30-day forecast window after the first quarter earnings announcement, only the last forecast is selected. 10 In addition, we only include firms from industries with sufficient data to calculate the efficiency score needed. Some industries in certain years could not be included due to the inability of the industry-specific production function estimates to converge, especially when there is a small number of firms in the industry. Instead of overriding the stringent converging rules, all non-converging industries are left out of the sample to avoid possible model specification problems. 10

14 As can be seen in Panel A of Table 1, 19,985 firm-years have the required Compustat data for ROA and ROE, and the Compustat data needed to calculate the firm-specific efficiency score for that firm-year. When this sample of firm-years is matched with forecast data drawn from the IBES database, it yields a final sample of 1,839 firm-years, representing 907 separate firms. Panel B of Table 1 shows the industry composition of this sample of 907 firms. These firms are spread over 34 different industries, with concentrations in Oil and Gas Extraction (9- percent of sample), Computer Services (mainly software) (8.2-percent), Apparel and Accessory Stores (7.3-percent), Computer and Office Equipment (6.6-percent), and Electronic Components and Accessories (6.2-percent). (Insert Table 1 About Here) 3.3 Measuring Firms Relative Efficiency For the measure of relative production efficiency, we calculate individual indices of operating inefficiency for each firm-year separately by industry-year. For each industry-year, we employ a stochastic frontier methodology based on a translog production function. The firmspecific objective function employed in this study can be expressed as: ( X,w,v) Y = f, (4) where: Y is sales revenue, X is a vector of operational inputs generating this revenue, w represents firm-specific deviations from the efficient frontier due to factors under managerial control, and v represents random uncontrollable factors that affect firm s performance (see Berger and Mester 1997). To separately estimate this production relation using data for each industry-year, we employ a standard translog function with more than two inputs. 11 In the case of one output (Y) and three inputs (X s), the translog function can be expressed as follows (e.g., see Bairam 1994), which are estimated for each industry-year: 11 The translog specification is used in preference to the Cobb-Douglas specification. Unlike the Cobb-Douglas specification, the translog specification is flexible in that it allows for non-uniform scale characteristics, and a degree of substitution among inputs that is not limited to unity. 11

15 2 ( Y ) = β 0 + β 1lnX + β 2lnX 2 + β 3lnX β 11( lnx 1) β 22( lnx 2) + 0.5β 33( lnx 3) + β 12( lnx 1. lnx 2) + β 13( lnx 1. lnx 3) + β 23( lnx 2. lnx 3) + lnε ln 1 (5) This is followed by the decomposition of the error term from the estimates of this equation into its two components (v and w), as defined in equation (4). The decomposition is achieved by specifying the following distributional assumptions (see Jondrow et al. 1982): 2 (, σ ) 2 v iid N, and w N( 0, σ ). 0 v This specification of error terms is based on the intuition that the performance of firms differs as a result of : 1) random fluctuations such as luck, climate, machine performance etc (captured by v); and 2) a firm-specific components (w), capturing a firms ability to follow the industry best practice. The firm-specific inefficiency scores obtained through this error decomposition (w) are further transformed into the more appealing efficiency scores (EFFIC) using Battese and Coelli s (1988) algorithm. The specific details of the decomposition and the transformation are given in Appendix A. In our selection of input and output variables used to estimate equation (5) we follow the prior efficiency studies with some modification. We use the dollar value of net sales (Compustat item number A12) as a measure of output. The quantity of output, rather than its dollar value, has traditionally been used in efficiency studies. However, this approach has been criticized. Kolari and Zardkoohi (1987) among others, argue that firms compete to increase their market share, which is measured as their share of dollar sales in the market, as opposed to the quantity sold. Also, when there are (even slight) differences in the quality of products among firms in the same industry, this leads to a distortion in the measurement of relative efficiency across firms if one uses only a quantity measure of output. This distortion results from the fact that higher quality producers, who will tend to have higher production costs, will appear to be systematically less efficient. If higher quality producers can pass-on their higher production costs to consumers in the form of higher prices, then the dollar value of their output(s) will be a more appropriate measure of their output. w 12

16 The three inputs used in our specification of firm production function are: Cost of Goods Sold, Selling, General and Administrative Expenses, and Physical Capital Cost (that consists of rent and depreciation expense) (Compustat item numbers A41, A189, and the total of A47+A14-A163, respectively). Our choice of these three input categories also follows the choice of different types of costs in the literature regarding the evaluation of input choices in the production process, such as variable, semi-variable, and fixed costs (van den Broeck 1988; Bairam 1994). We scale all the variables in the frontier estimation by total assets to avoid heteroskedasdicity and scale bias (Berger and Mester 1997) Measuring Individual Analysts Uncertainty Using our sample of earnings forecasts, we calculate ex-post realized squared error in the mean forecast (denoted SE^ ) and dispersion (denoted D^ ). Similar to equations (2) and (3) in Section 2.2, we calculate the ex-post realized squared error in the mean forecast and dispersion for firms as follows: SE it = 2 ( Ait Fit N ( a= 1 ) (6) 2 1 D ˆ it = Fait Fit), (7) Njt 1 where SE it is the estimated squared error in the mean forecast for firm i in year t; Dˆ it is the sample variance of the forecasts for firm i in year t; Ait is the actual earnings for firm i in year t; F ait is the forecast of earnings from analyst a for firm i in year t; Fit is the mean of the forecasts for firm i in year t; and N it is the observed number of forecasts for firm i in year t. We scale these estimates of the squared error in the mean forecast (SE^ absolute value of actual EPS ( A it ). Scaled SE^ and D^ ) and dispersion ( D^ ) by the, together with the observed number of 12 Since the costs and revenues of large firms are expected to be larger than for small firms, the random errors of larger firms would have larger variances without any normalization. This is an important consideration since the (in)efficiency terms are derived from the combined residuals. Without an appropriate normalization, this effect may cause the variances in these terms to depend on firm size. 13

17 analysts forecasting (N), are substituted into equation (1) to calculate BKLS uncertainty (U it ) for firm i in year t Empirical Models Important data and econometric issues arise in estimating the above equations. First, our data consists of observations for 1,839 firm-years, which represent 907 separate firms. Our sample consists of an unbalanced set of panel data; some firms have more yearly observations than others. In addition, firm efficiency scores (and firm size) tend to be correlated across sample years (Kwan and Eisenbeis, 1996), introducing the possibility of cross-sectional dependence affecting a pooled cross-sectional time-series analysis of the data. As a result, we replace the firm-year observations with firm-specific mean values, computed across all the firmyears available for each firm. Using such across-panel means is a widely adopted procedure for such unbalanced panel datasets (Greene 2000; p. 567). 14 In addition, we cannot predict linear relations between the dependent and independent variables. This arises from the fact that BKLS uncertainty, our dependent variable, is based upon squared error in individual forecasts. Thus, similar to Lang and Lundholm (1996), we use nonparametric rank regressions (with the average squared error in individual forecasts (U) as our dependent variable). 15 We test the association between BKLS uncertainty and firm relative operational efficiency using the following equation: Model 1: mui = ψ 0 + ψ 1mEFFICi + ψ 2mSIZEi + γjidj + εi. (8) J j= 2 13 We scale by the absolute value of actual EPS ( A it ) to ensure that SE^ and D^ are comparable across firm-years. We also remove observations with absolute values of A it less than 10 cents from the sample as a control for extreme observations induced by the choice of scaling variable. We also conduct our analysis using unscaled SE^ and D ^ and scaling by price. Our inferences are unchanged using these alternative specifications. 14 We also use a Weighted Least Squared (WLS) estimator (see Section 4.3) using the number of firm-year observations per firm as a weighting variable. Our inferences are unchanged using this alternative specification. 15 Use of ranks relaxes the linearity assumption, and assumes only a monotonic relation (Conover 1980; and Lang and Lundholm 1996). As a result, the rank regression specification is more efficient than ordinary least squares using the untransformed data. Results from a comparison of the regression residuals for the untransformed and rank-transformed specifications are consistent with an improved specification using the rank transformation. 14

18 mx indicates that we are taking the mean value of the variable X for firm i across all available years for our sample period. We also estimate Model 1 using both ROA and ROE in the place of EFFIC. Where: mu i is the (rank of) average level of uncertainty across the analysts forecasting earnings for firm i; meffic i is the (rank of) relative efficiency score for firm i; msize i is (rank of) market capitalization of firm i; and ID j are a series of industry dummy variables equal to one for firms in industry j. Model 1 tests the association between a firms relative production efficiency (EFFIC) and individual analysts earnings uncertainty, controlling for firm size and industry effects. We predict that: ψ 1 <0. As an alternative, we also estimate Model 1 using either ROA or ROE as alternative experimental variables proxying for firms relative operational efficiency. Again, for these alternative estimates of Model 1, we predict that: ψ 1 <0. In summary, to test our first conjecture that more efficient firms have earnings that are easier for analysts to predict, we estimate three variants of Model 1 above, using EFFIC, ROA, or ROE as three alternative proxies for firms relative efficiency. Our second hypothesis relates to the relative strength of the relationship between earnings uncertainty and within-industry variation in EFFIC versus the ratios ROA or ROE. We test the directional hypothesis that there is a stronger relationship between EFFIC and U then between either ROE or ROA and U. We use a Vuong test, which is based upon a comparison of nonnested models, to test this hypothesis. This test is based upon a comparison of the R 2 statistics of the different models estimated (i.e., Model 1 with EFFIC vs. Model 1 with ROA; and Model 1 with EFFIC vs. Model 1 with ROE). (Insert Table 2 About Here) Table 2, presents descriptive statistics for the sample of 907 firms. Consistent with concurrent research (Barron et al. 2002a and 2002b), the data for BKLS uncertainty (mu i ) indicates that the distribution of this variable is highly skewed -- for example, the mean of the sample is , whereas the standard deviation is This is not surprising, since BKLS uncertainty is a measure of the average squared error in the individual forecasts of analysts 15

19 following a firm, and is calculated using dispersion (forecast variance) and squared error in the mean forecast. Our sample firms are quite large, with a mean market capitalization of approximately $2.3 billion. 4. Empirical Results As the first step in our analysis, a production frontier is estimated for each industry-year in our sample period (1993 through 1997) using the Translog equation (5) setout in section 3.3. The residuals from these industry-specific efficient frontier estimates are then decomposed into their random (v) and firm-specific components (w) for each firm-year. The firm-specific component (w) is, in turn, used to calculate the efficiency score, EFFIC (see Appendix A for details of the transformation). This efficiency score data is then matched with IBES earnings forecast data to produce the final dataset of 1,839 firm-years representing 907 firms described in Tables 1 and Pairwise Correlation Analysis Table 3 presents Spearman (Rank) correlations between our variables of interest. As can be seen in Table 3, consistent with our expectation, we find a negative association between firms relative production efficiency (EFFIC) and BKLS uncertainty (U) (correlation coefficient equals 0.14; significant at p<0.01, one-tailed test). In addition, analysts earnings uncertainty is also negatively related to both the return on assets (ROA) and return on equity (ROE) ratios -- correlation coefficients are 0.19 and 0.20, both significant at p<0.01, one-tailed. Thus, the strength of the association between both ratios and analysts earnings uncertainty seems, in fact, to be stronger than the association between relative production efficiency and individual analysts earnings uncertainty. Consistent with prior research (Lys and Soo 1995), we also find a negative association between firm size (SIZE) and BKLS uncertainty (correlation coefficient equals 0.07; significant at p<0.01, one-tailed test). (Insert Table 3 About Here) The pairwise evidence presented in Table 3 suggests that BKLS uncertainty (U), is negatively associated with both firms relative production efficiency, and the ratios ROA and 16

20 ROE. These analyses are incomplete however, as we have not controlled for other known determinants of analysts information environment, and therefore, they do not constitute evidence of a marginal effect of EFFIC controlling for size and industry effect. We address this question next. 4.2 Multivariate Regression Analysis and Vuong Test Results Table 4 presents our results from our Ordinary Least Squares (OLS) rank regression estimates of models based on equation (8). Note, we do not display the coefficient estimates for the 33 industry dummy variables also included in the estimation. Consistent with our expectations, the OLS rank regression analysis of equation (8) confirms a negative relationship between firms relative production efficiency, as proxied by EFFIC, and BKLS uncertainty (U) - - see Model 1a. Consistent with our first general expectation, controlling for firm size and industry, there is a significant negative association between within-industry variations in analysts earnings uncertainty and the relative production efficiency of those firms (p<0.01, onetailed). (Insert Table 4 About Here) Table 4 also shows the results of two alternative estimated models based on equation (8) - - Models 1b and 1c, where we use either ROA or ROE as alternative experimental variables. Consistent with our expectation, we find a negative association between either ROA or ROE and individual analysts earnings uncertainty (p<0.01, one-tailed, for both models). Again, consistent with prior research indicating that analysts have more precise information, as reflected in smaller forecast errors for larger firms (see Lys and Soo 1995), in all three estimates of Model 1 we find a significantly negative association between BKLS uncertainty and firm size (p<0.05, one tailed). 16 The R 2 s reported in Table 4 suggest that efficiency explains more of the variation in analysts uncertainty than either ROA or ROE. However, to compare Model 1a formally with 16 The results on SIZE are marginally significant (at the 0.05, one-tailed test). In part this is due to the industry control variables, which capture variations in firm size across industries. Running the analysts without the industry control variables yields stronger results for SIZE. 17

21 models 1b and 1c in terms of their explanatory powers, we employed Vuong test for nonnested models. Other nonnested model tests such as J-test by Davidson and Mackinnon (1981) can give inconclusive results when the models include variables with incremental explanatory variables as may be the case here (Dechow 1994). The Vuong test on the other hand is a more powerful test, and allows a directional test to determine the more powerful model for explaining a given dependent variable. 17 Table 4 includes the results of the Vuong tests comparing Model 1a with both Models 1b and 1c. The z-statistics (and corresponding one-tail p-values) comparing these models are shown on the right hand side of Table 4. Since positive and significant test statistics implies residuals from models 1b (ROA) and 1c (ROE) are larger than model 1a (EFFIC), consistent with our argument, we find that the model with EFFIC is a better model in explaining the variation in analyst earnings uncertainty (p=0.02 and 0.03, one-tailed test). As an additional test to compare the relative strength of the association between individual analysts level of earnings uncertainty (U) and relative operational efficiency (EFFIC), compared to either the ROA or ROE ratio, we also estimate nested models and use a Wald test (Greene 2000, p. 273) to compare coefficient estimates. Specifically, we estimate two models; first including both EFFIC and ROA, and second including both EFFIC and ROE as independent variables. We then use a Walt test to compare the magnitude of the coefficient on EFFIC with that of either ROA or ROE. Untabulated results indicate that the coefficient on EFFIC is statistically larger (p<0.01) for both models. EFFIC, thus, seems to capture more information associated with within-industry variations in the quality of analysts information about earnings than either ROA or ROE. Since 17 Following Dechow (1994), we obtained m i (mean of likelihood ratio, LR) for each observation using: RSS roa, roe n ( eiroa, iroe ) ( eieff ) mi = log + 2 RSSeffic 2 RSSroa, roe RSSeff where e iroa or iroe and e ieff residuals, and RSS roa, roe and RSS eff are the residual sum of squares from models 1b, 1c and 1a respectively. Next, we calculated the standard deviation of LR by regressing m i on unity. The coefficient in this regression is equal to the first term in m i above giving us the mean difference in explanatory power between the efficiency scores and ROA or ROE. The standard error from this regression provides the significance of this difference. The z-statistic to evaluate the significance, then, is calculated using the t-statistic from this regression 1/ 2 multiplied by (( n 1) / n). 18

22 EFFIC is a broader and more encompassing proxy for relative efficiency, which has been shown to be related to the firm s performance stability, this indicates that analysts act as if they use extensive within-industry knowledge regarding production processes and operational efficiency to forecast earnings more accurately. This may partly help explain why individual analysts specialize so much by industry in selecting the firms they tend to follow. 4.3 Sensitivity Analysis We performed several sensitivity tests to establish the robustness of the results. Unless otherwise outlined below, our inferences from our results on Tables 4 and 5 are unchanged using any of these alternative specifications: Weighted Least squares: We re-estimated all the regressions reported in this paper using Weighted Least Squares (WLS), where the number of observations available for each firm in each quarterly regression forms the weighting variable (Kmenta 1997, p. 368). Our results for firm size are more significant using the WLS estimator. This is not surprising, as the WLS estimator places relatively more weighting on the observations for larger firms that tend to have a greater number of firm-year observations. Scaling Variable: In our analysis D^ and SE^ are scaled by the absolute value of actual Earnings Per Share (EPS). We also conduct our analysis using stock price at the end of the fiscal year as a scaling variable, and using unscaled D^ and SE^. Average Bias-Adjusted Estimator: The principal assumptions underlying the BKLS uncertainty measure is that analysts strive to issue the most accurate forecast possible, or that forecasts are unbiased (see Barron et al. 2002b). We test the possibility that the results of our hypotheses tests could be affected by bias. Following Barron et al. (2002b), as a diagnostic measure, we recalculate the squared error in the mean forecast as follows: where SE * ijt * SEijt = 1 I ( Aijt Fijt) ( I i = 1 2 A ijt Fijt), (12) is the squared error in the mean forecast for firm i in industry j in year t, adjusted for the average level of error across all analysts forecasting for all analysts forecasting for all firm- 19

23 years in the sample. Thus, we adjust the squared errors in the mean forecasts of firms for the average level of optimism in all forecasts for all firm-years in our sample. Different Specifications of ROA: We also use a number of different specifications of ROA (for example scaling by the average of total assets between the start and end of the fiscal year). Alternative Forecast Sample Periods: We also conduct our analysts using samples of forecast revisions made after the second and third quarterly earnings announcements. 5. Conclusion Evidence indicates that relatively more efficient firms tend to have more stable levels of output compared to other firms within the same industries (Mills and Schumann 1985). These firms also tend to have more sustainable levels of operating performance, as indicated by the lower firm-specific time-series standard deviations of their profitability ratios (ROA s) (Berger et al. 1993; Berger and Mester 1997). Given their more stable and sustainable level of performance, we test the related prediction that these firms have more predictable earnings. We test this prediction using analysts forecasts of annual earnings. In this study, we examine the relationship between the quality of individual analysts information regarding annual earnings (analysts earnings uncertainty), and a proxy for firms relative operational efficiency. We estimate firm efficiency using a stochastic frontier approach using data on multiple firm inputs. This approach provides a broad measure of a firm s relative operational efficiency within its industry. First, we test if this measure of a firm s relative efficiency is related to the quality of analysts information regarding earnings, controlling for firm size and industry effects. Results indicate that analysts earnings uncertainty (the quality of their information) is lower (higher) for more efficient firms. Second, we compare the performance of this variable to that of two related accounting ratios, Return on Assets (ROA) and Return on Equity (ROE), which we use as two alternative proxies for firms level of relative operational efficiency. The results of this analysis indicate a stronger association between analysts earnings uncertainty and the measure of relative operational efficiency, than the accounting ratios ROA and ROE. 20