A Re-Examination of Financial Analysts' Differential Earnings Forecast Accuracy*

Abstract. This research re-examines whether there are differences in the forecast accuracy of financial analysts through a comparison of their annual earnings per share forecasts. The comparison of analyst forecast accuracy is made on both an ex post (within sample) and an ex ante (out of sample) basis. Early examinations of this issue by Richards (1976), Brown and Rozeff (1980), O'Brien (1987), Coggin and Hunter (1989), O'Brien (1990), and Butler and Lang (1991) were ex post and suggest the absence of analysts who can provide relatively more accurate forecasts over multiple years. Contrary to the results of prior research and consistent with the belief in the popular press, we document that differences do exist in financial analysts' ex post forecast accuracy. We show that the previous studies failed to find differences in forecast accuracy due to inadequate (or no) control for differences in the recency of forecasts issued by the analysts. It has been well documented in the literature that forecast recency is positively related to forecast accuracy (Crichfield, Dyckman, and Lakonishok 1978; O'Brien 1988; Brown 1991). Thus, failure to control for forecast recency may reduce the power of tests, making it difficult to reject the null hypothesis of no differences in forecast accuracy even if they do exist. In our analysis, we control for the differences in recency of analysts' forecasts using two different approaches. First, we use an estimated generalized least squares estimation procedure that captures the recency-induced effects in the residuals of the model. Second, we use a matched-pair design whereby we measure the relative forecast accuracy of an analyst by comparing his/her forecast error to the forecast error of another randomly selected analyst making forecasts for the same firm in the same year on or around the same date. Using both approaches, we find that differential forecast accuracy does exist amongst analysts, especially in samples with minimum forecast horizons of five and 60 trading days. We show that these differences are not attributable to differences in the forecast issuance frequency of the financial analysts. In sum, after controlling for firm, year, forecast recency, and forecast issuance frequency of individual analysts, the analyst effect persists. To validate our findings, we examine whether the differences in the forecast accuracy of financial analysts persist in holdout periods. Analysts were assigned a “superior” (“inferior”) status for a firm-year in the estimation sample using percentile rankings on the distribution of absolute forecast errors for that firm-year. We use estimation samples of one- to four-year duration, and consider two different definitions of analyst forecast superiority. Analysts were classified as firm-specific “superior” if they maintained a “superior” status in every year of the estimation sample. Furthermore, they were classified as industry-specific “superior” if they were deemed firm-specific “superior” with respect to at least two firms and firm-specific “inferior” with respect to no firm in that industry. Using either definition, we find that analysts classified as “superior” in estimation samples generally remain superior in holdout periods. In contrast, we find that analysts identified as “inferior” in estimation samples do not remain inferior in holdout periods. Our results suggest that some analysts' earnings forecasts should be weighted higher than others when formulating composite earnings expectations. This suggestion is predicated on the assumption that capital markets distinguish between analysts who are ex ante superior, and that they utilize this information when formulating stock prices. Our study provides an ex ante framework for identifying those analysts who appear to be superior. When constructing weighted forecasts, a one-year estimation period should be used because we obtain the strongest results of persistence in this case.     

THE PERFORMANCE CONSEQUENCES OF SUBFIELD ENTRY

The emergence of technical subfields is a common phenomenon in dynamic as well as relatively stable industries. The proper strategic response to the emergence of a subfield, that is, the decision on whether to enter or not to enter, is a key determinant of future firm performance. We propose that this entry decision is not a simple one. The effects of subfield entry may be influenced by strategic factors related to the subfield as well as to the greater industry environment. In this study, we apply a population ecology framework to the study of subfield birth and evolution and use this perspective to develop and test several propositions related to the effects of subfield entry on performance. The data pertain to the evolution of the automatic teller machine subfield over the first 9 years of its existence for a population of over 3500 banks. Our results support the population ecology framework, generally emphasizing the positive performance consequences of early subfield entry. © 1997 by John Wiley & Sons, Ltd.     

Learning and the Yield Curve

Two central implications of Expectations Hypothesis under rational expectations are inconsistent with yield curve data: (i) future expected long yields fall, instead of rising, when yield spread rises; (ii) long yields are excessively volatile relative to short yields. I propose an optimization framework in which boundedly rational agents use adaptive learning to form expectations. The belief structure rationalizes pattern of yields observed in the data so that the first puzzle does not arise with subjective expectations: intertemporal income and substitution effects are amplified relative to rational expectations. The second puzzle is partly accounted for by extra volatility due to parameter uncertainty.     

Estimating the Intertemporal Risk–Return Tradeoff Using the Implied Cost of Capital

We argue that the implied cost of capital (ICC), computed using earnings forecasts, is useful in capturing time variation in expected stock returns. First, we show theoretically that ICC is perfectly correlated with the conditional expected stock return under plausible conditions. Second, our simulations show that ICC is helpful in detecting an intertemporal risk–return relation, even when earnings forecasts are poor. Finally, in empirical analysis, we construct the time series of ICC for the G–7 countries. We find a positive relation between the conditional mean and variance of stock returns, at both the country level and the world market level.     

Additional Evidence on Analysts’ Decision to Issue Disaggregated Earnings Forecasts: Strategic Biasing

This study examines why analysts issue disaggregated earnings forecasts to I/B/E/S. Some recent studies suggest that analysts with superior forecasting ability issue disaggregated earnings forecasts to build reputation in the marketplace and stop forecast disaggregation once their reputation has been established. Based on an analysis of I/B/E/S forecast data for U.S. firms from 1998 to 2008, we find that, in a given year, about 20%–34% of analysts disaggregate for some, but not for all the firms that they follow. This evidence of selective disaggregation by analysts suggests that reputation building alone does not fully explain the decision to disaggregate forecasts. We hypothesize that the decision to disaggregate earnings forecasts is at the firm‐level as well and is systematically related to the analysts’ bias in the issued forecasts. Our findings are that (a) analysts’ overall optimistic bias and forecast errors decrease monotonically with the level of forecast disaggregation, and (b) analysts that selectively disaggregate their forecasts for some firms or who do not persistently disaggregate a given firm's forecasts exhibit more positive bias and larger forecast errors. Our findings are consistent with the notion that the analysts who issue biased forecasts, for example, to curry favour with the management, are less likely to provide disaggregated information as part of the forecast.     

The 1932 Federal Reserve Open-Market Purchases as a Precedent for Quantitative Easing

The $1 billion open-market operation conducted by the Federal Reserve, at the height of the Great Depression, was a successful precedent to the recent Quantitative Easing (QE) programs. The 1932 program entailed large purchases of medium- and long-term securities over a 4-month period. An event study analysis indicates that the program dramatically lowered medium- and long-term Treasury yields. A segmented markets model is used to analyze the effects of the open-market purchases on the economy. A significant degree of financial market segmentation is estimated, and partly explains the observed upturn in output growth. Had the Federal Reserve continued its operations and used the announcement strategy used in QE1, the Great Contraction could have been attenuated earlier. Our historical analysis suggests that the Federal Reserve in 2008 had a good predecessor to its actions.