Investors' Misweighting of Firm-Level Information and the Market's Expectations of Earnings

Prior studies use fundamental earnings forecasts to proxy for the market's expectations of earnings because analyst forecasts are biased and are available for only a subset of firms. We find that as a proxy for market expectations, fundamental forecasts contain systematic measurement errors analogous to those in analysts' biased forecasts. Therefore, these forecasts are not representative of investors' beliefs. The systematic measurement errors from using fundamental forecasts to proxy for market expectations occur because investors misweight the information in many firm-level variables when estimating future earnings, but fundamental forecasts are formed using the historically efficient weights on firm-level variables. Thus, we develop an alternative ex ante proxy for the market's expectations of future earnings (“the implied market forecast”) using the historical (and inefficient) weights, as reflected in stock returns, that the market places on firm-level variables. A trading strategy based on the implied market forecast error, which is measured as the difference between the implied market forecast and the fundamental forecast, generates excess returns of approximately 9 percent per year. These returns cannot be explained by investors' reliance on analysts' biased forecasts. Overall, our results reveal that market expectations differ from both fundamental forecasts and analysts' forecasts.     

To Guide or Not to Guide? Causes and Consequences of Stopping Quarterly Earnings Guidance

In recent years, quarterly earnings guidance has been harshly criticized for inducing “managerial short‐termism” and other ills. Managers are, therefore, urged by influential institutions to cease guidance. We examine empirically the causes of such guidance cessation and find that poor operating performance — decreased earnings, missing analyst forecasts, and lower anticipated profitability — is the major reason firms stop quarterly guidance. After guidance cessation, we do not find an appreciable increase in long‐term investment once managers free themselves from investors’ myopia. Contrary to the claim that firms would provide more alternative, forward‐looking disclosures in lieu of the guidance, we find that such disclosures are curtailed. We also find a deterioration in the information environment of guidance stoppers in the form of increased analyst forecast errors and forecast dispersion and a decrease in analyst coverage. Taken together, our evidence indicates that guidance stoppers are primarily troubled firms and stopping guidance does not benefit either the stoppers or their investors.     

The Incremental Informativeness of Stock Prices for Future Accounting Earnings*

This study extends previous research that documents a stock price reaction leading accounting earnings. The primary issue is that prior studies use a naive earnings expectation model (random walk) as the benchmark for the information content of lagged returns and do not adequately address the “incremental” information content of lagged returns. This study identifies and estimates firm-specific models of earnings to control directly for the autocorrelation in earnings. The explanatory power of lagged prices with respect to this earnings residual is investigated using both a multiple regression model of lagged returns and a multiple time-series vector autoregressive model. In-sample estimation of the models provides clear evidence that stock prices impound information about future earnings incremental to the information contained in historical earnings data. Holdout period analysis of the earnings forecasts from these lagged return models finds that both models outperform the naive seasonal random walk expectation, but neither model outperforms the more sophisticated Box-Jenkins forecasts. On an individual firm basis, earnings forecasts supplemented with the lagged return data tend to be less precise than the Box-Jenkins forecasts, but the price-based models demonstrate an ability to rank the earnings forecast errors from the time-series models. The analysis helps to characterize the limitations of lagged returns as a means of predicting future earnings innovations.     

Valuation Implications of Unconditional Accounting Conservatism: Evidence from Analysts' Target Prices

We examine whether financial analysts understand the valuation implications of unconditional accounting conservatism when forecasting target prices. While accounting conservatism affects reported earnings, conservatism per se does not have an effect on the present value of future cash flows. We examine whether analysts adjust for the effect of conservatism included in their earnings forecasts when using these forecasts to estimate target prices. We find that signed target price errors (actual minus forecast) have a significant positive association with the degree of conservatism in forward earnings, suggesting that target prices are biased due to accounting conservatism. Cross‐sectional analysis suggests that more sophisticated analysts and superior long‐term forecasters adjust for conservatism to a greater extent than other analysts. In additional analyses, we explore the mechanism through which conservatism leads to bias in target prices. We first show that analysts' earnings forecasts are negatively associated with the degree of conservatism; that is, analysts include the effect of unconditional conservatism in their earnings forecasts. Based on alternative earnings‐based valuation models that analysts may use, our evidence suggests that analysts fail to appropriately adjust their valuation multiple for the effect of conservatism included in their earnings forecasts when using these forecasts to derive target prices. As a consequence, we find that, for extreme changes in conservatism, the bias in analysts' target prices due to conservatism leads to a distortion of market prices. The evidence highlights the concern that analysts may not appreciate the valuation implications of conservative accounting which could inhibit price discovery.     

Price and Volume Reactions to Public Information Releases: An Experimental Approach Incorporating Traders' Subjective Beliefs*

This paper examines how market prices, volume, and traders' dividend expectations respond to public information releases in laboratory markets for a long-lived financial asset. The objective is to study deviations from the symmetric information risk-neutral rational expectations (RE) benchmark, which predicts no trade in such settings. The results of a series of double-auction and call markets are reported in which traders manage a portfolio of cash and asset shares over 15 rounds of trading. A public signal regarding the value of the liquidating dividend is released every third round, and traders' subjective expectations of the liquidating dividend are elicited each round as cash-motivated forecasts. We find that, despite the public dividend signal, traders' dividend forecasts are heterogeneous. Forecasts and prices both underreact to the public signals, with prices under-reacting more than forecasts. In general, price changes are not closely associated with public signals, and there is greater excess price volatility in double auctions than in call markets. Forty-three percent of trades are inconsistent with the trader's forecasts, and inconsistent trades occur more frequently in the double-auction markets. On average, approximately 10 percent of the outstanding shares are traded in each round, and trading volume is increasing in the mean absolute forecast revision and decreasing in the contemporaneous dispersion in forecasts. These results suggest that differential processing of the public signal and/or speculative trading for short-term gain may help to explain why symmetric information RE predictions are often not supported in empirical and experimental settings. They also suggest that market reactions to public information releases may be influenced by market microstructure.     

Earnings Announcement Disclosures and Changes in Analysts' Information

This study examines how financial disclosures with earnings announcements affect sell‐side analysts' information about future earnings, focusing on disclosures of financial statements and management earnings forecasts. We find that disclosures of balance sheets and segment data are associated with an increase in the degree to which analysts' forecasts of upcoming quarterly earnings are based on private information. Further analyses show that balance sheet disclosures are associated with an increase in the precision of both analysts' common and private information, segment disclosures are associated with an increase in analysts' private information, and management earnings forecast disclosures are associated with an increase in analysts' common information. These results are consistent with analysts processing balance sheet and segment disclosures into new private information regarding near‐term earnings. Additional analysis of conference calls shows that balance sheet, segment, and management earnings forecast disclosures are all associated with more discussion related to these items in the questions‐and‐answers section of conference calls, consistent with analysts playing an information interpretation role with respect to these disclosures.     

Maintaining a Reputation for Consistently Beating Earnings Expectations and the Slippery Slope to Earnings Manipulation

This paper investigates whether maintaining a reputation for consistently beating analysts' earnings expectations can motivate executives to move from “within GAAP” earnings management to “outside of GAAP” earnings manipulation. We analyze firms subject to SEC enforcement actions and find that these firms consistently beat analysts' quarterly earnings forecasts in the three years prior to the manipulation period and continue to do so by smaller “beats” during the manipulation period. We find that manipulating firms beat expectations around 86 percent of the time in the 12 quarters prior to the manipulation period (versus 75 percent for control firms) and that manipulation often ends with a miss in expectations. We document that executives of manipulating firms face strong stock market and CEO pressure to perform. Prior to the manipulation period, these firms have high analyst optimism, growing institutional interest, and high market valuations, along with powerful CEOs. Further, we find that maintaining a reputation for beating expectations is more important than CEO overconfidence and is incremental to CEO equity incentives for explaining manipulation. Our results suggest that pressure to maintain a reputation for beating analysts' expectations can encourage aggressive accounting and, ultimately, earnings manipulation.     

An Analysis of Historical and Future-Oriented Information in Accounting-Based Security Valuation Models*

The Ohlson (1995) and Feltham and Ohlson (1995) valuation model provides a rigorous framework for summarizing the information in expected future earnings and book values. However, the model provides little guidance on selecting an empirical proxy for expected future earnings. We examine whether and under what circumstances historical earnings and analyst earnings forecasts offer comparable explanation of security prices. This issue is of particular interest because analyst forecasts are less readily available than historical data. Under appropriate circumstances, historical data may allow wider use of the Feltham-Ohlson valuation model by researchers and investors. A related issue is the incremental explanatory power of historical earnings and realized future earnings (perfect-foresight forecasts) for security prices beyond analyst forecasts. If historical earnings are incrementally informative, that would suggest that analyst forecasts do not fully reflect price-relevant information in past earnings. If future earnings are incrementally informative, that would suggest that security prices reflect investors' implicit earnings forecasts beyond analyst forecasts. We examine these issues using a historical model (based on past earnings), a perfect-foresight model (based on realized future earnings), and a forecast model (based on Value Line earnings forecasts). All three models provide significant explanatory power for security prices, and each set of earnings data provides incremental explanatory power for prices when used with the other sets of earnings data. We estimate the models separately for firms with moderate and extreme earnings-to-price (E/P) ratios, a proxy for earnings permanence. For moderate-E/P firms, the historical model's explanatory power exceeds that of the perfect foresight model, and is indistinguishable from that of the analyst forecast model. In contrast, for extreme-E/P firms, the perfect-foresight model offers greater explanatory power than the historical model, but lower explanatory power than analyst forecasts. Our results suggest that financial analysts' forecasting efforts are best focused on firms whose earnings contain large temporary components (extreme E/P firms). However, in general, both historical data and analyst forecasts are complementary information sources for security valuation.     

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.     

Horizon‐Dependent Underreaction in Financial Analysts' Earnings Forecasts*

This paper provides empirical evidence that underreaction in financial analysts' earnings forecasts increases with the forecast horizon, and offers a rational economic explanation for this result. The empirical portion of the paper evaluates analysts' responses to earnings‐surprise and other earnings‐related information. Our empirical evidence suggests that analysts' earnings forecasts underreact to both types of information, and the underreaction increases with the forecast horizon. The paper also develops a theoretical model that explains this horizon‐dependent analyst underreaction as a rational response to an asymmetric loss function. The model assumes that, for a given level of inaccuracy, analysts' reputations suffer more (less) when subsequent information causes a revision in investor expectations in the opposite (same) direction as the analyst's prior earnings‐forecast revision. Given this asymmetric loss function, underreaction increases with the risk of subsequent disconfirming information and with the disproportionate cost associated with revision reversal. Assuming that market frictions prevent prices from immediately unraveling these analyst underreac‐tion tactics, investors buying (selling) stock on the basis of analysts' positive (negative) earnings‐forecast revisions also benefit from analyst underreaction. Therefore, the asymmetric cost of forecast inaccuracy could arise from rational investor incentives consistent with a preference for analyst underreaction. Our incentives‐based explanation for underreaction provides an alternative to psychology‐based explanations and suggests avenues for further research.     

Determinants of Managerial Earnings Guidance Prior to Regulation Fair Disclosure and Bias in Analysts' Earnings Forecasts*

Prior to Regulation Fair Disclosure (“Reg FD”), some management privately guided analyst earnings estimates, often through detailed reviews of analysts' earnings models. In this paper I use proprietary survey data from the National Investor Relations Institute to identify firms that reviewed analysts' earnings models prior to Reg FD and those that did not. Under the maintained assumption that firms conducting reviews guided analysts' earnings forecasts, I document firm characteristics associated with the decision to provide private earnings guidance. Then I document the characteristics of “guided” versus “unguided” analyst earnings forecasts. Findings demonstrate an association between several firm characteristics and guidance practices: managers are more likely to review analyst earnings models when the firm's stock is highly followed by analysts and largely held by institutions, when the firm's market‐to‐book ratio is high, and its earnings are important to valuation but hard to predict because its business is complex. A comparison of guided and unguided quarterly forecasts indicates that guided analyst estimates are more accurate, but also more frequently pessimistic. An examination of analysts' annual earnings forecasts over the fiscal year does not distinguish between guidance and no‐guidance firms; both experience a “walk‐down” in annual estimates. To distinguish between guidance and no‐guidance firms, one must examine quarterly earnings news: unguided analysts walk down their annual estimates when the majority of the quarterly earnings news is negative; guided analysts walk down their annual estimates even though the majority of the quarterly earnings news is positive.     

Who Herds? Who Doesn't? Estimates of Analysts’ Herding Propensity in Forecasting Earnings

We develop parametric estimates of the imitation‐driven herding propensity of analysts and their earnings forecasts. By invoking rational expectations, we solve an explicit analyst optimization problem and estimate herding propensity using two measures: First, we estimate analysts’ posterior beliefs using actual earnings plus a realization drawn from a mean‐zero normal distribution. Second, we estimate herding propensity without seeding a random error, and allow for nonorthogonal information signals. In doing so, we avoid using the analyst's prior forecast as the proxy for his posterior beliefs, which is a traditional criticism in the literature. We find that more than 60 percent of analysts herd toward the prevailing consensus, and herding propensity is associated with various economic factors. We also validate our herding propensity measure by confirming its predictive power in explaining the cross‐sectional variation in analysts’ out‐of‐sample herding behavior and forecast accuracy. Finally, we find that forecasts adjusted for analysts’ herding propensity are less biased than the raw forecasts. This adjustment formula can help researchers and investors obtain better proxies for analysts’ unbiased earnings forecasts.     

Do Firms Use Tax Reserves to Meet Analysts’ Forecasts? Evidence from the Pre‐ and Post‐FIN 48 Periods

We examine whether firms decrease tax reserves to meet analysts’ quarterly earnings forecasts in the period prior to FIN 48, and whether that behavior changed following FIN 48. We use analysts’ forecasts of pretax and after‐tax income to impute premanaged earnings, or earnings before any tax manipulation. Pre‐FIN 48, we observe that firms reduce their tax reserves (i.e., increase income) when premanaged earnings are below analysts’ forecasts. Specifically, 78 percent of firm‐quarters that would have missed the analyst forecast if not for the tax reserve decrease, meet that target when the decrease is included. Furthermore, we find a significant positive association between the decrease in tax reserves and the deviation of premanaged earnings from analysts’ forecasts. In contrast, post‐FIN 48, we find no evidence that firms use changes in tax reserves to manage earnings to meet analysts’ forecasts. Thus, our results suggest that FIN 48 has, at least initially, curtailed firms’ use of tax reserves to manage earnings.     

Forecasting Risk in Earnings

Conventional measures of risk in earnings based on historical standard deviation require long time‐series data and are inadequate when the distribution of earnings deviates from normality. We introduce a methodology based on current fundamentals and quantile regression to forecast risk reflected in the shape of the distribution of future earnings. We derive measures of dispersion, asymmetry, and tail risk in future earnings using quantile forecasts as inputs. Our analysis shows that a parsimonious model based on accruals, cash flows, special items, and a loss indicator can predict the shape of the distribution of earnings with reasonable power. We provide evidence that out‐of‐sample quantile‐based risk forecasts explain incrementally analysts' equity and credit risk ratings, future return volatility, corporate bond spreads, and analyst‐based measures of future earnings uncertainty. Our study provides insights into the relations between earnings components and risk in future earnings. It also introduces risk measures that will be useful for participants in both the equity and credit markets.     

Earnings Management to Avoid Losses and Earnings Decreases: Are Analysts Fooled?*

This paper explores whether analyst forecasts impound the earnings management to avoid losses and small earnings decreases documented in Burgstahler and Dichev 1997, whether analysts are able to identify which specific firms engage in such earnings management, and the implications for significant forecast error anomalies at zero earnings and zero forecast earnings. We use data from Zacks Investment Research 1999 and find that analysts anticipate earnings management to avoid small losses and small earnings decreases. Further, analysts are much more likely to forecast zero earnings than firms are to realize zero earnings, and analysts are unable to consistently identify the specific firms that engage in earnings management to avoid small losses. This latter inability contributes to significant forecast pessimism associated with zero reported earnings and significant forecast optimism associated with zero earnings forecasts.     

An Approach to Adjusting Analysts' Consensus Forecasts for Selection Bias*

Many recent empirical studies have concluded that analysts' earnings forecasts are optimistic on average. In this paper, we attempt to undo the effect of one potential source of optimistic bias in analysts' earnings forecasts. Assuming forecasts come from a truncated normal distribution, we estimate the “true” population mean using maximum likelihood. We find that our estimates of earnings are more accurate and less biased than standard measures of sample mean and median. However, we do not find a closer relationship between excess market returns and forecast errors from our maximum likelihood estimate than from the sample mean. This may suggest that the market does not fully incorporate analysts' incentives in generating expectations about future earnings.     

The Relation between Analysts' Forecasts of Long‐Term Earnings Growth and Stock Price Performance Following Equity Offerings*

In this paper we evaluate the role of sell‐side analysts' long‐term earnings growth forecasts in the pricing of common equity offerings. We find that, in general, sell‐side analysts' long‐term growth forecasts are systematically overly optimistic around equity offerings and that analysts employed by the lead managers of the offerings make the most optimistic growth forecasts. In additional, we find a positive relation between the fees paid to the affiliated analysts' employers and the level of the affiliated analysts' growth forecasts. We also document that the post‐offering underperformance is most pronounced for firms with the highest growth forecasts made by affiliated analysts. Finally, we demonstrate that the post‐offering underperformance disappears once we control for the overoptimism in earnings growth expectations. Thus, the evidence presented in this paper is consistent with the “equity issue puzzle” arising from overly optimistic earnings growth expectations held at the time of the offerings.     

Analyst Coverage and the Likelihood of Meeting or Beating Analyst Earnings Forecasts

This paper examines the relation between analyst coverage and whether firms meet or beat analyst earnings forecasts. We distinguish between whether a firm's reported quarterly earnings meet (i.e., equal or exceed by one cent) or beat (i.e., exceed by more than one cent) its consensus analyst earnings forecasts. We find a positive relation between analyst coverage and whether a firm meets or beats analyst forecasts. However, the more pronounced relation is that between analyst coverage and meeting analyst forecasts. Also, when we consider exogenous shocks to analyst coverage due to brokerage mergers or closures and conglomerate spinoffs, we continue to find a robust positive relation only between analyst coverage and meeting analyst forecasts. To shed light on the causal relation involved, we examine and find that greater analyst coverage is associated with a significantly larger market reaction to negative earnings surprises. We also document that firms with greater analyst coverage are more likely to guide analyst earnings forecasts downwards. Taken together, our evidence suggests that greater analyst coverage raises the pressure on managers to meet analyst earnings forecasts.     

Unintended Effects of Preannouncements on Investor Reactions to Earnings News*

This study uses an experiment to examine three alternative theoretical explanations for the unintended effects of preannouncements on investor reactions to earnings news. The theoretical explanations are cue consistency, recency effects, and diminishing marginal reactions. The experiment varies the amount of a management preannouncement at five different levels while holding constant consensus analyst expectations prior to the preannouncement and the subsequent earnings announcement. Participants provide preliminary forecasts of current‐ and next‐period earnings per share (EPS) prior to the preannouncement, after the preannouncement, and after the earnings announcement. The pattern of participants' final next‐year EPS forecasts and the results of follow‐up analyses appear most consistent with the predictions of diminishing marginal reactions and, to a somewhat lesser extent, cue consistency, suggesting that both mechanisms play a role in determining the effects of preannouncements. There is little evidence supporting recency effects. Finally, supplemental evidence indicates that participants are unaware that preannouncements influence their reactions to earnings news, suggesting that the effects are unintended. This study has implications for managers who make preannouncement disclosure decisions and for academics who wish to understand and interpret prior research on earnings preannouncements.     

A Comparison of Dividend,Cash Flow,and Earnings Approaches to Equity Valuation*

Standard formulas for valuing the equity of going concerns require forecasting payoffs to infinity but practical analysis requires that payoffs be forecasted over finite horizons. This truncation inevitably involves often-troublesome terminal value calculations. This paper contrasts dividend discount techniques, discounted cash flow analysis, and techniques based on accrual earnings when each is applied with finite-horizon forecasts. Valuations based on average ex post payoffs over various horizons, with and without terminal value calculations, are compared with ex ante market prices to discover the error introduced by each technique in truncating the horizon. Valuation errors are lower using accrual earnings techniques rather than cash flow and dividend discounting techniques. The accounting features that make a given technique less than ideal for finite horizon analysis are also detailed. Conditions where a given technique requires particularly long forecasting horizons are identified and the performance of the alternative techniques under those conditions is examined.