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.     

Evidence that Prices Do Not Fully Reflect the Implications of Current Earnings for Future Earnings: An Experimental Markets Approach*

Abstract. Analysts have been found to underweight the innovation in the most recent quarterly earnings when forecasting next-quarter earnings, and these expectations have been posited as an explanation for post-earnings-announcement drift. This study uses an experimental asset market to examine whether similar errors in forecasting quarterly earnings are made by student-subjects. We examine two aspects of their behavior: (1) do subjects underestimate the autocorrelation in quarterly earnings when forming earnings expectations? and (2) are asset prices consistent with a subject's underestimation of the autocorrelation in quarterly earnings? We observe subject errors in forecasts that underweight extreme innovations in the most recent quarterly earnings by approximately 40 percent. The prices in the experimental markets also fail to reflect fully the most recent innovation in quarterly earnings. We are able to predict the sign of the incorrect pricing, from the mean initial earnings predictions of the subjects, in 74 percent of the 135 markets. These forecast errors observed in this study are consistent with forecast errors observed for analysts, and this consistency suggests that errors in analysts' forecasts may be at least partially attributable to the use of judgmental heuristics.     

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.     

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.     

Are Earnings Forecasts Informed by Proxy Statement Compensation Disclosures?

We investigate the extent to which market participants use compensation payouts released in the DEF 14A proxy statement (DEF14A) to assess future firm performance by examining sell-side analysts' earnings forecasts. Consistent with prior work, we confirm that CEO compensation unexplained by current observable economic factors is positively associated with future firm performance. We find that both the likelihood that analysts revise their forecasts following release of the DEF14A and the magnitude and direction of analysts' forecast revisions are positively associated with unexplained CEO compensation. These associations are stronger after the SEC required additional compensation-related disclosures in late 2006 but lower if the firm has weak corporate governance or more precise other information. Analysts' reactions are not complete, however. Analysts' forecast errors measured months after the DEF14A release are associated with past unexplained compensation, especially in the pre-2006 period and for analysts who do not revise at the DEF14A release. Taken together, our results suggest that compensation payouts released in the DEF14A contain useful forward-looking information that is recognized by at least some sophisticated market participants and that the increased disclosure regulations assisted market participants in incorporating this information.     

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.     

The reaction of Canadian securities to revisions of earnings forecasts*

Abstract. This first study of Canadian securities' earnings forecasts published by Institutional Brokers Estimate System (IBES) focuses on changes in the mean earnings per share forecasts of 159 to 188 companies from 1985 to 1987. Cumulative average residuals are used to detect the announcement effects of large earnings forecast revisions. The main results of this study are the following. First, an investor with access to changes of earnings per share forecasts at the beginning of the month of publication could realize abnormal excess returns. Second, trading strategies based on earnings forecasts revisions can also yield abnormal returns, but the magnitude of the revision, the sector of the company, and the month in which the revision is realized must be considered. Third, when financial analysts' forecasts are published, the informational content of large revisions in forecasts has already been discounted by the market. This result is similar to findings of U.S.– and U.K.–based studies. Finally, large forecasts revisions coincide with a period of abnormal returns. However, the information content of the announcement of forecasts changes cannot be established. The gains are larger if the trade is undertaken before the diffusion of the forecast revision to the IBES subscribers. These results do not vary with the model chosen to predict company returns. This does not necessarily indicate the existence of a market inefficiency because information acquisition and analysis costs, as well as transaction costs, may diminish considerably these abnormal trading gains.     

The Effect of Measurement Subjectivity Classifications on Analysts' Use of Persistence Classifications When Forecasting Earnings Items

Earnings items are typically classified in financial reports based on their persistence and measurement subjectivity. Archival research examines investors' use of persistence and measurement subjectivity classifications for forecasting and valuation. However, this research typically examines only one of these classifications at a time and ignores the potential interactive implications of an earnings item's persistence and measurement subjectivity classifications. We recruited experienced financial analysts to participate in two experiments that examined the effect of measurement subjectivity classifications on analysts' use of persistence classifications when forecasting earnings items. We find that analysts rely less on an earnings item's persistence classification when measurement subjectivity is high relative to when measurement subjectivity is low. We also find that presentation format affects analysts' use of these two classifications. Specifically, we find that the matrix format (i.e., rows display persistence classifications and columns display measurement subjectivity classifications) facilitates analysts' combined use of persistence and measurement subjectivity classifications relative to the sequential format (i.e., the classifications are displayed separately). These findings suggest that archival research could improve its examination of market participants' use of earnings classifications for forecasting and valuation by recognizing that the implications of an earnings item's persistence classification can vary according to the item's measurement subjectivity classification. By also demonstrating how presentation format affects analysts' use of earnings classifications, our study provides further insights into this fundamental issue in accounting research and standard setting.     

Analysts' Interpretation and Investors' Valuation of Tax Carryforwards*

We examine how financial analysts and equity investors incorporate information on deferred taxes from carryforwards into earnings forecasts and share prices. We focus on carryforwards because, in providing this information each period, management must use their private information about the firm's profitability prospects. Thus, accounting measurement of tax carryforwards is another way of providing a management earnings forecast. In analyzing the role of carryforwards in valuation, we distinguish between two conflicting effects. First, deferred taxes from carryforwards represent future tax savings; hence, they should be valued positively as assets. In contrast, the existence of tax carryforwards may signal a higher likelihood of future losses, which would have a negative effect on expected earnings and share prices. We find that analysts consider earnings of firms with carryforwards to be less persistent because of the increased likelihood of future losses. We also find that analysts tend to be less precise and more optimistic (biased) in forecasting earnings of firms with carryforwards. This higher optimism and lower precision are more pronounced just after firms adopt Statement of Financial Accounting Standards (SPAS) 109 and are almost entirely corrected over time. An analysis of investors' valuation indicates a strong positive relation between deferred taxes from carryforwards and share prices, suggesting that these carryforwards are valued as assets. Also, earnings and book values of equity are valued less in firms that have carryforwards than in firms without carryforwards. Finally, the valuation allowance required under SFAS 109 assists equity investors in valuing a firm's earnings and net assets. The combined findings on analysts' interpretation and investors' valuation suggest that analysts fail to fully capture the implication of carryforwards on future earnings within their forecasting horizon.     

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.     

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.     

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.     

Does Tax Planning Affect Analysts' Forecast Accuracy?

We investigate whether firms' tax planning affects the accuracy of analysts' forecasts. Tax planning can exacerbate the complexity of firms' operations through strategic choices to exploit tax laws. Because of its effect on firms' operations, tax planning can influence analysts' efforts to understand and forecast earnings. Specifically, if the additional complexity arising from tax planning makes firm attributes less representative of expected earnings, analysts may issue less accurate forecasts. Using auditor‐provided tax services (APTS) as a measure of tax planning, we find that, as firms spend more on tax planning, the accuracy of analysts' forecasts of both earnings per share and tax expense declines. We also document that firms with higher levels of APTS have greater year‐to‐year volatility in, and lower persistence of, effective tax rates and earnings. Our results suggest that increased firm complexity, due to greater tax planning, makes earnings and tax expense more difficult to forecast and that analysts do not properly adjust for these effects. Thus, when deciding to engage in tax planning, firms appear to make trade‐offs between potential tax savings and negative effects on earnings properties and analysts' forecasts.     

Are Analysts' Cash Flow Forecasts Naïve Extensions of Their Own Earnings Forecasts?

We examine the sophistication of analysts' cash flow forecasts to better understand what accrual adjustments, if any, analysts make when forecasting cash flows. As a preliminary step, we first demonstrate that prior empirical tests used to evaluate the sophistication of analysts' cash flow forecasts are not diagnostic. We then present three sets of evidence to triangulate our conclusion that analysts' cash flow forecasts incorporate meaningful accrual adjustments. First, we review a stratified random sample of 90 analyst reports and find that the majority of these analysts include explicit adjustments for working capital and other accruals in their cash flow forecasts. Second, using a large sample of analysts' cash flow forecasts from 1993–2008, we find that these forecasts outperform time‐series cash flow forecasts in correctly predicting the sign and magnitude of accruals. Finally, we find a significant market reaction to analysts' cash flow forecast revisions, suggesting that investors find these revisions informative. Collectively, our findings demonstrate that analysts' cash flow forecasts are not simply naïve extensions of their own earnings forecasts, but that they reflect meaningful and useful accrual adjustments. These findings are relevant to researchers who examine analysts' cash flow forecasts in a variety of settings, and to investors and practitioners who employ these forecasts for valuation purposes.     

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.     

On Constructing an EPS Measure: An Assessment of the Properties of Dilution*

This paper evaluates the information content of the treasury stock method for computing diluted earnings per share (EPS). We demonstrate that the treasury stock method decreases the annual association between earnings changes and stock returns and explain why this is the case. Further, we show that the treasury stock method leads to a dilutive adjustment that biases the random walk model of annual earnings in a predictable direction. Finally, we demonstrate that using the treasury stock method appears to confuse both analysts and investors: analysts' forecast errors increase with the size of the dilutive adjustment, and the association between unexpected earnings and stock returns at the earnings announcement date weakens as the dilutive adjustment increases.     

Consensus,Dispersion and Security prices*

Abstract. This study establishes and tests, within the framework of a noisy rational expectations equilibrium model, the existence of a formal linear relationship between security prices, the average (consensus) and the dispersion of agents' expectations. Variations in the average and in the dispersion of agents' expectations, measured by the earnings forecasts produced by financial analysts, which are gathered and made available by The Institutional Brokers Estimate System (I/B/E/S), have respectively a positive and negative effect on security prices. The difficulties raised by this estimation, as well as the institutional dimensions of the financial analysis industry are examined. The main results are the following: (1) the most important changes in consensus (in absolute value) correspond to the most important changes in dispersion in the analysts' forecasts, (2) the changes in the consensus and the dispersion of forecasts are respectively positively and negatively linked to Canadian security returns, but given the delay between the production and the public availability of the forecasts, an important part of the price adjustment occurs before the disclosure of forecast changes, (3) the effect on security returns of variations in the consensus dominates the effect of variations in the forecasts' dispersion. Thus, it seems that the impact of information arrival on security prices does not only depend on the direction and the magnitude of the expectations' average revision, but also depends on the direction and the magnitude of the change in the expectations' dispersion.     

Expectations Management and Beatable Targets: How Do Analysts React to Explicit Earnings Guidance?*

This study investigates security analysts' reactions to public management guidance and assesses whether managers successfully guide analysts toward beatable earnings targets. We use a panel data set between 1995 and 2001 to examine the fiscal‐quarter‐specific determinants of management guidance and the timing, extent, and outcomes of analysts' reactions to this guidance. We find that management guidance is more likely when analysts' initial forecasts are optimistic, and, after controlling for the level of this optimism, when analysts' forecast dispersion is low. Analysts quickly react to management guidance and are more likely to issue final meetable or beatable earnings targets when management provides public guidance. Our evidence suggests that public management guidance plays an important role in leading analysts toward achievable earnings targets.     

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.     

Mitigating the Influence of Analysts Who Issue Aggressive Stock Price Targets: The Role of Joint Versus Separate Evaluation*

Investors frequently rely on individual analysts' stock price targets. Aggressive price targets often reflect analysts' attempts to strategically influence investors. Therefore, investors' welfare may be compromised if they take aggressive price targets at face value. In this study, we examine conditions under which investors are more likely to infer that analysts who issue aggressive price targets are acting strategically. Investors can evaluate multiple analysts' price targets with or without other related information (e.g., earnings estimates). Investors can also evaluate the information provided by multiple analysts jointly or separately one analyst at a time. Two experiments find that as predicted, when investors evaluate multiple analysts' price targets without earnings estimates, there is no difference in investors' perceptions about whether the aggressive analyst is acting strategically across joint versus separate evaluation. However, also as predicted, when investors evaluate multiple analysts' price targets along with their earnings estimates, investors perceive the aggressive analyst as acting more strategically under joint evaluation than under separate evaluation. Our findings suggest that jointly evaluating multiple analysts' price targets with other related information, such as earnings estimates, can reduce the likelihood that investors would be overly influenced by aggressive analysts.