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Do Analysts Say Anything About Earnings Without Revising Their Earnings Forecasts?
Abstract
Analysts are selective about which forecasts they update and, thus, convey information about current quarter earnings even when not revising the current quarter earnings (CQE) forecast. We find that (1) textual statements, (2) share price target revisions, and (3) future quarter earnings forecast revisions all predict error in the CQE forecast. We document several reasons analysts sometimes omit information from the CQE forecast: to facilitate beatable forecasts by suppressing positive news from the CQE forecast, to herd toward the consensus, and to avoid small forecast revisions. We also show that omitting information from CQE forecasts leads to lower forecast dispersion and predictable returns at the earnings announcement.
... Producing accurate earnings forecasts requires effort to properly calibrate information to the horizon of the forecast. If there are benefits to biasing forecasts-for example, overweighting private information or catering to managers (Bernhardt et al. 2006;Berger et al. 2019)-then analysts will trade off their incentives to bias with their incentives for accuracy. We argue accuracy incentives are stronger (weaker) at shorter (longer) horizons. ...
... First, because more recent forecasts have greater salience at the time of the earnings release, we argue accuracy incentives are higher (lower) at shorter (longer) forecast horizons (e.g., DeBondt and Thaler 1990). Second, analysts have incentives to provide beatable forecasts at shorter horizons and optimistic forecasts at longer horizons (e.g., Ke and Yu 2006;Berger et al. 2019). Thus their incentives for optimism increase with the forecast's horizon, which decreases accuracy incentives by introducing bias. ...
... 16 15 In untabulated analyses, we find adjusting earnings forecasts using other horizon forecasts does not improve cost of capital estimates. 16 Berger et al. (2019) show that, after the initial forecast of the quarter, analysts tend to respond to good news by revising share price targets and bad news by revising the current quarter's earnings forecast. In this section, we document that longer horizon earnings forecast revisions respond more to good news than shorter horizon earnings forecasts. ...
We show analysts’ own earnings forecasts predict error in their own forecasts of earnings at other horizons, which we argue provides a measure of the extent to which analysts inefficiently use information. We construct our measure by exploiting two sources of variation in analysts’ incentives: (i) more recent forecasts have greater salience at the time of the earnings release so accuracy incentives are higher (lower) at shorter (longer) forecast horizons and (ii) analysts have greater incentives for optimism (pessimism) at longer (shorter) horizons. Consistent with these incentives affecting the incorporation of information into forecasts, we document (i) current year forecasts underweight (overweight) information in shorter (longer) horizon forecasts and (ii) the mis-weighting is more pronounced when recent news is negative—when analysts have greater (weaker) incentives to incorporate the news into shorter (longer) horizon forecasts. Finally, returns tests suggest that forecasts adjusted for the inefficiency we document better represent market expectations of earnings.
... 8 The Internet Appendix is available in the online version of this article on The Journal of Finance website. The stylized facts presented in Section I of the Internet Appendix complement evidence on analysts' incentives to produce quantitative and qualitative information about firm performance after their last forecast revision (Berger, Ham, and Kaplan (2019)). 9 While we argue that it is important to incorporate within-quarter forecasts to determine the consensus, our results are robust to different sample-period bounds within the targeting quarter. ...
We show that public banks face negative stock return jumps after missing their earnings per share (EPS) targets, and theoretically and quantitatively link these jumps to bunching behavior in the EPS surprise distribution. Bunching banks cut deposit rates to meet their targets, but do so at the expense of deposit outflows and franchise value losses. Local competitors, including private banks unexposed to capital market pressure, increase deposit rates, compensating depositors for switching. Our results provide new evidence that performance targeting incentives can affect consumer product prices, and suggest that competition may provide a check on public firms' targeting efforts.
... 4 A related issue is the possible manipulation of GDP growth targets. In the accounting literature, managers and analysts may engage in an "earnings guidance game" and manage down their earnings forecasts (Richardson et al., 2004;Berger et al., 2019). ...
... Further, for model (2), we include several additional variables that affect analysts forecast errors. These controls are accounting quality (AQ), forecast horizon (HORIZON), earnings surprise (SURPRISE) and lagged analysts forecast errors (LEFROR) (Berger, Ham, & Kaplan, 2019;Desai, Hogan, & Wilkins, 2006;Dhaliwal et al., 2012;Ge, Ji, & Louis, 2021;Lang and Lundholm, 1996;Lennox, 2016). Variable definitions are provided in Appendix A. The results are reported in Table 12. ...
This paper investigates the impact of the Shanghai–Hong Kong Stock Connect (SHSC) scheme on voluntary corporate social responsibility (CSR) disclosure in China. Using a difference-in-differences (DiD) design, we find that companies that participate in the SHSC scheme are more inclined to voluntarily issue CSR reports. This effect is more pronounced for companies that have limited access to international markets and those with weak corporate governance. Additional analyses show that SHSC-connected firms also produce higher quality CSR reports and achieve a better CSR performance. Our findings imply that capital market liberalization promotes voluntary corporate disclosure for investors.
Facing regulatory pressure while balancing the demand for market guidance, analysts must weigh the risk of noncompliance to fulfill stakeholders' interests. Literature suggests that analysts strategically adjust the presentation of information in their research reports to meet competing demands. This study analyzes a sample of 76,922 Chinese analysts' research reports from 2018 to 2021 and examines how the 2020 Standards and Norms in China influenced analysts' earnings forecast revisions and textual tones. The findings exhibit a significant reduction in the magnitude of earnings forecast revisions postpolicy, particularly among analysts who rely heavily on private information, as well as increased optimism in textual tone. Policy details and penalties enhance compliance, whereas reputation incentives (e.g., star analyst titles) strengthen regulatory effects. However, manipulative incentives (e.g., shared provincial ties, high institutional ownership, or “walk‐down” forecast patterns) weaken these effects. This study enhances the understanding of analysts' forecast behaviors and their interrelationships while contributing new insights into the limitations of current regulatory effectiveness.
Consensus analyst target prices are widely available online at no cost to investors. In this paper, we examine how the amount of dispersion in the individual target prices comprising the consensus affects the predictive association between the consensus target price and future returns. We find that returns implied by consensus target prices and realized future returns are positively correlated when dispersion is low, but they become highly negatively correlated when dispersion is high. Further analyses suggest that the differing effect of dispersion stems from incentive-driven staleness in price targets by some analysts after bad news. As a stock performs poorly and some analysts are slow to update their target prices, dispersion increases, and the consensus target price becomes too high. This has important implications for how consensus analyst target prices should inform investment decisions. We show that a hedge strategy taking a long (short) position in stocks with the highest predicted returns among stocks with the lowest (highest) dispersion earns more than 11% annually. Finally, we show that the negative correlation between consensus-based predicted returns and future realized returns for high-dispersion stocks exists mainly for stocks with high retail interest, suggesting that unsophisticated investors are misled by inflated target prices that are available freely online.
This paper was accepted by Suraj Srinivasan, accounting.
Funding: The authors acknowledge financial support from Indiana University and Yale University.
Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.03549 .
This study examines whether crowdsourced forecasts of earnings and revenues help investors unravel bias in earnings announcement news, which is commonly derived from analyst forecasts. Our results suggest that investors, on average, understand and price the predictive signals reflected in crowdsourced forecasts about the bias in analyst-based earnings and revenue surprises. Using the staggered addition of firms to the Estimize platform, we find that crowdsourced coverage is associated with reductions in the mispricing of forecast bias and declines in earnings announcement premia. We further find some evidence that managers use income-increasing accruals to meet the crowdsourced forecast benchmark and that they respond to crowdsourced coverage through increased downward earnings and revenue guidance. Overall, we conclude that user-generated content on crowdsourced financial information platforms helps investors discount biases in traditional equity research and thereby better process the news in earnings announcements.
JEL Classifications: G14; G20; M41.
Analysts’ street earnings forecasts are sometimes based on GAAP earnings and sometimes based on non-GAAP earnings, which exclude various GAAP earnings components. Therefore, differences in analysts’ street earnings forecasts may capture differences in not only expected performance but also the earnings metric forecasted. We argue that analysts who forecast non-GAAP, rather than GAAP, street earnings are more likely to separately analyze earnings components. Consistent with this argument, we find that analysts who forecast non-GAAP street earnings issue relatively more accurate forecasts. We also argue that excluded earnings components often reflect negative transitory items, and that variation across analysts in the earnings metric forecasted suggests that the negative excluded items are forecasted by only a subset of analysts. Consistent with this assertion, we find that variation across analysts in the earnings metric forecasted is associated with a lower consensus GAAP earnings surprise and lower stock returns around the earnings announcement. Finally, although variation in the earnings metric forecasted is a source of analyst forecast dispersion, we find that it is also incrementally associated with a lower earnings response coefficient, consistent with the existence of transitory items. We therefore find that the variation in the earnings metric forecasted is an important source of analyst forecast dispersion that predicts not only a lower earnings surprise but also a lower earnings response.
This paper was accepted by Brian Bushee, accounting.
Purpose
In the backdrop of emerging market setting of India, this study aims to attempt to identify how Institutional investors use sell side analyst outputs for their decision-making processes in light of inherent biases in their forecasts and recommendations. The study also conceptualizes the role of internal buy side teams in the process and try to figure out the key attributes and services provided by sell side analysts, which provide maximum value to the investors.
Design/methodology/approach
The study is centered upon in-depth semi-structured interviews of ten institutional investors from top Indian asset management companies covering a wide range of topics tied back to theoretical explanations. The data collected was transcribed, coded and analyzed using content analysis to ensure a systematic synthesis of point of view.
Findings
The findings show that internal analyst teams of institutional investors play a dominant role in terms of validation of sell side analysts’ outputs (given the inherent biases in sell side analyst forecasts). Further, the engagement of sell side analysts by the investors are determined not only through profitable recommendations but also on the basis of soundness of the investment rationale along with other services provided. Finally, this study puts into perspective, the critical role of analyst industry knowledge and access to company management (as opposed to analyst pedigree and forecast accuracy) for institutional investors decision-making.
Practical implications
The findings of the paper have profound implications for various stakeholders such as companies, sell side analysts, policy makers, researchers and students of finance in terms of detailed understanding of investment processes of institutional investors in the context of emerging markets like India, which have a different legal and regulatory set-up compared to developed markets. The authors also provide a critical perspective through an intriguing paradox that exists between finance theory and its relevance for actual practitioners.
Originality/value
To the best of the authors’ knowledge, this is the first study in India which look inside the “black box” of institutional investors and their decision-making process, especially with respect to how they use sell side outputs.
Manuscript Type
Empirical
Research Question/Issue
This study examines reactions of financial analysts to the disclosure of corporate fraud. We posit that analysts downgrade earnings forecasts of fraud firms after fraud disclosure to signal their quality and integrity. We explore how internal and external contingencies shape analysts’ reputational concerns, influence their motivation to signal via earnings downgrades, and consequently affect their responses to corporate fraud.
Research Findings/Insights
Using longitudinal data on Chinese publicly traded firms between 2002 and 2013 and employing a difference‐in‐differences design, this study documents that financial analysts significantly lower their earnings estimates of fraudulent firms after fraud disclosure compared to the control group of non‐fraud firms. In addition, post‐disclosure earnings downgrades are larger for more experienced analysts, for analysts following firms with stronger analyst coverage, and after the revision of the professional ethics code for analysts.
Theoretical/Academic Implications
This study provides empirical support to signaling theory. We show that earnings downgrades of fraudulent firms may serve as a signal for financial analysts to indicate their quality and integrity. We document that analysts’ motivation to signal via earnings downgrades is influenced by their reputational concerns, which are moderated by their career experience, peer pressure, professional norm and social expectations.
Practitioner/Policy Implications
This study offers insights to policy makers on how a professional labor market and codes of ethics could serve as disciplinary mechanisms to strengthen the external governance role of financial analysts.
We examine a specific form of what we term analyst contrarianism. We define contrarianism as cases where an analyst expresses a summary opinion contrary to the direction of a given earnings surprise or revision. Distinct from analyst optimism or boldness, we document that analysts interpret negative (positive) earnings news in a positive (negative) light in approximately 11–15 percent of reports. We conjecture that some analysts look for opportunities to make a contrarian stock call for their clients in order to gain visibility, recognition, and career advancement. Our empirical evidence, which is supported by analyst interviews and content analysis of analyst reports, shows that: (1) analysts at non-top-tier brokerage houses are more likely to make a contrarian call, (2) analyst reports that contain contrarian opinions are associated with greater market reactions, and (3) contrarian analysts are more likely to exhibit career advancement.
JEL Classifications: G41; M41.
This paper develops a test for herding in forecasts by professional financial analysts that is robust to (a) correlated information amongst analysts; (b) common unforecasted industry-wide earnings shocks; (c) information arrival over the forecasting cycle; (d) the possibility that the earnings that analysts forecast differ from what the econometrician observes; and (e) systematic optimism or pessimism among analysts. We find that forecasts are biased, but that analysts do not herd. Instead, analysts “anti-herd”: Analysts systematically issue biased contrarian forecasts that overshoot the publicly-available consensus forecast in the direction of their private information.
A constraint that actions can depend on observations only through a communication channel with finite Shannon capacity is shown to be able to play a role very similar to that of a signal extraction problem or an adjustment cost in standard control problems. The resulting theory looks enough like familiar dynamic rational expectations theories to suggest that it might be useful and practical, while the implications for policy are different enough to be interesting.
We investigate whether investors price the option to abandon a firm at its exit value. Theory prices this real option as an American put with both a stochastic strike price (exit value) and a stochastic value of the underlying security (the value of cash flows). The empirical implications are that firm value increases in exit value, after controlling for expected going-concern cash flows, and that more generalizable assets produce more abandonment option value. Using discounted earnings forecasts to proxy for expected cash flows and prior literature to categorize asset generalizability, we find strong support for the predictions of abandonment option theory.
We examine the impact of Regulation Fair Disclosure on the competitive advantage of All-Star analysts as measured by turnover in the rankings. Institutional Investor All-Americans, chosen by votes of institutional investors based on overall helpfulness, experienced a significant increase in turnover as Reg FD was implemented. Non-U.S. analysts and U.S. analysts ranked solely on the basis of public stock recommendations did not. Within a few years, however, All-American turnover returned to pre-Reg-FD levels, suggesting that the new All-Americans built a competitive advantage stressing aspects of performance less dependent on privileged communication.
We estimate the dynamics of recommendations by financial analysts, uncovering the determinants of inertia in their recommendations. We provide overwhelming evidence that analysts revise recommendations reluctantly, introducing frictions to avoid frequent revisions. More generally, we characterize the sources underlying the infrequent revisions that analysts make. Publicly available data matter far less for explaining recommendation dynamics than do the recommendation frictions and the long-lived information that analysts acquire but the econometrician does not observe. Estimates suggest that analysts structure recommendations strategically to generate a profitable order flow from retail traders. We provide extensive evidence that our model describes how investors believe analysts make recommendations, and that investors value private information revealed by analysts' recommendations. © 2016 The Accounting Research Center at the University of Chicago Booth School of Business.
Our objective is to penetrate the “black box” of sell‐side financial analysts by providing new insights into the inputs analysts use and the incentives they face. We survey 365 analysts and conduct 18 follow‐up interviews covering a wide range of topics, including the inputs to analysts’ earnings forecasts and stock recommendations, the value of their industry knowledge, the determinants of their compensation, the career benefits of Institutional Investor All‐Star status, and the factors they consider indicative of high‐quality earnings. One important finding is that private communication with management is a more useful input to analysts’ earnings forecasts and stock recommendations than their own primary research, recent earnings performance, and recent 10‐K and 10‐Q reports. Another notable finding is that issuing earnings forecasts and stock recommendations that are well below the consensus often leads to an increase in analysts’ credibility with their investing clients. We conduct cross‐sectional analyses that highlight the impact of analyst and brokerage characteristics on analysts’ inputs and incentives. Our findings are relevant to investors, managers, analysts, and academic researchers.
Using both a linear regression method and a probability-based method, we find that on average, analysts place larger than
efficient weights on (i.e., they overweight) their private information when they forecast corporate earnings. We also find
that analysts overweight more when issuing forecasts more favorable than the consensus, and overweight less, and may even
underweight, private information when issuing forecasts less favorable than the consensus. Further, the deviation from efficient
weighting increases when the benefits from doing so are high or when the costs of doing so are low. These results suggest
that analysts’ incentives play a larger role in misweighting than their behavioral biases.
The expectations management literature has so far focused on firms meeting the analyst consensus forecast—the expectations of analysts as a group—at earnings announcements. In this study we argue that investors may use individual analyst forecasts as additional benchmarks in evaluating reported earnings because the consensus forecast underutilizes private information contained in individual analyst forecasts. We predict that measures reflecting such private information have incremental explanatory power over the consensus forecast for the market's reaction to earnings news. We find results consistent with this prediction by examining two measures: (1) the percentage of individual forecasts met and (2) meeting the key analyst forecast. We extend the literature by documenting the role of individual analyst forecasts in investors' evaluations of reported earnings.
JEL Classifications: G10; G11; G17; G14; G24.
Data Availability: Data are publicly available from the sources identified in the paper.
We examine implications of the conjecture that analysts announce recommendations and forecasts selectively, based upon whether their information about the firm is favorable. We propose this as an alternative to the common assumption that analysts introduce bias into their forecasts, and provide empirical evidence on this alternative. An effect of selective reporting is that ex post observed distributions of earnings forecast errors appear over-optimistic, even if each forecast is unbiased ex ante. This occurs because high forecasts are both more likely to be observed and more likely to be over-optimistic than low forecasts, for any given realization. We find strong evidence supporting the self-selection conjecture in analyst recommendations, and generally consistent evidence in analyst forecast errors. We also document that analysts avoid issuing negative information by sparse use of sell recommendations and by delaying downgrades, but not by avoiding downgrades altogether.
I investigate interaction that occurs privately between sell-side analysts and senior management by examining a set of internal records compiled by a large-cap NYSE traded firm. I find that certain types of analysts interact more frequently with management including those who cover fewer firms, have less experience as an analyst, expend more effort covering the firm, and work for more influential firms. Private interaction between analysts and managers also differs from public interaction that occurs via conference calls in terms of the timing of interaction and depth of management access. Overall, the analysis suggests that private interaction with management can be an important communication channel for analysts for reasons other than firm-specific forecasting news.
Prior research suggests that financial analysts' earnings forecasts and stock prices underreact to earnings news. This paper provides evidence that analysts and investors correct this underreaction in response to the next earnings announcement and to other (non-earnings-surprise) information available between earnings announcements. Our evidence also suggests that analysts and investors underreact to information reflected in analysts' earnings forecast revisions and that non-earnings-surprise information helps correct this underreaction as well. Controlling for corrective non-earnings-surprise information significantly increases estimates of the degree to which analysts' forecasting behavior can explain drifts in returns following both earnings announcements and analysts' earnings forecast revisions.
We use proprietary data from a major investment bank to investigate factors associated with analysts’ annual compensation. We find compensation to be positively related to "All-Star" recognition, investment-banking contributions, the size of analysts’ portfolios, and whether an analyst is identified as a top stock-picker by the Wall Street Journal. We find no evidence that compensation is related to earnings forecast accuracy. But consistent with prior studies, we find analyst turnover to be related to forecast accuracy, suggesting that analyst forecasting incentives are primarily termination-based. Additional analyses indicate that "All-Star" recognition proxies for buy-side client votes on analyst research quality used to allocate commissions across banks and analysts. Taken as a whole, our evidence is consistent with analyst compensation being designed to reward actions that increase brokerage and investment-banking revenues. To assess the generality of our findings, we test the same relations using compensation data from a second high-status bank and obtain similar results.
It has been alleged that firms and analysts engage in an earnings guidance game where analysts first issue optimistic earnings forecasts and then 'walk down' their estimates to a level firms can beat at the official earnings announcement. We examine whether the walk-down to beatable targets is associated with managerial incentives to sell stock after earnings announcements on the firm's behalf (via new equity issuance) or from their personal accounts (through option exercises and stock sales). Consistent with these hypotheses, we find that the walk-down to beatable targets is most pronounced when firms or insiders are net sellers of stock after an earnings announcement. These findings provide new insights on the impact of capital market incentives on communications between managers and analysts.
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.
Using virtually all publicly available analyst earnings forecasts for a sample of 58 companies in the 1980–1986 period (over 23,000 individual forecasts by 100 analyst firms), our evidence indicates that individual analyst earnings forecasts are informative, even when they are preceded by earnings forecasts made by other analysts or by corporate accounting disclosures. However, our results indicate that analysts earnings forecasts contain only roughly 66% of the information reflected by security prices prior to the forecast-release date.
We examine the impact of Regulation Fair Disclosure on the competitive advantage of All-Star analysts as measured by turnover in the rankings. Institutional Investor All-Americans, chosen by votes of institutional investors based on overall helpfulness, experienced a significant increase in turnover as Reg-FD was implemented. Non-US analysts and US analysts ranked solely on the basis of public stock recommendations did not. Within a few years, however, All-American turnover returned to pre-Reg-FD levels, suggesting that the new All-Americans built a competitive advantage stressing aspects of performance less dependent on privileged communication.
This paper develops a formal model of analyst earnings forecasts that discriminates between rational behavior and that induced by cognitive biases. In the model, analysts are Bayesians who issue sequential forecasts that combine new information with the information contained in past forecasts. The model enables us to test for cognitive biases, and to quantify their magnitude. We estimate the model and find strong evidence that analysts are overconfident about the precision of their own information and also subject to cognitive dissonance bias. But they are able to make corrections for bias in the forecasts of others. We show that our measure of overconfidence varies with book-to-market ratio in a way consistent with the findings of Daniel and Titman (1999). We also demonstrate the existence of these biases in international data.
Archived here is the preprint version, also found on SSRN.
This study hypothesizes that because shareholders have a liquidation option, losses are not expected to perpetuate. They are thus less informative than profits about the firm's future prospects. The results are consistent with the hypothesis. They also show that the documented increase in the earnings response coefficent as the cumulation period increases appears to be due exclusively to the effect of losses. The liquidation option effect extends to profitable cases where earnings are low enough to make the option attractive. Alternating explanations for the low informativeness of losses such as mean reversal of earnings are not supported by the tests.
This paper provides evidence that analysts who have predicted earnings more accurately than the median analyst in the previous four quarters tend to be simultaneously less accurate and further from the consensus forecast in their subsequent earnings prediction. This phenomenon is economically and statistically meaningful. The results are robust to different estimation techniques and different control variables. Our findings are consistent with an attribution bias that leads analysts who have experienced a short-lived success to become overconfident in their ability to forecast future earnings.
Several theories of reputation and herd behavior (e.g., Scharfstein and Stein (1990) and Zweibel (1995)) suggest that herding among agents should vary with career concerns. Our goal is to document whether such a link exists in the labor market for security analysts. We find that inexperienced analysts are more likely to be terminated for inaccurate earnings forecasts than are their more experienced counterparts. Controlling for forecast accuracy, they are also more likely to be terminated for bold forecasts that deviate from the consensus. Consistent with these implicit incentives, we find that inexperienced analysts deviate less from consensus forecasts. Additionally, inexperienced analysts are less likely to issue timely forecasts, and they revise their forecasts more frequently. These findings are broadly consistent with existing career concern motivated herding theories.
Using both a linear regression method and a probability-based method, we find that on average, analysts place larger than efficient weights on (i.e., they overweight) their private information when they forecast corporate earnings. We also find that analysts overweight more when issuing forecasts more favorable than the consensus, and overweight less, and may even underweight, private information when issuing forecasts less favorable than the consensus. Further, the deviation from efficient weighting increases when the benefits from doing so are high or when the costs of doing so are low. These results suggest that analysts' incentives play a larger role in misweighting than their behavioral biases. Copyright 2006, Oxford University Press.
This study classifies analysts' earnings forecasts as "herding" or "bold" and finds that (1) boldness likelihood increases with the analyst's prior accuracy, brokerage size, and experience and declines with the number of industries the analyst follows, consistent with theory linking boldness with career concerns and ability; (2) bold forecasts are more accurate than herding forecasts; and (3) herding forecast revisions are more strongly associated with analysts' earnings forecast errors (actual earnings-forecast) than are bold forecast revisions. Thus, bold forecasts incorporate analysts' private information more completely and provide more relevant information to investors than herding forecasts. Copyright 2005 by The American Finance Association.
Conservatism is interpreted to mean that accountants more frequently report current "bad news" about future cash flows in contemporaneous earnings than current "good news." Thus, earnings reported under GAAP should be more timely in reporting "bad news" about future cash flows than "good news." This paper, using the firm's stock return as a measure of news, shows that the contemporaneous association between earnings and negative returns is two to five times as large as the contemporaneous association between earnings and positive returns. It is also shown that the greater timeliness of earnings relative to cash flow measures is largely due to a greater sensitivity to concurrent negative returns. This result is consistent with accountants recording accruals conservatively. Another implication of conservatism is that negative earnings surprises are likely to be less persistent than positive earnings surprises, because earnings reports more bad news concurrently than good news, with the latter being spread over several periods. This is shown to be true empirically. It is predicted and found that earnings response coefficients are higher for positive earnings changes than for negative earnings changes, which is consistent with the market correcting for the difference in persistence in conservatively determined earnings. It is also found that the sensitivity of earnings to negative returns has more than quadrupled since 1980, while the sensitivity of earnings to negative returns has declined by two-thirds, suggesting that earnings measurement has become more conservative. Increases in accounting conservatism are found to be correlated with increases in auditor liability, but no causal inferences are drawn.
Recent work by Diether, Malloy, and Scherbina (2002) has established a negative relationship between stock returns and the dispersion of analysts' earnings forecasts. I offer a simple explanation for this phenomenon based on the interpretation of dispersion as a proxy for unpriced information risk arising when asset values are unobservable. The relationship then follows from a general options-pricing result: For a levered firm, expected returns should always decrease with the level of idiosyncratic asset risk. This story is formalized with a straightforward model. Reasonable parameter values produce large effects, and the theory's main empirical prediction is supported in cross-sectional tests. Copyright 2004 by The American Finance Association.
We provide evidence that stocks with higher dispersion in analysts' earnings forecasts earn lower future returns than otherwise similar stocks. This effect is most pronounced in small stocks and stocks that have performed poorly over the past year. Interpreting dispersion in analysts' forecasts as a proxy for differences in opinion about a stock, we show that this evidence is consistent with the hypothesis that prices will reflect the optimistic view whenever investors with the lowest valuations do not trade. By contrast, our evidence is inconsistent with a view that dispersion in analysts' forecasts proxies for risk. Copyright The American Finance Association 2002.
This paper proposes and tests a quadratic-loss utility function for modeling corporate earnings forecasting, where financial analysts trade off bias to improve management access and forecast accuracy. Optimal forecasts with minimum expected error are optimistically biased and exhibit predictable cross-sectional variation related to analyst and company characteristics. Empirical evidence from individual analyst forecasts is consistent with the model's predictions. These results suggest that positive and predictable bias may be a rational property of optimal earnings forecasts. Prior studies using classical notions of unbiasedness may have prematurely dismissed analysts' forecasts as being irrational or inaccurate. Copyright The American Finance Association 2001.
This study examines whether security analysts underreact or overreact to prior earnings information, and whether any such behavior could explain previously documented anomalous stock price movements. We present evidence that analysts' forecasts underreact to recent earnings. This feature of the forecasts is consistent with certain properties of the naive seasonal random walk forecast that Bernard and Thomas (1990) hypothesize underlie the well‐known anomalous post‐earnings‐announcement drift. However, the underreactions in analysts' forecasts are at most only about half as large as necessary to explain the magnitude of the drift. We also document that the “extreme” analysts' forecasts studied by DeBondt and Thaler (1990) cannot be viewed as overreactions to earnings, and are not clearly linked to the stock price overreactions discussed in DeBondt and Thaler (1985, 1987) and Chopra, Lakonishok, and Ritter (Forthcoming). We conclude that security analysts' behavior is at best only a partial explanation for stock price underreaction to earnings, and may be unrelated to stock price overreactions.
The primary aim of the paper is to place current methodological discussions in macroeconometric modeling contrasting the ‘theory first’ versus the ‘data first’ perspectives in the context of a broader methodological framework with a view to constructively appraise them. In particular, the paper focuses on Colander’s argument in his paper “Economists, Incentives, Judgement, and the European CVAR Approach to Macroeconometrics” contrasting two different perspectives in Europe and the US that are currently dominating empirical macroeconometric modeling and delves deeper into their methodological/philosophical underpinnings. It is argued that the key to establishing a constructive dialogue between them is provided by a better understanding of the role of data in modern statistical inference, and how that relates to the centuries old issue of the realisticness of economic theories.
Security analysts' career concerns and herding of earnings forecasts
- Hong
Meeting individual analyst expectations
- Kirk
Do security analysts overreact?
- DeBondt