scholarly journals Earnings Predictability And Broker-Analysts’ Earnings Forecast Bias

2017 ◽  
Vol 33 (6) ◽  
pp. 1285-1302
Author(s):  
Michael Eames ◽  
Steven Glover
Keyword(s):  
Private Information ◽  
Alternative Model ◽  
Analyst Forecast ◽  
Earnings Forecast ◽  
Earnings Expectations ◽  
Forecast Bias ◽  
Analyst Expectations ◽  
Earnings Predictability ◽  
Using Data ◽  
Short Horizon

Scholars have reasoned that analysts issue optimistic forecasts to improve their access to managers’ private information when earnings are unpredictable. While this requires a managerial preference for analyst forecast optimism, the observed walk-down of analyst expectations to beatable forecasts is consistent with a managerial preference for pessimism in short-horizon forecasts. Using data from various sample periods, alternative model specifications, and various measures of earnings unpredictability, we find that pessimism, not optimism, in short-horizon forecasts is associated with increasingly unpredictable earnings. Our results suggest that firms can more effectively manage analysts’ earnings expectations downward when earnings are relatively unpredictable.

Meeting Individual Analyst Expectations

2014 ◽  
Vol 89 (6) ◽  
pp. 2203-2231 ◽  
Author(s):  
Marcus P. Kirk ◽  
David A. Reppenhagen ◽  
Jennifer Wu Tucker
Keyword(s):  
Private Information ◽  
Explanatory Power ◽  
Analyst Forecasts ◽  
Data Availability ◽  
Analyst Forecast ◽  
Analyst Expectations ◽  
Management Literature ◽  
Two Measures ◽  
Reported Earnings

ABSTRACT 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.

Identifying the News in Analysts’ Earnings Forecasts Revisions: An Alternative to the Random Walk Expectation

2021 ◽  
Author(s):  
Ray Pfeiffer ◽  
Karen Teitel ◽  
Susan Wahab ◽  
Mahmoud Wahab
Keyword(s):  
Random Walk ◽  
Stock Returns ◽  
Earnings Forecasts ◽  
Simple Random Walk ◽  
Analyst Forecast ◽  
Forecast Errors ◽  
Earnings Expectations ◽  
Forecast Revisions ◽  
Conditioning Information ◽  
Using Data

Previous research indicates that analysts’ forecasts are superior to time series models as measures of investors’ earnings expectations. Nevertheless, research also documents predictable patterns in analysts’ forecasts and forecast errors. If investors are aware of these patterns, analysts’ forecast revisions measured using the random walk expectation are an incomplete representation of changes in investors’ earnings expectations. Investors can use knowledge of errors and biases in forecasts to improve upon the simple random walk expectation by incorporating conditioning information. Using data from 2005 to 2015, we compare associations between market-adjusted stock returns and alternative specifications of forecast revisions to determine which best represents changes in investors’ earnings expectations. We find forecast revisions measured using a ‘bandwagon expectations’ specification, which includes two prior analysts’ forecast signals and provides the most improvement over random-walk-based revision measures. Our findings demonstrate benefits to considering information beyond the previously issued analyst forecast when representing investors’ expectations of analysts’ forecasts.

scholarly journals Are Analysts Overoptimistic about the Prospects of Sin Firms?

2017 ◽  
Vol 8 (4) ◽  
pp. 99 ◽  
Author(s):  
Jin Zhang ◽  
Haeyoung Shin
Keyword(s):  
Stock Price ◽  
Forecast Accuracy ◽  
Earnings Forecasts ◽  
Positive Association ◽  
Negative Association ◽  
Analyst Forecast ◽  
Earnings Forecast ◽  
Absolute Value ◽  
Forecast Bias ◽  
The Difference

We investigate the association between the bias and accuracy of consensus analysts’ earnings forecasts and whether a firm is a sin firm or not. We measure analyst forecast bias as the difference between the consensus earnings forecast and the actual earnings, scaled by the stock price. We measure analyst forecast accuracy as the negative of the absolute value of the difference between the firms’ forecasted and actual earnings, scaled by the stock price. We find a positive association between the level of forecast optimism and sin firm membership. We find a negative association between the level of forecast accuracy and sin firm membership. Overall, these results imply that analysts tend to issue over-optimistic and less accurate earnings forecasts on sin firms.

Earnings Predictability and the Direction of Analysts' Earnings Forecast Errors

2003 ◽  
Vol 78 (3) ◽  
pp. 707-724 ◽  
Author(s):  
Michael J. Eames ◽  
Steven M. Glover
Keyword(s):  
Private Information ◽  
Forecast Error ◽  
Control Variable ◽  
Earnings Forecasts ◽  
Earnings Forecast ◽  
Forecast Errors ◽  
Effective Mechanism ◽  
Earnings Predictability

Das et al. (1998) suggest that as earnings become less predictable, analysts issue increasingly optimistic forecasts to please managers and consequently gain, or at least limit the loss of, access to managers' private information. We reexamine the association between earnings forecast error and earnings predictability because there is evidence suggesting that deliberate earnings forecast optimism is not an effective mechanism for gaining access to managers' information (e.g., Eames et al. 2002; Matsumoto 2002). We document associations between earnings level and both forecast error and earnings predictability. These associations suggest that earnings level may be an important control variable when examining the association between forecast error and earnings predictability. When we control for the level of earnings we find no significant association between forecast error and earnings predictability. Thus, we find no evidence that analysts intentionally issue optimistically biased earnings forecasts.

The Effect of Trading Volume on Analysts’ Forecast Bias

2011 ◽  
Vol 86 (2) ◽  
pp. 451-481 ◽  
Author(s):  
Anne Beyer ◽  
Ilan Guttman
Keyword(s):  
Private Information ◽  
Trading Volume ◽  
Forecast Error ◽  
Bad News ◽  
Earnings Forecast ◽  
Risk Averse ◽  
Forecast Bias ◽  
Optimal Trading ◽  
Trading Decisions ◽  
Separating Equilibrium

ABSTRACT: This study models the interaction between a sell-side analyst and risk-averse investors. It derives an analyst’s optimal earnings forecast and investors’ optimal trading decisions in a setting where the analyst’s payoff depends on the trading volume the forecast generates as well as on the forecast error. In the fully separating equilibrium, we find that the analyst biases the forecast upward (downward) if his private signal reveals relatively good (bad) news. The model predicts that: (1) the analyst biases the forecast upward more often than downward and the forecast is on average optimistic; (2) the magnitude of the analyst’s bias is increasing in the per-share benefit from trading volume he receives; and (3) the analyst’s expected squared forecast error may increase in the precision of his private information. Finally, we characterize the circumstances under which the (rational) analyst acts as if he overweights or underweights his private information.

Institutional Dual-Holders and Managers' Earnings Disclosure

2021 ◽  
Author(s):  
Leila Peyravan ◽  
Regina Wittenberg-Moerman
Keyword(s):  
Adverse Effect ◽  
Commercial Bank ◽  
Private Information ◽  
Voluntary Disclosure ◽  
Information Environment ◽  
Earnings Forecast ◽  
Excess Returns ◽  
Lending Relationships ◽  
Playing Field ◽  
Information Advantage

We investigate how institutional (non-commercial bank) investors that simultaneously invest in a firm's debt and equity (dual-holders) influence the firm's voluntary disclosure. Because institutional dual-holders trade on private information gleaned through lending relationships, we predict and find that borrowers increase earnings forecast disclosure to reduce these investors' information advantage following the origination of loans with their participation. We also show that the increase in disclosure is stronger when the access to a borrower's private information endows dual-holders with a greater information advantage and when the consequences of this access are likely to be more pronounced. We further find that institutional dual-holders earn excess returns when trading equity of non-guider firms following loan origination, but not when firms issue guidance, confirming that earnings disclosure helps level the playing field among investors. Our findings highlight that firms actively use disclosure to mitigate the adverse effect of dual-holders on their information environment.

Discussion: “Dispersion in Analysts' Earnings Forecasts as a Measure of Uncertainty”

1998 ◽  
Vol 13 (3) ◽  
pp. 271-274 ◽  
Author(s):  
Lawrence D. Brown
Keyword(s):  
Stock Price ◽  
Annual Report ◽  
Earnings Forecasts ◽  
Analyst Forecast ◽  
Earnings Forecast ◽  
Data Set ◽  
Forecast Dispersion ◽  
Forecast Revision ◽  
Analyst Earnings ◽  
The Impact

This paper tackles an interesting question; namely, whether dispersion in analysts' earnings forecasts reflects uncertainty about firms' future economic performance. It improves on the extant literature in three ways. First, it uses detailed analyst earnings forecast data to estimate analyst forecast dispersion and revision. The contrasting evidence of Morse, Stephan, and Stice (1991) and Brown and Han (1992), who respectively used consensus and detailed analyst data to examine the impact of earnings announcements on forecast dispersion, suggest that detailed data are preferable for determining the data set on which analysts' forecasts are conditioned. Second, it relates forecast dispersion to both analyst earnings forecast revision and stock price reaction to the subsequent earnings announcement. Previous studies related forecast dispersion to either analyst forecast revision (e.g., Stickel 1989) or to subsequent stock price movements (e.g., Daley et al. [1988]), but not to both revision and returns. Third, it includes the interim quarters along with the annual report. In contrast, previous research focused on the annual report, ignoring the interims (Daley et al. [1988]).

scholarly journals When Private Information Settles the Bill: Money and Privacy in Google’s Market for Smartphone Applications

2019 ◽  
Vol 65 (8) ◽  
pp. 3470-3494 ◽  
Author(s):  
Michael Kummer ◽  
Patrick Schulte
Keyword(s):  
Private Information ◽  
Personal Information ◽  
Supply And Demand ◽  
User Group ◽  
Smartphone Applications ◽  
Difference In Differences ◽  
Android Market ◽  
Using Data ◽  
Google Play ◽  
Shed Light

We shed light on a money-for-privacy trade-off in the market for smartphone applications (“apps”). Developers offer their apps at lower prices in return for greater access to personal information, and consumers choose between low prices and more privacy. We provide evidence for this pattern using data from 300,000 apps obtained from the Google Play Store (formerly Android Market) in 2012 and 2014. Our findings show that the market’s supply and demand sides both consider an app’s ability to collect private information, measured by the apps’s use of privacy-sensitive permissions: (1) cheaper apps use more privacy-sensitive permissions; (2) given price and functionality, demand is lower for apps with sensitive permissions; and (3) the strength of this relationship depends on contextual factors, such as the targeted user group, the app’s previous success, and its category. Our results are robust and consistent across several robustness checks, including the use of panel data, a difference-in-differences analysis, “twin” pairs of apps, and various measures of privacy-sensitivity and app demand. This paper was accepted by Anandhi Bharadwaj, information systems.

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