scholarly journals The Role of Anchoring Bias in the Equity Market: Evidence from Analysts’ Earnings Forecasts and Stock Returns

2012 ◽  
Vol 48 (1) ◽  
pp. 47-76 ◽  
Author(s):  
Ling Cen ◽  
Gilles Hilary ◽  
K. C. John Wei
Keyword(s):  
Stock Returns ◽  
Earnings Forecasts ◽  
Equity Market ◽  
Stock Splits ◽  
Forecast Errors ◽  
Forecast Revisions ◽  
Earnings Per Share ◽  
Earnings Surprises ◽  
Anchoring Bias

AbstractWe test the implications of anchoring bias associated with forecast earnings per share (FEPS) for forecast errors, earnings surprises, stock returns, and stock splits. We find that analysts make optimistic (pessimistic) forecasts when a firm’s FEPS is lower (higher) than the industry median. Further, firms with FEPS greater (lower) than the industry median experience abnormally high (low) future stock returns, particularly around subsequent earnings announcement dates. These firms are also more likely to engage in stock splits. Finally, split firms experience more positive forecast revisions, more negative forecast errors, and more negative earnings surprises after stock splits.

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.

Earnings Surprises that Motivate Analysts to Reduce Average Forecast Error

2008 ◽  
Vol 83 (2) ◽  
pp. 303-325 ◽  
Author(s):  
Orie E. Barron ◽  
Donal Byard ◽  
Yong Yu
Keyword(s):  
Private Information ◽  
Forecast Error ◽  
Earnings Forecasts ◽  
Forecast Errors ◽  
Future Earnings ◽  
Earnings Surprises ◽  
The Mean ◽  
Negative Earnings

Large earnings surprises and negative earnings surprises represent more egregious errors in analysts' earnings forecasts. We find evidence consistent with our expectation that egregious forecast errors motivate analysts to work harder to develop or acquire relatively more private information in an effort to avoid future forecasting failures. Specifically, we find that after large or negative earnings surprises there is a greater reduction in the error in individual analysts' forecasts of future earnings, and these individual forecasts are based more heavily on individual analysts' private information. This increased reliance on private information reduces the error in the mean forecast of upcoming earnings (even after controlling for the effect of reduced error in individual forecasts). As reliance on private information increases, more of each individual forecast error is idiosyncratic, and thus averaged out in the computation of the mean forecast.

The Role of Anchoring Bias in the Equity Market

2010 ◽  
Author(s):  
Ling Cen ◽  
Gilles Hilary ◽  
K. C. John Wei ◽  
Jie Zhang
Keyword(s):  
Equity Market ◽  
Anchoring Bias

Herding on Earnings News: The Role of Institutional Investors in Post–Earnings-Announcement Drift

2017 ◽  
Vol 32 (4) ◽  
pp. 536-560 ◽  
Author(s):  
Linda H. Chen ◽  
Wei Huang ◽  
George J. Jiang
Keyword(s):  
Stock Returns ◽  
Institutional Investors ◽  
Stock Prices ◽  
Institutional Trading ◽  
Earnings Announcement ◽  
Post Earnings Announcement Drift ◽  
Gradual Process ◽  
Earnings Surprises ◽  
Institutional Herding

We examine the role of institutional investors underlying post–earnings-announcement drift (PEAD). Our results show that while institutional investors generally herd on earnings news, such correlated trading among institutions does not eliminate or reduce market underreaction to earnings surprises. Instead, PEAD is significant only in the subsample of stocks where institutions herd in the same direction as earnings surprises. In fact, institutional herding is also positively related to next-quarter earnings announcement returns. We provide evidence that institutional herding on or against earnings news is largely driven by firm characteristics, particularly past firm performance and stock returns. In addition, we find that relative to nontransient institutions, transient institutions have a stronger tendency to herd on earnings information. Finally, based on long-run stock returns, we show that when institutions herd on earnings surprises, institutional trading represents a gradual process of incorporating information into stock prices. However, when institutions herd against earnings surprises, institutional trading slows down stock price discovery.

Tax-Motivated Loss Shifting

2013 ◽  
Vol 88 (5) ◽  
pp. 1657-1682 ◽  
Author(s):  
Merle M. Erickson ◽  
Shane M. Heitzman ◽  
X. Frank Zhang
Keyword(s):  
Earnings Forecasts ◽  
Data Availability ◽  
Analyst Forecast ◽  
Forecast Errors ◽  
Corporate Reporting ◽  
Earnings Surprises ◽  
Reporting Decisions ◽  
Tax Payments ◽  
Tax Loss ◽  
Do So

ABSTRACT: This paper examines the implications of tax loss carryback incentives for corporate reporting decisions and capital market behavior. During the 1981 through 2010 sample period, we find that firms increase losses in order to claim a cash refund of recent tax payments before the option to do so expires, and we estimate that firms with tax refund-based incentives accelerate about $64.7 billion in losses. Tax-motivated loss shifting is reflected in both recurring and nonrecurring items and is more evident for financially constrained firms. Analysts do not generally incorporate tax-motivated loss shifting into their earnings forecasts, resulting in more negative analyst forecast errors for firms with tax-based incentives than for firms without. Holding earnings surprises constant, however, investors react less negatively to losses reported by firms with tax loss carryback incentives. Data Availability: Data are available from sources identified in the paper.

scholarly journals The Role of Analysts in Intra-Industry Information Transfer

2013 ◽  
Vol 88 (4) ◽  
pp. 1265-1287 ◽  
Author(s):  
Gilles Hilary ◽  
Rui Shen
Keyword(s):  
Information Transfer ◽  
Earnings Forecasts ◽  
Data Availability ◽  
Forecast Revisions ◽  
Management Forecast

ABSTRACT When a firm issues a management forecast, analysts who have observed more forecasts from this firm since covering it (i.e., have more MF-Experience) subsequently improve their own accuracy more and provide timelier earnings forecasts for other (non-issuing) firms in the same industry. We also find that, subsequent to a management forecast, investors are more responsive to forecast revisions for non-issuing firms made by analysts with more MF-Experience. Further tests suggest that our results are not explained by endogeneity in firm coverage. Data Availability: Data are commercially available.

scholarly journals The Earnings Expectations Game and the Dispersion Anomaly

2021 ◽  
Author(s):  
David Veenman ◽  
Patrick Verwijmeren
Keyword(s):  
Stock Returns ◽  
Return Predictability ◽  
Analyst Forecast ◽  
Forecast Dispersion ◽  
Earnings Expectations ◽  
Negative Relation ◽  
Earnings Surprises ◽  
Ex Ante ◽  
New Evidence

This study examines the role of differences in firms’ propensity to meet earnings expectations in explaining why firms with high analyst forecast dispersion experience relatively low future stock returns. We first demonstrate that the negative relation between dispersion and returns is concentrated around earnings announcements. Next, we show that this relation disappears when we control for ex ante measures of firms’ propensity to meet earnings expectations and that the component of dispersion explained by these measures drives the return predictability of dispersion. We further demonstrate that firms with low analyst dispersion are substantially more likely to achieve positive earnings surprises and provide new evidence consistent with both expectations management and strategic forecast pessimism explaining this result. Overall, we conclude that investor mispricing of firms’ participation in the earnings-expectations game provides a viable explanation for the dispersion anomaly. Accepted by Brian Bushee, accounting.

I/B/E/S Reported Actual EPS and Analysts' Inferred Actual EPS

2012 ◽  
Vol 88 (3) ◽  
pp. 853-880 ◽  
Author(s):  
Lawrence D. Brown ◽  
Stephannie Larocque
Keyword(s):  
Majority Rule ◽  
Forecast Accuracy ◽  
Earnings Forecasts ◽  
Analyst Forecast ◽  
Earnings Forecast ◽  
Sample Period ◽  
Forecast Dispersion ◽  
Forecast Revisions ◽  
Earnings Surprises ◽  
Analyst Earnings

ABSTRACT Users of I/B/E/S data generally act as if I/B/E/S reported actual earnings represent the earnings analysts were forecasting when they issued their earnings estimates. For example, when assessing analyst forecast accuracy, users of I/B/E/S data compare analysts' forecasts of EPS with I/B/E/S reported actual EPS. I/B/E/S states that it calculates actuals using a “majority rule,” indicating that its actuals often do not represent the earnings that all individual analysts were forecasting. We introduce a method for measuring analyst inferred actuals, and we assess how often I/B/E/S actuals do not represent analyst inferred actuals. We find that I/B/E/S reported Q1 actual EPS differs from analyst inferred actual Q1 EPS by at least one penny 39 percent of the time during our sample period, 36.5 percent of the time when only one analyst follows the firm (hence, this consensus forecast is based on the “majority rule”), and 50 percent of the time during the last three years of our sample period. We document two adverse consequences of this phenomenon. First, studies failing to recognize that I/B/E/S EPS actuals often differ from analyst inferred actuals are likely to obtain less accurate analyst earnings forecasts, smaller analyst earnings forecast revisions conditional on earnings surprises, greater analyst forecast dispersion, and smaller market reaction to earnings surprises than do studies adjusting for these differences. Second, studies failing to recognize that I/B/E/S EPS actuals often differ from analyst inferred actuals may make erroneous inferences.

Do Investors Fully Unravel Persistent Pessimism in Analysts' Earnings Forecasts?

2017 ◽  
Vol 93 (3) ◽  
pp. 349-377 ◽  
Author(s):  
David Veenman ◽  
Patrick Verwijmeren
Keyword(s):  
Stock Returns ◽  
Earnings Forecasts ◽  
Analyst Forecast ◽  
Short Term ◽  
Market Prices ◽  
Earnings Expectations ◽  
Earnings Surprises ◽  
Announcement Returns ◽  
Low Probability ◽  
Jel Classifications

ABSTRACT This study presents evidence suggesting that investors do not fully unravel predictable pessimism in sell-side analysts' earnings forecasts. We show that measures of prior consensus and individual analyst forecast pessimism are predictive of both the sign of firms' earnings surprises and the stock returns around earnings announcements. That is, we find that firms with a relatively high probability of forecast pessimism experience significantly higher announcement returns than those with a low probability. Importantly, we show that these findings are driven by predictable pessimism in analysts' short-term forecasts, as opposed to optimism in their longer-term forecasts. We further find that this mispricing is related to the difficulty investors have in identifying differences in expected forecast pessimism. Overall, we conclude that market prices do not fully reflect the conditional probability that a firm meets or beats earnings expectations as a result of analysts' pessimistically biased short-term forecasts. JEL Classifications: G12; G14; G20.

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