Analyst Forecast Revision Consistency and Bias in Earnings Forecast Revisions

2021 ◽  
Author(s):  
Mary E. Barth ◽  
Wayne R. Landsman ◽  
Vivek Raval ◽  
Sean Wang
Keyword(s):  
Analyst Forecast ◽  
Earnings Forecast ◽  
Forecast Revisions ◽  
Forecast Revision

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 Returns to buying upward revision and selling downward revision stocks

2016 ◽  
Vol 42 (11) ◽  
pp. 1110-1124
Author(s):  
Tony Chieh-Tse Hou ◽  
Phillip McKnight ◽  
Charlie Weir
Keyword(s):  
Stock Returns ◽  
Stock Prices ◽  
Information Diffusion ◽  
Trading Strategy ◽  
Good News ◽  
Analyst Forecast ◽  
Earnings Forecast ◽  
Content Type ◽  
Forecast Revisions ◽  
New Information

Purpose The purpose of this paper is to investigate the role of earnings forecast revisions by equity analysts in predicting Canadian stock returns Design/methodology/approach The sample covers 420 Canadian firms over the period 1998-2009. It analyses investors’ reactions to 27,271 upward revisions and 32,005 downward revisions of analysts’ forecasts for Canadian quoted companies. To test whether analysts’ earnings forecast revisions affect stock return continuation, forecast revision portfolios similar to Jegadeesh and Titman (2001) are constructed. The paper analyses the returns gained from a trading strategy based on buying the strong upward revisions portfolio and short selling the strong downward revisions portfolio. It also separates the sample into upward and downward revisions. Findings The authors find that new information in the form of analyst forecast revisions is not impounded efficiently into stock prices. Significant returns persist for a trading strategy that buys stocks with recent upward revisions and short sells stocks with recent downward revisions. Good news is impounded into stock prices more slowly than bad news. Post-earnings forecast revisions drift is negatively related to analyst coverage. The effect is strongest for stocks with greatest number of upward revisions. The introduction of the better disclosure standards has made the Canadian stock market more efficient. Originality/value The paper adds to the limited evidence on the effect of analyst forecast revisions on the returns of Canadian stocks. It sheds light on the importance of analysts’ earnings forecast information and offers support for the investor conservatism and information diffusion hypotheses. It also shows how policy can improve market efficiency.

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.

How Completely Do Analysts Incorporate Firm-Specific and Industry Information in Their Forecasts? Evidence and Implications for Post-Forecast Revision Drift

2021 ◽  
pp. 0148558X2110437
Author(s):  
Sami Keskek ◽  
Senyo Tse
Keyword(s):  
Analyst Forecast ◽  
Positive Relation ◽  
Forecast Revisions ◽  
Forecast Revision ◽  
Available Information

Prior studies find a positive relation between analyst forecast revisions and upcoming news, suggesting that analysts’ forecast revisions are incomplete with respect to available information. In this study, we use the association between forecast revisions and upcoming news to measure forecast completeness and show that post-forecast-revision drift is higher when forecasts are incomplete. We follow Hui and Yeung’s (2013) approach to separate forecast revision news into industry-wide and firm-specific components because they find that drift is primarily associated with the industry component. We find that forecast revisions are less complete for industry-wide news than for firm-specific news. Furthermore, analysts’ industry-wide revisions are less complete early in the year and when the underlying news is bad, and we find stronger post-forecast-revision drift in those cases. We also show that analysts who were optimistic in prior periods tend to issue forecasts that are less complete and that generate stronger drift than forecasts by other analysts. Our findings provide an explanation for the drift that contrasts with prior studies that attribute the drift to investors’ slow assimilation of the news in forecast revisions. Thus, our study sheds light on analysts’ role in conveying firm-specific and industry-wide news to investors and on the implications for post-forecast-revision drift.

Information Technology Capability, Management Forecast Accuracy, and Analyst Forecast Revisions

2018 ◽  
Vol 32 (3) ◽  
pp. 49-70 ◽  
Author(s):  
Feiqi Huang ◽  
He Li ◽  
Tawei Wang
Keyword(s):  
Forecast Accuracy ◽  
Analyst Forecast ◽  
Management Forecasts ◽  
Forecast Revisions ◽  
It Capability ◽  
Management Forecast ◽  
Prior Literature ◽  
Technology Capability ◽  
Information Technology Capability ◽  
The Relationship

SYNOPSISPrior literature has firmly established the relationship between IT capability and firm performance. In this paper, we extend the research in this field and investigate (1) whether IT capability contributes to management forecast accuracy, and (2) whether IT capability improves the informativeness of management forecasts and enhances the extent to which analysts incorporate management forecasts in their revisions. Using firms listed on InformationWeek 500 as our high IT capability group, we empirically demonstrate that firms with high IT capability are able to increase management forecast accuracy, and that analysts incorporate more information from management forecasts in their revisions if the firm has high IT capability.

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