The Association between the Magnitude of Quarterly Earnings Forecast Errors and Risk-Adjusted Stock Returns

1984 ◽  
Vol 22 (2) ◽  
pp. 526 ◽  
Author(s):  
Robert L. Hagerman ◽  
Mark E. Zmijewski ◽  
Pravin Shah
Keyword(s):  
Stock Returns ◽  
Earnings Forecast ◽  
Forecast Errors ◽  
Quarterly Earnings

Predictability of Analyst Earnings Forecast Errors and Institutional and Individual Investors’ Reactions to Earnings News

2020 ◽  
pp. 0148558X2093933
Author(s):  
Nilhabra Bhattacharya ◽  
Per Olsson ◽  
Hyungshin Park
Keyword(s):  
Institutional Investors ◽  
Forecast Error ◽  
Individual Investors ◽  
Earnings Forecast ◽  
Forecast Errors ◽  
Financial Reports ◽  
Earnings Announcement ◽  
Quarterly Earnings ◽  
Earnings Information ◽  
Analyst Earnings

We decompose analysts’ earnings forecast error into predictable and unpredictable components and investigate individual vis-à-vis institutional investors’ reactions to each of these components. We find that in the immediate post-earnings announcement window, only individuals under-react to the predictable component, while both individuals and institutions under-react to the unpredictable component. The price drift in this window is driven primarily by investors’ under-reaction to the unpredictable component. This drift remains highly significant in larger firms and intensifies in firms with complex financial reports, suggesting that it likely represents the slow and noisy process of price discovery. Around the next quarterly earnings announcement, only individuals under-react to the previous quarter’s predictable component, and this fixation drives the entire price drift in this window. This drift disappears in larger firms and gets exacerbated in firms with greater forecast error autocorrelations, suggesting that it is likely attributable to incomplete processing of earnings information by individuals.

The Information Content of Quarterly Dividend Changes

1995 ◽  
Vol 10 (2) ◽  
pp. 293-317 ◽  
Author(s):  
Thomas J. Carroll
Keyword(s):  
Stock Returns ◽  
Earnings Forecasts ◽  
Positive Association ◽  
Earnings Forecast ◽  
Forecast Errors ◽  
Cross Sectional ◽  
Future Earnings ◽  
New Information ◽  
Dividend Announcements ◽  
Dividend Increases

This paper shows that dividend changes reveal new information about future earnings levels and are mixed with regard to future earnings variance. Revisions of Value Line earnings forecasts spanning up to five quarters have a positive association with unexpected dividend changes. Consistent with the negative association documented between dividend changes and future earnings variance, these revisions also exhibit greater cross-sectional dispersion following dividend decreases than following dividend increases. The relation between stock returns and earnings forecast errors following dividend announcements shows that dividend announcements convey information to the market about earnings in the next quarter and the quarter one year hence, but are not consistent with dividends revealing new information about the variance of future earnings.

Predicting Earnings Using a Model Based on Cost Variability and Cost Stickiness

2006 ◽  
Vol 81 (2) ◽  
pp. 285-307 ◽  
Author(s):  
Rajiv D. Banker ◽  
Lei (Tony) Chen
Keyword(s):  
Stock Returns ◽  
Forecast Accuracy ◽  
Predictive Ability ◽  
Forecast Model ◽  
Cash Flows ◽  
Substantial Improvement ◽  
Earnings Forecast ◽  
Forecast Errors ◽  
Operating Income ◽  
Consensus Forecasts

We evaluate the descriptive validity of the cost behavior model for profit analysis using Compustat data. For this purpose, we propose an earnings forecast model decomposing earnings into components that reflect (1) variability of costs with sales revenue and (2) stickiness in costs with sales declines. We evaluate the predictive ability of our model by benchmarking its performance in forecasting one-year-ahead returns on equity against that of two other time-series models based on line item information reported in the income statement and in the statement of cash flows. Specifically, we consider a model that disaggregates earnings into operating income and non-operating income components and another that disaggregates earnings into cash flows and accruals components. While all three models are less accurate than analysts' consensus forecasts that rely on a larger information set, we find that our model provides substantial improvement in forecast accuracy over the other two models that use only the line items in the financial statements. Finally, invoking the market efficiency assumption, we find that earnings forecast errors based on our model have greater relative information content than forecast errors based on the two alternative models based on financial statement information in explaining abnormal stock returns.

Refining financial analysts’ forecasts by predicting earnings forecast errors

2017 ◽  
Vol 25 (2) ◽  
pp. 256-272 ◽  
Author(s):  
Tatiana Fedyk
Keyword(s):  
Serial Correlation ◽  
Design Methodology ◽  
Financial Analysts ◽  
Earnings Forecasts ◽  
Autoregressive Process ◽  
Earnings Forecast ◽  
Forecast Errors ◽  
Content Type ◽  
Quarterly Earnings ◽  
Prior Literature

Purpose The purpose of this paper is to examine the way serial correlation in quarterly earnings forecast errors varies with firm and analyst attributes such as the firm’s industry and the analyst’s experience and brokerage house affiliation. Prior research on financial analysts’ quarterly earnings forecasts has documented serial correlation in forecast errors. Design/methodology/approach Finding that serial correlation in forecast errors is significant and seemingly independent of firm and analyst attributes, the consensus forecast errors are modeled as an autoregressive process. The model of forecast errors that best fits the data is AR(1), and the obtained autoregressive coefficients are used to predict consensus forecast errors. Findings Modeling the consensus forecast errors as an autoregressive process, the present study predicts future consensus forecast errors and proposes a series of refinements to the consensus. Originality/value These refinements were not presented in prior literature and can be useful to financial analysts and investors.

Can Twitter Help Predict Firm-Level Earnings and Stock Returns?

2017 ◽  
Vol 93 (3) ◽  
pp. 25-57 ◽  
Author(s):  
Eli Bartov ◽  
Lucile Faurel ◽  
Partha S. Mohanram
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Future Prospects ◽  
Traditional Media ◽  
Stock Trading ◽  
Firm Level ◽  
Quarterly Earnings ◽  
Announcement Returns ◽  
Twitter Activity ◽  
Original Information

ABSTRACT Prior research has examined how companies exploit Twitter in communicating with investors, and whether Twitter activity predicts the stock market as a whole. We test whether opinions of individuals tweeted just prior to a firm's earnings announcement predict its earnings and announcement returns. Using a broad sample from 2009 to 2012, we find that the aggregate opinion from individual tweets successfully predicts a firm's forthcoming quarterly earnings and announcement returns. These results hold for tweets that convey original information, as well as tweets that disseminate existing information, and are stronger for tweets providing information directly related to firm fundamentals and stock trading. Importantly, our results hold even after controlling for concurrent information or opinion from traditional media sources, and are stronger for firms in weaker information environments. Our findings highlight the importance of considering the aggregate opinion from individual tweets when assessing a stock's future prospects and value.

The Association between Unsystematic Security Returns and the Magnitude of Earnings Forecast Errors

1979 ◽  
Vol 17 (2) ◽  
pp. 316 ◽  
Author(s):  
William H. Beaver ◽  
Roger Clarke ◽  
William F. Wright
Keyword(s):  
Earnings Forecast ◽  
Forecast Errors ◽  
Security Returns
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