Whisper Forecasts of Quarterly Earnings per Share

In this study we examine different methodologies to estimate earnings. More specifically, we evaluate the viability of Genetic Programming as both a forecasting model estimator and a forecast-combining methodology. When we compare the performance of traditional mechanical forecasting (ARIMA) models and models developed using Genetic Programming we observe that Genetic Programming can be used to create time-series models for quarterly earnings as accurate as the traditional linear models. Genetic Programming can also effectively combine forecasts. However, Genetic Programming's forecast combinations are sometimes unable to improve on Value Line. Moreover, simple averaging of forecasts results in better predictive accuracy than Genetic Programming-combining of forecasts. Hence, as implemented in this study, Genetic Programming is not superior to traditional methodologies in either forecasting or forecast combining of quarterly earnings.

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The predictive power of quarterly earnings per share based on time series and artificial intelligence model

Applied Financial Economics ◽

10.1080/09603100600592752 ◽

2006 ◽

Vol 16 (18) ◽

pp. 1375-1388

Author(s):

Syouching Lai ◽

Hungchih Li

Keyword(s):

Artificial Intelligence ◽

Time Series ◽

Predictive Power ◽

Quarterly Earnings ◽

Earnings Per Share

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The Effect of Reporting Streaks on Ex Ante Uncertainty

Management Science ◽

10.1287/mnsc.2019.3320 ◽

2020 ◽

Vol 66 (8) ◽

pp. 3771-3787 ◽

Cited By ~ 1

Author(s):

Thaddeus Neururer ◽

George Papadakis ◽

Edward J. Riedl

Keyword(s):

Stock Returns ◽

Earnings Announcements ◽

Earnings Announcement ◽

Risk Premiums ◽

Quarterly Earnings ◽

Earnings Per Share ◽

Ex Ante ◽

Variance Risk ◽

Analyst Earnings ◽

Lower Uncertainty

This paper predicts and finds that investor uncertainty surrounding a key information release event—the earnings announcement—is decreasing in a firm’s reporting streak. We use two proxies related to investor ex ante uncertainty and corresponding pricing of such uncertainty: option-implied volatilities and variance risk premiums; both are measured with maturities surrounding the impending quarterly earnings announcement. Consistent with prior research, we measure reporting streak as the number of consecutive quarters the firm meets or beats the consensus analyst earnings-per-share forecast. Empirical results confirm expectations that the two uncertainty-related constructs are decreasing in the length of the reporting streak. These results, combined with further evidence documenting that lower uncertainty leads to lower stock returns surrounding the earnings announcements, suggest that longer reporting streaks reflect lower risk during earnings announcements. This paper was accepted by Shiva Rajgopal, accounting.

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Nonlinearities And Volatility Patterns In Corporate Earnings

Journal of Applied Business Research (JABR) ◽

10.19030/jabr.v15i2.5681 ◽

2011 ◽

Vol 15 (2) ◽

pp. 79

Author(s):

Nikiforos T. Laopodis

Keyword(s):

Random Walk ◽

Risk Premium ◽

Conditional Variance ◽

Quarterly Earnings ◽

Conditional Mean ◽

Random Walk Hypothesis ◽

Volatility Persistence ◽

Earnings Per Share ◽

Corporate Earnings ◽

Stochastic Properties

<span>This paper explores the stochastic properties of the quarterly earnings per share series for industrials, railroads and utilities since 1935. Evidence of stochastic dependency suggests modeling them as a conditionally heteroskedastic process. Changes in earnings tentatively reject the random walk hypothesis since future returns can be predicted from past information. The conditional variance is found to be sensitive to market advances and positively correlated with the conditional mean since the risk premium is statistically significant. Finally, volatility persistence appears to be high in all series.</span>

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Analyst Conflicts and Research Quality

Quarterly Journal of Finance ◽

10.1142/s2010139212500103 ◽

2012 ◽

Vol 02 (02) ◽

pp. 1250010 ◽

Cited By ~ 12

Author(s):

Anup Agrawal ◽

Mark A. Chen

Keyword(s):

Conflicts Of Interest ◽

Earnings Forecasts ◽

Research Quality ◽

Firm Resources ◽

Forecast Revisions ◽

Quarterly Earnings ◽

Earnings Per Share ◽

Stock Analysts

This paper examines whether the quality of stock analysts' forecasts is related to conflicts of interest from their employers' investment banking (IB) and brokerage businesses. We consider four aspects of forecast quality: accuracy, bias, and revision frequency of quarterly earnings per share (EPS) forecasts and relative optimism in long-term earnings growth (LTG) forecasts. Using a unique dataset that contains the annual revenue breakdown of analysts' employers among IB, brokerage, and other businesses, we uncover two main findings. First, accuracy and bias in quarterly EPS forecasts appear to be unrelated to conflict magnitudes after controlling for forecast age, firm resources, and analyst characteristics. Second, relative optimism in LTG forecasts and the revision frequency of quarterly EPS forecasts are positively related to the importance of brokerage business to analysts' employers. Additional tests suggest that the frequency of quarterly forecast revisions is positively related to analysts' trade generation incentives. Our findings suggest that reputation concerns keep analysts honest with respect to short-term earnings forecasts but not LTG forecasts. In addition, conflicts from brokerage appear to play a more important role in shaping analysts' forecasting behavior than has been previously recognized.

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