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

Ray Pfeiffer (), Karen Teitel (), Susan Wahab and Mahmoud Wahab ()
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Ray Pfeiffer: Simmons University, 300 Fenway, Boston, MA 02115, USA
Karen Teitel: College of the Holy Cross, 1 College St, Worcester, MA 01610, USA
Susan Wahab: University of Hartford, 200 Bloomfield Address, West Hartfort, CT 06117, USA
Mahmoud Wahab: University of Hartford, 200 Bloomfield Address, West Hartfort, CT 06117, USA

Review of Pacific Basin Financial Markets and Policies (RPBFMP), 2021, vol. 24, issue 04, 1-42

Abstract: 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.

Keywords: Analysts’ earnings forecasts; forecast errors; time series models; forecast revisions; random walk; bandwagon expectations (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0219091521500326

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