Operating Exposure to Weather, Earnings Predictability, and Analyst Forecast

51 Pages Posted: 23 Jun 2021

See all articles by Lei Zhang

Lei Zhang

City University of Hong Kong

Date Written: June 11, 2021

Abstract

This study quantifies firm-specific operating exposure to cumulative unexpected weather variations and examines how it affects earnings predictability and analysts’ forecasts. Two competing hypotheses are tested. The reduction in earnings seasonality hypothesis posits that operating weather exposure reduces earnings seasonality, thereby increasing forecast dispersion and reducing forecast accuracy. The increase in short-term earnings persistence hypothesis posits that operating weather exposure makes short-term earnings more persistent, leading to lower forecast dispersion and higher accuracy. The results provide strong evidence that firms with higher operating weather exposure display lower earnings seasonality but higher short-term earnings persistence. The net effect is that analysts’ forecasts become significantly noisier with more dispersion and lower accuracy. These results are stronger for industries with higher seasonality and for regions experiencing extreme weather conditions. Further analysis shows that firms’ profit margin and asset turnover exposures to abnormal precipitation and temperature variations contribute to the overall weather effects.

Keywords: operating weather exposure, earnings seasonality, short-term earnings persistence, analyst forecast dispersion, analyst forecast accuracy

JEL Classification: G17, G24, Q54

Suggested Citation

Zhang, Lei, Operating Exposure to Weather, Earnings Predictability, and Analyst Forecast (June 11, 2021). Available at SSRN: https://ssrn.com/abstract=3865166 or http://dx.doi.org/10.2139/ssrn.3865166

Lei Zhang (Contact Author)

City University of Hong Kong ( email )

College of Business
83 Tat Chee Avenue
Hong Kong
China

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