Operating Exposure to Weather, Earnings Predictability, and Analyst Forecast

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, which may lead 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 more pronounced for industries with higher seasonality and for regions experiencing relatively 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.