How Do Natural Disasters Impede Corporate Earnings Management?

41 Pages Posted: 7 Oct 2020 Last revised: 18 Dec 2022

See all articles by Kai Wu

Kai Wu

Central University of Finance and Economics (CUFE) - School of Finance

Huiming Zhang

Nanjing University of Information Science & Technology (NUIST) - School of Environmental Science and Engineering

Shouyang Wang

Chinese Academy of Sciences (CAS) - Center for Forecasting Science; Academy of Mathematics and Systems Sciences

Yueming Qiu

School of Public Policy, University of Maryland at College Park

Mark S. Seasholes

Arizona State University (ASU)

Date Written: December 12, 2022

Abstract

Natural disasters can cause heavy economic losses to firms, potentially propelling these firms to use earnings management to change public perceptions of the damage. However, no academic consensus has been reached on how and when firms manage earnings following external shocks. In this study, we use difference-in-difference regressions for a sample of publicly listed U.S. firms from 1980 to 2017 to investigate the effects of natural disasters on the timing and magnitude of earnings management. We find that natural disasters reduce the magnitude of corporate earnings management by 14.9% in the two years following such disasters. The negative effect is mediated through the information disclosure channel. Natural disasters have a more pronounced effect on firms in less competitive markets and firms with less tangible assets. Our findings of reduced earnings management following negative external shocks are contradictory to "big bath'' accounting practices but are consistent with the rational expectations of executives and the external monitoring of stakeholders.

Keywords: natural disasters, earnings management, external monitoring

JEL Classification: G10, G14, Q54

Suggested Citation

Wu, Kai and Zhang, Huiming and Wang, Shouyang and Qiu, Yueming (Lucy) and Seasholes, Mark S., How Do Natural Disasters Impede Corporate Earnings Management? (December 12, 2022). Available at SSRN: https://ssrn.com/abstract=3676848 or http://dx.doi.org/10.2139/ssrn.3676848

Kai Wu

Central University of Finance and Economics (CUFE) - School of Finance ( email )

Beijing
China

Huiming Zhang

Nanjing University of Information Science & Technology (NUIST) - School of Environmental Science and Engineering ( email )

China

Shouyang Wang

Chinese Academy of Sciences (CAS) - Center for Forecasting Science; Academy of Mathematics and Systems Sciences ( email )

China

Yueming (Lucy) Qiu

School of Public Policy, University of Maryland at College Park ( email )

Mark S. Seasholes (Contact Author)

Arizona State University (ASU) ( email )

Farmer Building 440G PO Box 872011
Tempe, AZ 85287
United States

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