Natural Disaster Risk and Firm Performance: Text Mining and Machine Learning Approach

Abstract

We develop a perceived measure of firms’ disaster exposure and/or preparedness equal to the number of words related to natural disaster events in the firms’ Form 10-Ks. We then link this measure to contemporary and future firm decision-making and performance. We find that this perceived natural disaster risk and the government-reported damages of natural hazards this year are negatively associated with firm profitability next year. However, the perceived natural disaster risk is not associated with sales growth and Tobin's Q ratio. Specifically, the perceived natural disaster risk negatively affects firm profitability in the services sector but not in the manufacturing sector. The firm profitability in the services sector is also negatively affected by the billion-dollar natural disasters in the same year. Finally, we find that advanced machine learning models robustly outperform linear regression in predicting firm performance under natural disaster risks. The main implication from this study is that we can employ textual data in financial reports to measure the perceived natural disaster risk and predict its effects on firm performance.

Keywords: Natural disaster risk, firm performance, Form 10-Ks, text mining, machine learning

JEL Classification: C45, C53, L25, M21, Q51, Q54