Corporate Risk Disclosure and Audit Fee: A Text Mining Approach

The aim of this study is to introduce an innovative text mining approach to assess firms' risks using unstructured textual disclosure from annual reports. Specifically, we use Natural Language Processing techniques to extract firms' self-identified risks including financial, strategic, operational, and hazard risks based on an enterprise risk management framework. We examine the association between these four risk measures derived from the risk factor section in 10-K filings and audit fees. The results show that audit fees are significantly and positively related to firm-specific financial, strategic, and operational risks, indicating the informativeness of corporate textual risk disclosures. This study provides direct support for the recent US reporting regulatory requirement of adding a new section on risk factors in corporate annual reports.