Corporate Risk Disclosure and Audit Fee: A Text Mining Approach

Rong Yang, Yang Yu, Manlu Liu and Kean Wu

European Accounting Review, 2018, vol. 27, issue 3, 583-594

Abstract: 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.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (18)

Downloads: (external link)
http://hdl.handle.net/10.1080/09638180.2017.1329660 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:euract:v:27:y:2018:i:3:p:583-594

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/REAR20

DOI: 10.1080/09638180.2017.1329660

Access Statistics for this article

European Accounting Review is currently edited by Laurence van Lent

More articles in European Accounting Review from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().