The Informational Content of Key Audit Matters: Evidence from Using Artificial Intelligence in Textual Analysis

51 Pages Posted: 31 May 2023

See all articles by Stephan Küster

Stephan Küster

Free University of Berlin (FUB) - Department of Finance, Accounting and Taxation

Tobias Steindl

University of Regensburg

Max Goettsche

Catholic University of Eichstaett-Ingolstadt

Date Written: May 31, 2023

Abstract

Key Audit Matters (KAMs) are informative for future accounting outcomes. Using FINBERT, a deep learning model for natural language processing that allows human-like text comprehension, we show that goodwill-related KAMs are predictive for firms' future impairments. In fact, we find that using KAMs as a stand-alone predictor for future impairments significantly outperforms a random classifier. Delving deeper into the semantic meaning of reported KAMs, we find that their predictive power is driven by text passages that elaborate how the firm and the auditor exercised their judgement in respect to the accounting and auditing of goodwill. Further analyses indicate that the informational content of KAMs is also incrementally predictive beyond key firm-level determinants of impairments identified in prior studies. Taken together, our findings contribute to the overall understanding of the informational content of KAMs, a key rationale for their introduction.

Keywords: audit reporting; key audit matters; prediction; natural language processing; FINBERT; goodwill impairment

JEL Classification: M41, M42, C45, G32, M48

Suggested Citation

Küster, Stephan and Steindl, Tobias and Goettsche, Max, The Informational Content of Key Audit Matters: Evidence from Using Artificial Intelligence in Textual Analysis (May 31, 2023). Available at SSRN: https://ssrn.com/abstract=4464713 or http://dx.doi.org/10.2139/ssrn.4464713

Stephan Küster (Contact Author)

Free University of Berlin (FUB) - Department of Finance, Accounting and Taxation ( email )

Garystrasse 21
Berlin, 14195
Germany

Tobias Steindl

University of Regensburg

Max Goettsche

Catholic University of Eichstaett-Ingolstadt ( email )

Auf der Schanz 49
Ingolstadt, D-85049
Germany

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