Report Users' Perceived Sentiments of Key Audit Matters and Firm Performance: Evidence from a Deep Learning-Based Natural Language Processing Approach

ABSTRACT We investigate the associations between the sentiment report users perceive in key audit matters (KAMs) and current and future firm performance. We also investigate the validity of the bidirectional encoder representations from transformers (BERT) model for automatically extracting KAM sentiment in Taiwanese listed firms' audit reports. Positive associations between KAM sentiment and current and next-year firm performances, measured by Tobin's Q, ROA, and ROE, are discovered based on a two-year sample of 1,606 firm–year observations, including manually labeled sentiment data in 2017 and BERT-extracted sentiment data in 2018. However, the evidence of the positive association between KAM sentiment and current firm market performance (Tobin's Q) is weaker in 2017 than in 2018 statistically. Our results suggest that KAM sentiment reflects future firm performance and support the application of the BERT deep learning approach for textual mining. This study has implications for regulators, practitioners, and academics. JEL Classifications: D83; L25; M42.