Financial fraud detection for Chinese listed firms: Does managers' abnormal tone matter?

Jingyu Li, Ce Guo, Sijia Lv, Qiwei Xie and Xiaolong Zheng

Emerging Markets Review, 2024, vol. 62, issue C

Abstract: This study introduces a novel perspective on financial fraud detection by exploring the utility of managers' abnormal tone. To mitigate bias in indicator selection, we implement a feature selection process involving a comprehensive set of 301 indicators, including financial, non-financial, and textual, and various machine learning algorithms. The dataset contains 6077 pairs of fraudulent and non-fraudulent samples in China. Our findings underscore the significance of abnormal tone in fraud detection, establishing it as a prominent factor in the feature selection process. The accuracy outcomes from eight machine learning models further confirm that incorporating abnormal tone can enhance fraud detection performance.

Keywords: Financial fraud; Managers' abnormal tone; Machine learning; Feature selection (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ememar:v:62:y:2024:i:c:s1566014124000657

DOI: 10.1016/j.ememar.2024.101170

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