Unearthing Financial Statement Fraud: Insights from News Coverage Analysis
提出一个名为PeerMeta的财务报表舞弊检测框架，利用新闻覆盖构建新标签、加入同行因素特征，并应用元学习聚合19种分类器，召回率达0.982，对监管和投资者有重要价值。
We propose a financial statement (FS) fraud detection framework, called PeerMeta, that makes improvements in all three components of the detection procedure: label measurement, feature set, and detection model. For the label measurement, prior studies mainly adopt FS fraud events that have already been disclosed and confirmed. We construct a new measure based on news coverage that can reflect unrevealed FS fraud behaviors as well. For the feature set, we innovatively add peer factors learned through the business description texts in financial reports. For the detection model, two meta-learning algorithms are applied to aggregate the 19 popular classifiers. The results indicate that the proposed method has amazingly high recall of real fraud cases announced by regulatory authorities, reaching a staggering value of 0.982. We document that all components in PeerMeta contribute to the improvements of FS fraud detection and also showcase the significant economic value of the detection framework and find that recall is more crucial for the economic value than precision. This paper was accepted by Agostino Capponi, finance. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71991470, 7199471, 72121002, 72310107002] and the National Key R&D Program of China [Grant 2021YFC3340703]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03604 .