The significance and importance of financial reporting as the basis and foundation for relevant decision makers to make judgments cannot be overstated, and both capital market participants and regulators attach great importance to the disclosure of financial reports. Although relevant regulatory authorities such as China's Securities Regulatory Commission and Accounting Standards Board have issued corresponding standards and regulations to strictly regulate the disclosure behaviour of companies' financial reports, due to insufficient supervision or relatively low costs of non-compliance, listed companies have committed FF in violation of relevant laws and regulations in order to preserve their own interests, causing unmeasurable losses to stakeholders. The main objective of this paper is to launch a study on the identification of misstatements in listed companies' annual reports based on ML under the perspective of financial restatement prediction. Based on the research on FF patterns and FFI in the first part, we firstly select the set of primary features based on the theory of FF motive, then perform Mann-Whitney test on the set of primary features to obtain the set of original features, and then use Bortua algorithm to select the final set of FI features from the set of original features. The data of the fraud samples and non-fraud samples were loaded into the four types of financial fraud identification models (FFIM) in the order of the original FI features and the FI features constructed by the BA. The model identification results showed that the combination of the FFI features constructed by the BA and the RFM had a good identification effect, and the overall evaluation indexes of G-mean and F-value were 75.9% and 78.3% respectively.

Ga dan Cai Rang. Machine Learning-Based Study on the Identification of Misstatements in Annual Reports of Listed Companies - A Financial Restatement Prediction Perspective. Machine Learning Theory and Practice (2021), Vol. 2, Issue 1: 26-37. https://doi.org/10.38007/ML.2021.020104.

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