Empirical Analysis of Financial Statement Fraud of Listed Companies Based on Logistic Regression and Random Forest Algorithm

Xinchun Liu and Miaochao Chen

Journal of Mathematics, 2021, vol. 2021, 1-9

Abstract: Financial supervision plays an important role in the construction of market economy, but financial data has the characteristics of being nonstationary and nonlinear and low signal-to-noise ratio, so an effective financial detection method is needed. In this paper, two machine learning algorithms, decision tree and random forest, are used to detect the company's financial data. Firstly, based on the financial data of 100 sample listed companies, this paper makes an empirical study on the fraud of financial statements of listed companies by using machine learning technology. Through the empirical analysis of logistic regression, gradient lifting decision tree, and random forest model, the preliminary results are obtained, and then the random forest model is used for secondary judgment. This paper constructs an efficient, accurate, and simple comprehensive application model of machine learning. The empirical results show that the comprehensive application model constructed in this paper has an accuracy of 96.58% in judging the abnormal financial data of listed companies. The paper puts forward an accurate and practical method for capital market participants to identify the fraud of financial statements of listed companies and has certain practical significance for investors and securities research institutions to deal with the fraud of financial statements.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:9241338

DOI: 10.1155/2021/9241338

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