金融上市公司财务预警研究——基于PCA分析和Logistic模型

Research on Financial Early Warning of Financial Listed Companies—Based on PCA Analysis and Logistic Models

摘要: 这篇研究旨在利用主成分分析和Logistic模型来进行金融上市公司财务预警分析。财务预警分析是评估公司财务健康状况并预测可能的财务困境的重要工具。本研究通过PCA分析来降维，提取出最具代表性的财务指标，然后利用Logistic回归模型来预测公司是否会陷入财务困境。用79家金融业上市公司2021年数据进行检验，得出建模组总体预测准确率为71.67%，检验组总体预测准确率为82%。通过这种方法，研究旨在为金融上市公司提供有效的财务预警机制，帮助企业管理层及时发现潜在的财务风险，同时完善了对目前金融业上市公司财务预警模型的相关研究，帮助投资者、管理者和监管机构更好地评估和管理财务风险。

Abstract: The aim of this study is to conduct financial early warning analysis of financial listed companies using Principal Component Analysis and Logistic Modelling. Financial early warning analysis is an important tool for assessing the financial health of a company and predicting possible financial distress. In this study, PCA analysis is used to reduce the dimensionality and extract the most representative financial indicators, and then Logistic regression model is used to predict whether the company will be in financial distress. Tests using 2021 data from 79 listed companies in the financial sector yielded an overall prediction accuracy of 71.67% for the modelling group and 82% for the test group. Through this method, the study aims to provide listed financial companies with an effective financial early warning mechanism to help corporate management identify potential financial risks in a timely manner, and at the same time, it improves the relevant research on the current financial early warning models of listed companies in the financial industry to help investors, managers, and regulators better assess and manage financial risks.