Research on Financial Crisis Early Warning Optimization of Listed Companies Based on Machine Learning Algorithm

Financial crisis is an important factor affecting the reputation of listed companies, whether their stocks can be traded normally, and whether the company's operations can be carried out normally. How to establish an effective financial crisis early warning method for listed companies to prevent the occurrence of financial crisis is a powerful guarantee for the sustainable development of current listed companies. This paper evaluates the existing frontier early warning models of corporate financial distress, selects some early warning methods that scholars focus on at this stage, and responds to a major controversy in the empirical research on existing financial early warning models, that is, whether various machine learning algorithms are accurate or not. Sexually, it is completely superior to traditional early warning methods. The early warning index system, early warning index reduction method, early warning method and machine learning model interpretation method selected in this paper are all effective; in the ensemble learning method, random forest has the most accurate early warning results among all models. Companies in different industries have different possibilities for financial distress, with manufacturing, real estate, and wholesale and retail facing the highest levels of risk; among all early warning indicators, financial indicators related to profitability should be monitored the most because they are related to net profit. Profit-related indicators have the greatest impact on the model prediction results.