Research on Financial Early Warning Based on Support Vector Machine Algorithm PCA-SVM Model

In the stock market, there are always some listed companies that cause financial crisis due to bad operation, irregular internal management and external environment change. Due to the lag of financial information and the existence of earnings management and financial fraud in some listed companies, the stakeholders make wrong judgments and lose their interests. Machine learning has the advantage of quickly processing large amounts of data and can efficiently mine valuable information to draw conclusions. Therefore, many scholars apply machine learning to financial early warning. This paper further studies the financial risk early warning model and makes the following contributions: describes the theoretical knowledge of the financial early warning model in detail, and creates the theoretical basis for the subsequent research. To a certain extent, the research of support vector machine on financial risk early warning is in-depth, especially on information technology. The research of risk multi-classification early warning of technical listed companies provides a new way for the same type of research. A PCA-SVM model is proposed, and its performance is enhanced on the basis of the original model. After comparing the PCA-SVM model with other models, it is found that the SVM model has the highest accuracy and the highest stability.