Research on Financial Risk Prediction Based on Improved Random Subspace

Yinghui Li,Man Fai Leung

doi:10.1155/2022/3406176

Abstract

In order to provide timely and effective information and decision support for financial market entities, combined with random subspace and weight fused Lasso, this paper constructs a financial risk prediction model based on the improved random subspace method. Firstly, the basic principles of random subspace and SVM algorithm are introduced. Then, WFL and Al methods are introduced to improve random subspace, so as to reduce the dimension of multisource heterogeneous data and realize the adaptive fusion of features. Then, a financial risk prediction model based on weighted fusion adaptive random subspace is constructed, in which SVM is used as the basic classifier and the output strategy of result integration is introduced. Finally, based on the data of some listed companies, the improved random subspace method is compared with other methods. The results show that the improved random subspace method has a higher prediction value, which indicates that the method is reasonable and effective in financial risk prediction. In the improved random subspace method, combined feature F1 + F2 + F3 is better than other methods in T − 3, T − 4, and T − 5, and the prediction value is more than 95%, which fully demonstrates the rationality of the improved random subspace method in financial risk prediction. The area under the ROC curve (AUC) predicted by weight fused adaptive integration-based random subspace (FAIB_RS) method is about 95% in T − 3, 93% in T − 4, and 95.5% in T − 5, which is obviously higher than that of the other eight methods.

Highlights

With the rapid development of the financial industry and the continuous popularization of the internet financial model, the financial market is facing more severe financial risks
In order to improve the ability of financial market subjects to obtain financial risk early warning information, domestic experts and scholars have done a lot of research on financial risk and put forward a variety of financial risk prediction methods
In T − 5, the values of this method are 95.79%, 96.17%, and 96.67%. e comparison analysis shows that the improved random subspace method has achieved the highest results in financial risk prediction among all methods, especially in the feature set of F1 + F2 + F3 under the time panel T − 5, which has achieved an average rate of 97.67%

Summary

Introduction

With the rapid development of the financial industry and the continuous popularization of the internet financial model, the financial market is facing more severe financial risks. Erefore, the machine learning method based on Scientific Programming single-source data can only detect the risks of formal financial activities, while the effect of using multisource data to detect financial risks is not good. The integration of classifiers in reference [6] has been applied in the financial industry, there are some wrong factors that hinder the prediction performance, such as irrelevant features, inclined categories, and so on. How to make good use of big data to effectively predict and prevent is of great significance to the financial industry, which is the essence of financial management, that is, risk management and control. Combined with the advantages of big data prevention and control and prediction, this paper summarizes feasible and effective financial risk management and control countermeasures. E RS steps are as follows: Step: according to the feature dimension of data samples, data samples are randomly selected to form data subsets of similar sizes. e subspace ratio parameter r is used to adjust the size of the data subset.

Methods

Results

Conclusion