Author(s)

Yaowen Hu

Corresponding Author

Yaowen Hu

Abstract

The purpose of this paper is to discuss the prediction and economic statistical analysis of financial crisis in order to cope with the ever-changing economic environment and the complexity of financial markets. The results of economic statistical analysis provide us with profound enlightenment and show the complex relationship between different factors. It is recognized that the financial crisis is a multi-dimensional phenomenon, and its influencing factors include both macroeconomic factors and micro-level market behavior. This urges us to comprehensively use different statistical methods to understand and predict the operation of financial markets more comprehensively. In order to improve the accuracy and robustness of prediction, this paper establishes a prediction model combining BP neural network (BPNN) and Support Vector Machine(SVM). The performance of the model is comprehensively evaluated by using cross-validation and ROC curve, and the efficiency of the model is improved by adjusting parameters and optimizing data characteristics. Under the background of increasing uncertainty and risks, the rational use of economic statistical analysis and advanced forecasting models will become an effective tool to meet the challenges of financial markets. This paper emphasizes the importance of comprehensive application of various methods in the study of financial crisis, and calls for future research to further deepen the understanding of financial markets in order to better cope with future economic challenges.

Keywords

financial crisis; statistical analysis; Prediction