Title: Machine learning in financial risk forecasting and management for trading firms

Authors: Zhiqing Zhou

Addresses: School of Humanities and Management, Xi'an Traffic Engineering Institute, Xi'an, 710300, China

Abstract: To improve the financial risk prediction ability of e-commerce enterprises, this study combines BP neural network and PLS method to construct a financial crisis warning model for e-commerce enterprises, namely the BP-PLS model. The experiment first analyses and selects financial crisis warning indicators for e-commerce enterprises, and then extracts components using PLS method. Then, the results of component extraction are used as input vectors for the BP neural network. Finally, the experiment used the BP-PLS model to construct financial crisis warning models for e-commerce enterprises in T-1, T-2, and T-3 years, respectively. The experimental results show that the accuracy of both T-1 and T-2 models is above 90%. The accuracy of the T-3 model exceeds 85%. Therefore, the established model can meet the needs of financial crisis warning. In addition, the model has excellent performance and its training error convergence effect is superior to other models.

Keywords: machine learning; e-commerce enterprise; financial risk; forecast; BPNN.

DOI: 10.1504/IJCSYSE.2023.132917

International Journal of Computational Systems Engineering, 2023 Vol.7 No.2/3/4, pp.159 - 167

Received: 29 Nov 2022
Accepted: 12 Mar 2023
Published online: 16 Aug 2023 *

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