Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Analyzing the Internet financial market risk management using data mining and deep learning methods

Analyzing the Internet financial market risk management using data mining and deep learning methods To identify and analyze the occurrence of Internet financial market risk, data mining technology is combined with deep learning to process and analyze. The market risk management of the Internet is to improve the management level of Internet financial risk, improve the policy of Internet financial supervision and promote the healthy development of Internet finance.Design/methodology/approachIn this exploration, data mining technology is combined with deep learning to mine the Internet financial data, warn the potential risks in the market and provide targeted risk management measures. Therefore, in this article, to improve the application ability of data mining in dealing with Internet financial risk management, the radial basis function (RBF) neural network algorithm optimized by ant colony optimization (ACO) is proposed.FindingsThe results show that the actual error of the ACO optimized RBF neural network is 0.249, which is 0.149 different from the target error, indicating that the optimized algorithm can make the calculation results more accurate. The fitting results of the RBF neural network and ACO optimized RBF neural network for nonlinear function are compared. Compared with the performance of other algorithms, the error of ACO optimized RBF neural network is 0.249, the running time is 2.212 s, and the number of iterations is 36, which is far less than the actual results of the other two algorithms.Originality/valueThe optimized algorithm has a better spatial mapping and generalization ability and can get higher accuracy in short-term training. Therefore, the ACO optimized RBF neural network algorithm designed in this exploration has a high accuracy for the prediction of Internet financial market risk. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Enterprise Information Management Emerald Publishing

Analyzing the Internet financial market risk management using data mining and deep learning methods

Feng, Ran; Qu, Xiaoe
Journal of Enterprise Information Management , Volume 35 (4/5): 19 – Jun 20, 2022
Download PDF
19 pages

Loading next page...
 
/lp/emerald-publishing/analyzing-the-internet-financial-market-risk-management-using-data-UMErbYOw0t

References (56)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1741-0398
DOI
10.1108/jeim-03-2021-0155
Publisher site
See Article on Publisher Site

Abstract

To identify and analyze the occurrence of Internet financial market risk, data mining technology is combined with deep learning to process and analyze. The market risk management of the Internet is to improve the management level of Internet financial risk, improve the policy of Internet financial supervision and promote the healthy development of Internet finance.Design/methodology/approachIn this exploration, data mining technology is combined with deep learning to mine the Internet financial data, warn the potential risks in the market and provide targeted risk management measures. Therefore, in this article, to improve the application ability of data mining in dealing with Internet financial risk management, the radial basis function (RBF) neural network algorithm optimized by ant colony optimization (ACO) is proposed.FindingsThe results show that the actual error of the ACO optimized RBF neural network is 0.249, which is 0.149 different from the target error, indicating that the optimized algorithm can make the calculation results more accurate. The fitting results of the RBF neural network and ACO optimized RBF neural network for nonlinear function are compared. Compared with the performance of other algorithms, the error of ACO optimized RBF neural network is 0.249, the running time is 2.212 s, and the number of iterations is 36, which is far less than the actual results of the other two algorithms.Originality/valueThe optimized algorithm has a better spatial mapping and generalization ability and can get higher accuracy in short-term training. Therefore, the ACO optimized RBF neural network algorithm designed in this exploration has a high accuracy for the prediction of Internet financial market risk.

Journal

Journal of Enterprise Information Management – Emerald Publishing

Published: Jun 20, 2022

Keywords: Internet finance; Market risk management; Data mining technologies; Radial basis function; Ant colony optimization

There are no references for this article.