A Hybrid Particle Swarm Algorithm for Financial Risk Early Warning Optimization

Abstract

Risk early warning is the main content of financial management, however in the risk early warning process, the amount of financial information and early warning means will have an impact on the results, reduce the risk early warning accuracy, and cause the alert result is incorrect. Based on this, this study proposes a hybrid particle swarm algorithm to warn the financial management data risk to increase the level of financial management, and shorten the financial early warning time. Then comprehensive early warning of financial management data is carried out. Finally, continuous monitoring is used to manage risk early warning and output the results of final warning. The results provide the mixed particle swarm algorithm can accurately take the risk early warning, improve the level of risk early warning, as well as accuracy of risk early warning is greater, which is better than the continuous monitoring method. In this study, the hybrid particle swarm optimization algorithm can increase the financial risk early warning accuracy (90%), ensure the integrity of analysis results (85%), and shorten the early warning time, as well as control the early warning time within 20 seconds, so the overall results of hybrid particle swarm optimization algorithm are better than previous early warning algorithms. Therefore, the hybrid particle swarm algorithm can accommodate the early financial warning selection essentials and is advisable for continuous financial management analysis.