Using particle swarm optimization (PSO) to perform financial characteristic study for enterprises in Taiwan

摘要

Since Particle Swarm Optimization (PSO) has properties such as: fast convergence, the ability to search global optimum and very strong universal characteritistic, it is thus very suitable to be used in clustering analysis and parameter utilization of optimized neural network by the researchers. Therefore, in this article, it is used to applied in analyzing enterprise’s Financial Characteristic. First, in this article, based on the profit force and growth force of financial five forces, the financial ratio data of companies with stocks listed in regular and over-the-counter stock market in Taiwan and in financial crisis are collected, meanwhile, two normal enterprises with similar characteristics are collected for pairing purpose. Furthermore, with the aim of deriving profit force and growth force, respectively, Grey Relational Analysis is done; in the mean time, the analytical results of both of them are ranked according to grey relational grade so as to understand the performance ranking of each enterprise in profit force and growth force; then PSO is used to divide it into two groups, and the financial characteristics of these two groups of companies are compared, and the results can be used as reference by managers in the enterprises; finally in this article, three data mining techniques such as: PSO Grey Model Neural Network, Genetic Algorithm Optimized? Grey Model Neural Network and general Grey Model Neural Network are used, respectively to set up Enterprise Financial Distress model and Enterprise Financial Characteristic detection model. The anlysis indicates that two different groups can be divided based on PSO. One group is enterprises that excel in profit force and growth force while the other group is enterprises that are not good at both of them. On the other hand, in Enterprise Financial Distress model and Enterprise Financial Characteristic model, the PSO Grey Model Neural Network model demonstrates the fastest convergence and the best classification capability.