Stochastic Fuzzy Neural Network and It's Application in the Forecasting of Corporation's Earnings
Junping Wang · Acta Simulata Systematica Sinica · 2003
The paper systematically introduces the stochastic fuzzy neural network(SFNN) and applies it to the classification and forecasting of public corporation抯 earnings per share to settle the noise problem that the common forecasting methods have not considered. We choose 300 public corporations from Shanghai and Shenzhen stock markets as samples, and use the stochastic fuzzy neural network to simulate the classification and forecasting of earnings per share. We also use the fuzzy neural network to simulate and compare these two results. It shows that the simulation result of the stochastic fuzzy neural network is better. This provides some practical meanings for the investors to grasp the investing chance and make right investment decision to receive high earnings.