A fusion ANFIS model for forecasting EPS of leading industries in Taiwan

Earnings per share (EPS) is often regarded as a major indicator for investors to purchase stocks. The traditional approach is to use a conventional linear time series model for EPS prediction. However, the results would be in doubt when the forecasting problems are nonlinear. For this reason, this paper proposes a fusion forecasting model that incorporates an autoregressive model into an adaptive network-based fuzzy inference system (ANFIS) To illustrate the proposed model, 15-quarter EPS data are employed. The experimental results indicate that the proposed model outperforms the listing models.