A GA-weighted ANFIS model based on multiple stock market volatility causality for TAIEX forecasting

Stock market forecasting is important and interesting, because the successful prediction of stock prices may promise attractive benefits. The economy of Taiwan relies on international trade deeply, and the fluctuations of international stock markets will impact Taiwan stock market. For this reason, it is a practical way to use the fluctuations of other stock markets as forecasting factors for forecasting the Taiwan stock market. In this paper, the proposed model uses the fluctuations of other national stock markets as forecasting factors and employs a genetic algorithm (GA) to refine the weights of rules joining in an ANFIS model to forecast the Taiwan stock index. To evaluate the forecasting performances, the proposed model is compared with four different models: Chen's model, Yu's model, Huarng's model, and the ANFIS model. The results indicate that the proposed model is superior to the listing methods in terms of the root mean squared error (RMSE).

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► In this paper, the proposed model uses the fluctuations of other national stock markets as forecasting factors, employs genetic algorithm (GA) to optimize the weights of rules joining in an adaptive-network-based fuzzy inference system (ANFIS) model to forecast the Taiwan stock index. ► The results indicate that proposed model is superior to the listing methods in terms of the root mean squared error (RMSE). ► Moreover, forecasting rules generated by the proposed are useful and viable for stock investors, decision makers and future researches. ► Investors can utilize this forecasting model to discover the superior target of investment with benefits in stock market.