A study of ANFIS-based multi-factor time series models for forecasting stock index

Abstract

Despite the widespread use of time series models in stock index forecasts, some of these models have encountered problems: (1) the selection of input factors may depend on personal experience or opinion; and (2) most conventional time series models consider only one variable. Furthermore, traditional forecasting models suffer from the following drawbacks: (1) models may rely on restrictive assumptions (such as linear separability or normality) about the variables being analyzed; and (2) it is hard to define and select applicable input factors for artificial neural networks (ANNs) in particular, and the rules generated from ANNs are not easily understood. To address these issues, we propose a multi-factor time series model based on an adaptive network-based fuzzy inference system (ANFIS) for stock index forecasting. In the proposed model, stepwise regression was first applied for the objective selection of technical indicators and then combined with ANFIS to construct the forecasting model. We evaluated the performance of our proposed model against three other models, with transaction data from the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the Hong Kong Hang Seng Index (HSI) stock markets from 1998 to 2006 as experimental data sets and the root mean square error (RMSE) as the evaluation criterion. The results show the superiority of the proposed combined model, which outperformed other models in terms of RMSE and profitability, with strategies for increasing long-term uses of stock index forecasts made on the TAIEX and the HSI.