A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX

a r t i c l e i n f o Keywords: Subtractive clustering Adaptive network-based fuzzy inference system Technical indicators Adaptive learning Genetic algorithm Technical analysis is one of the useful forecasting methods to predict the future stock prices. For professional stock analysts and fund managers, how to select necessary technical indicators to forecast stock trends is important. Traditionally, stock analysts have used linear time series models for stock forecasting. However, the results would be in doubt when the forecasting problems are nonlinear. Further, stock market investors usually make short-term decisions based on recent price fluctuations, but most time series models only use the last period of stock prices in forecasting. In this paper, the proposed hybrid model utilizes an adaptive expectation genetic algorithm to optimize adaptive network-based fuzzy inference system (ANFIS) for predicting stock price trends, and four proposed procedures are included in the hybrid model for forecasting: (1) select essential technical indicators from popular indicators by a cited paper (Cheng et al., 2010); (2) use subtractive clustering to partition technical indicator values into linguistic values based on an objective data discretization method; (3) employ fuzzy inference system (FIS) to build linguistic rules from the linguistic technical indicator dataset and optimize the FIS parameters by adaptive network; and (4) refine the proposed model using the adaptive expectation model, which optimizes parameter by genetic algorithm. The effectiveness of the proposed model is verified with performance evaluations and root mean squared error (RMSE), and a 6-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) is selected as the experimental datasets. The experimental results have shown that the proposed model is superior to the three listing forecasting models (Chen's model, Yu's model, and Cheng et al.'s model) in terms of RMSE. In stock markets, there are complex factors that will influence stock markets and nonlinear relationships, which are contained among different periods of stock prices for investors to forecast the future stock trends with difficulty. Therefore, many forecasting methods have been employed in predicting stock prices since the first stock market was opened. Further, financial analysts and stock fund managers attempt to predict price activity in the stock market on the basis of either their professional knowledge or with the assistance of stock analyzing tools. If more accurate predictions are given, myriad profit will be made. Therefore, stock analysts have perennially strived to discover ways to predict stock prices …