African Journal of
Business Management

  • Abbreviation: Afr. J. Bus. Manage.
  • Language: English
  • ISSN: 1993-8233
  • DOI: 10.5897/AJBM
  • Start Year: 2007
  • Published Articles: 4193

Full Length Research Paper

An expanded Adaptive Neuro-Fuzzy Inference System (ANFIS) model based on AR and causality of multi-nation stock market volatility for TAIEX forecasting

Liang-Ying Wei
Department of Information Management, Yuanpei University, 306 Yuanpei Street, Hsin Chu 30015, Taiwan. 
Email: [email protected]

  •  Accepted: 21 January 2011
  •  Published: 04 August 2011


For common people, stock investing is one popular way to manage their property. As Information Technology (IT) has risen in recent years, every security company has analyzed computer systems for their customers by developing their own investing. Taiwan is an island nation, and the economy relies on international trade deeply. The fluctuations of international stock markets will impact Taiwan stock market to a certain degree. Therefore, the use of fluctuations of other stock markets as forecasting factors for forecasting the Taiwan stock market is a practical way. In this paper, the proposed model uses the fluctuations of other national stock markets as forecasting factors, employs different discretization methods (Fuzzy C-means clustering, Subtractive Clustering and Cumulative Probability Distribution Approach) to discretize stock data, utilizes a fuzzy inference system to produce understandable rules, and applies an adaptive neural network to optimize model parameters to reach the best forecasting accuracy. To evaluate the forecasting performances, the proposed model is compared with two different models, Chen’s model and Yu’s model. The experimental results indicate that the proposed model is superior to the listing methods in terms of RMSE (root mean squared error).


Key words: Data mining, Adaptive Neuro-Fuzzy Inference System (ANFIS), stock forecasting, subtractive clustering, cumulative probability distribution approach.