A self-organized neuro-fuzzy system for stock market dynamics modeling and forecasting

Ching Long Su; Chuen Jyh Chen; Shih Ming Yang

A self-organized neuro-fuzzy system for stock market dynamics modeling and forecasting

Ching Long Su, Chuen Jyh Chen, Shih Ming Yang

Research output: Contribution to journal › Article

Abstract

A self-organized, five-layer neuro-fuzzy model is developed to model the dynamics of stock market by using technical indicators. The model effectiveness in prediction and forecasting is validated by a set of data containing four indicators: the stochastic oscillator (%K and %D), volume adjusted moving average (VAMA) and ease of movement (EMV) from TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index). A modified moving average method is proposed to predict the input set for the neuro-fuzzy model in forecasting stock price. Simulation results show that the model is effective in prediction and accurate in forecasting. The input error from the prediction of the modified moving average method is attenuated significantly by the neuro-fuzzy model to yield better forecasting results.

Original language	English
Pages (from-to)	1137-1149
Number of pages	13
Journal	WSEAS Transactions on Information Science and Applications
Volume	7
Issue number	9
Publication status	Published - 2010 Sep 1

All Science Journal Classification (ASJC) codes

Information Systems
Computer Science Applications

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Su, C. L., Chen, C. J., & Yang, S. M. (2010). A self-organized neuro-fuzzy system for stock market dynamics modeling and forecasting. WSEAS Transactions on Information Science and Applications, 7(9), 1137-1149.

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