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

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.

[1]  Alberto Ferreira de Souza,et al.  Prediction-based portfolio optimization model using neural networks , 2009, Neurocomputing.

[2]  Ruei-Yuan Chang,et al.  A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively? , 2006 .

[3]  Kyung-shik Shin,et al.  A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets , 2007, Appl. Soft Comput..

[4]  Jingtao Yao,et al.  Financial time-series analysis with rough sets , 2009, Appl. Soft Comput..

[5]  David Enke,et al.  Intelligent technical analysis based equivolume charting for stock trading using neural networks , 2008, Expert Syst. Appl..

[6]  David Enke,et al.  A hybrid stock trading system for intelligent technical analysis-based equivolume charting , 2009, Neurocomputing.

[7]  Shih-Ming Yang,et al.  Development of a Self-Organized Neuro-Fuzzy Model for System Identification , 2007 .

[8]  HassanMd. Rafiul.  A combination of hidden Markov model and fuzzy model for stock market forecasting , 2009 .

[9]  Md. Rafiul Hassan.  A combination of hidden Markov model and fuzzy model for stock market forecasting , 2009, Neurocomputing.

[10]  Cheolbeom Park,et al.  What Do We Know About the Profitability of Technical Analysis? , 2007 .

[11]  Hiok Chai Quek,et al.  RLDDE: A novel reinforcement learning-based dimension and delay estimator for neural networks in time series prediction , 2007, Neurocomputing.

[12]  Kimon P. Valavanis,et al.  Surveying stock market forecasting techniques - Part II: Soft computing methods , 2009, Expert Syst. Appl..

[13]  Kimon P. Valavanis,et al.  Forecasting stock market short-term trends using a neuro-fuzzy based methodology , 2009, Expert Syst. Appl..

[14]  Shouyang Wang,et al.  Forecasting stock market movement direction with support vector machine , 2005, Comput. Oper. Res..

[15]  Kai Keng Ang,et al.  Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach , 2006, IEEE Transactions on Neural Networks.

[16]  Chuen Jyh Chen,et al.  System identification by neuro-fuzzy model with sugeno and mamdani fuzzy rules , 2009 .

[17]  D. M. Deighton,et al.  Computers in Operations Research , 1977, Aust. Comput. J..

[18]  Aytürk Keles,et al.  The adaptive neuro-fuzzy model for forecasting the domestic debt , 2008, Knowl. Based Syst..