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

Authors:
Ching Long Su;Chuen Jyh Chen;Shih Ming Yang
Affiliations:
Department of Information Management, Chang Jung Christian University, Taiwan, R.O.C.;Department of Aviation Service Management, Aletheia University, Taiwan, R.O.C.;Department of Aeronautics and Astronautics, National Cheng Kung University, Taiwan, R.O.C.
Venue:
WSEAS Transactions on Information Science and Applications
Year:
2010

Citing 12
Cited 0

Forecasting stock market movement direction with support vector machine

Computers and Operations Research
A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets

Applied Soft Computing
RLDDE: A novel reinforcement learning-based dimension and delay estimator for neural networks in time series prediction

Neurocomputing
Intelligent technical analysis based equivolume charting for stock trading using neural networks

Expert Systems with Applications: An International Journal
The adaptive neuro-fuzzy model for forecasting the domestic debt

Knowledge-Based Systems
Surveying stock market forecasting techniques - Part II: Soft computing methods

Expert Systems with Applications: An International Journal
Financial time-series analysis with rough sets

Applied Soft Computing
Forecasting stock market short-term trends using a neuro-fuzzy based methodology

Expert Systems with Applications: An International Journal
Prediction-based portfolio optimization model using neural networks

Neurocomputing
A combination of hidden Markov model and fuzzy model for stock market forecasting

Neurocomputing
A hybrid stock trading system for intelligent technical analysis-based equivolume charting

Neurocomputing
Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

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.