Forecasting stock market movement direction with support vector machine
Computers and Operations Research
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
Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach
IEEE Transactions on Neural Networks
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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.