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 Sept |
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications