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

C. L. Su, C. J. Chen, S. M. Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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 languageEnglish
Title of host publicationLatest Trends on Computers - 14th WSEAS International Conference on Computers, Part of the 14th WSEAS CSCC Multiconference
Pages733-745
Number of pages13
Publication statusPublished - 2010 Dec 1
Event14th WSEAS International Conference on Computers, Part of the 14th WSEAS CSCC Multiconference - Corfu Island, Greece
Duration: 2010 Jul 232010 Jul 25

Publication series

NameInternational Conference on Computers - Proceedings
Volume1

Other

Other14th WSEAS International Conference on Computers, Part of the 14th WSEAS CSCC Multiconference
CountryGreece
CityCorfu Island
Period10-07-2310-07-25

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture

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