Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6091
Title: A hybrid fuzzy intelligent agent-based system for stock price prediction
Authors: Zarandi, Mohammad Hossein Fazel
Hadavandi, Esmaeil
Türkşen, İsmail Burhan
Keywords: [No Keywords]
Issue Date: 2012
Publisher: Wiley
Abstract: Stock price prediction is an important task for most investors and professional analysts. However, it is a tough problem because of the uncertainties involved in prices. This paper presents a four-layer fuzzy multiagent system (FMAS) architecture to develop a hybrid artificial intelligence model based on the coordination of intelligent agents performing data preprocessing and function approximation tasks for next-day stock price prediction. The first layer is dedicated to metadata creation. The second layer is aimed at data preprocessing using stepwise regression analysis and self-organizing map neural network clustering for modularizing prediction problems. The third layer is aimed at model building for each cluster using genetic fuzzy systems and evaluating built models to choose the best evolved fuzzy system for each cluster. Finally, the fourth layer provides model analysis and knowledge presentation. The capability of FMAS is evaluated by applying it on stock price data gathered from IT and airline sectors and comparing the outcomes with the results of other methods. The results show that FMAS outperforms all previous methods, so it can be considered as a suitable tool for stock price prediction problems. (c) 2012 Wiley Periodicals, Inc.
URI: https://doi.org/10.1002/int.21554
https://hdl.handle.net/20.500.11851/6091
ISSN: 0884-8173
1098-111X
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

SCOPUSTM   
Citations

35
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

33
checked on Sep 24, 2022

Page view(s)

18
checked on Feb 6, 2023

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.