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Prediction of International Stock Markets Based on Hybrid Intelligent Systems

Prediction of International Stock Markets Based on Hybrid Intelligent Systems
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Author(s): Salim Lahmiri (University of Quebec at Montreal, Canada & ESCA School of Management, Morocco)
Copyright: 2016
Pages: 15
Source title: Handbook of Research on Innovations in Information Retrieval, Analysis, and Management
Source Author(s)/Editor(s): Jorge Tiago Martins (The University of Sheffield, UK)and Andreea Molnar (University of Portsmouth, UK)
DOI: 10.4018/978-1-4666-8833-9.ch004

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Abstract

This paper compares the accuracy of three hybrid intelligent systems in forecasting ten international stock market indices; namely the CAC40, DAX, FTSE, Hang Seng, KOSPI, NASDAQ, NIKKEI, S&P500, Taiwan stock market price index, and the Canadian TSE. In particular, genetic algorithms (GA) are used to optimize the topology and parameters of the adaptive time delay neural networks (ATNN) and the time delay neural networks (TDNN). The third intelligent system is the adaptive neuro-fuzzy inference system (ANFIS) that basically integrates fuzzy logic into the artificial neural network (ANN) to better model information and explain decision making process. Based on out-of-sample simulation results, it was found that contrary to the literature GA-TDNN significantly outperforms GA-ATDNN. In addition, ANFIS was found to be more effective in forecasting CAC40, FTSE, Hang Seng, NIKKEI, Taiwan, and TSE price level. In contrary, GA-TDNN and GA-ATDNN were found to be superior to ANFIS in predicting DAX, KOSPI, and NASDAQ future prices.

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