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Data driven modeling of co‐movement among international stock market

Data driven modeling of co‐movement among international stock market Purpose – The aim of this paper is to research the correlation using artificial intelligent tools among international stock markets issuing for the companies. Design/methodology/approach – The objective is to find out the correlation among markets so it can be used for trend prediction. The stock price data from various companies that have issued stock in different countries were used to produce analysis for predictive purposes. Various artificial intelligent tools were used and the predictive performance among them compared. Findings – The finding is that the predictive results when using one market to predict another is above 50 percent and higher, which is much better than random walk. Research limitations/implications – The limitations are that only the raw market data are worked on, but there are many factors that could affect the short‐term trend of a stock. Practical implications – This could benefit traders who are interested in trading international issuing stock by taking advantage of markets' different time zones. Originality/value – The approach provides a methodology approach to predict the moving trend of a stock among international markets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Modelling in Management Emerald Publishing

Data driven modeling of co‐movement among international stock market

Journal of Modelling in Management , Volume 2 (3): 13 – Nov 6, 2007

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Publisher
Emerald Publishing
Copyright
Copyright © 2007 Emerald Group Publishing Limited. All rights reserved.
ISSN
1746-5664
DOI
10.1108/17465660710834426
Publisher site
See Article on Publisher Site

Abstract

Purpose – The aim of this paper is to research the correlation using artificial intelligent tools among international stock markets issuing for the companies. Design/methodology/approach – The objective is to find out the correlation among markets so it can be used for trend prediction. The stock price data from various companies that have issued stock in different countries were used to produce analysis for predictive purposes. Various artificial intelligent tools were used and the predictive performance among them compared. Findings – The finding is that the predictive results when using one market to predict another is above 50 percent and higher, which is much better than random walk. Research limitations/implications – The limitations are that only the raw market data are worked on, but there are many factors that could affect the short‐term trend of a stock. Practical implications – This could benefit traders who are interested in trading international issuing stock by taking advantage of markets' different time zones. Originality/value – The approach provides a methodology approach to predict the moving trend of a stock among international markets.

Journal

Journal of Modelling in ManagementEmerald Publishing

Published: Nov 6, 2007

Keywords: Neural nets; Decision trees; Predictive process

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