A Data-Integrated Tree-Based Simulation to Predict Financial Market Movement

Durai Sundaramoorthi, Andrew Coult and Dung Hai Nguyen
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Durai Sundaramoorthi: Washington University in St. Louis, USA
Andrew Coult: Missouri Western State University, USA
Dung Hai Nguyen: Washington University in St. Louis, USA

International Journal of Operations Research and Information Systems (IJORIS), 2012, vol. 3, issue 3, 74-86

Abstract: The Standard and Poor’s 500 Index (S&P500) is one of the commonly used indices on the New York Stock Exchange. The 500 publicly traded companies that make up the index are chosen by a committee to best reflect the overall market of the United States. The broader objective of this research is to estimate the dynamics of the financial market movement in the United States. It is achieved by developing a data-integrated tree-based simulation model to predict S&P500 open and close values for a week. Classification and Regression Trees (CART) - a data mining method - is utilized to extract patterns of the financial market dynamics based on a data set collected from May 1, 2008 to November 30, 2009. The data set included the daily movement of financial markets in seven countries in Asia and Europe in relation to the daily movement of the S&P500. CART also utilized data on the currency exchange rates to capture the financial dynamics between the US and other countries. The simulation model repeatedly samples from four trees developed by CART to know how the opening and closing values of the S&P500 move in tandem with the other markets.

Date: 2012
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