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TopIntroduction
The Standard and Poor’s 500 Index (S&P500) is one of the commonly used indices on the New York Stock Exchange. It was introduced in its current form in March, 1957. The 500 publicly traded companies that make up the index are chosen by a committee to best reflect the overall market in the United States. The S&P500 hit its highest point on October 9, 2007. Since then, the United States economy has been on a generally downward trend, and so by extension, has the S&P500. This collapse began with the financial sector, stemming primarily from subprime mortgage problems, but spread rapidly into other sectors of the economy. The S&P500 was characterized by unusually high volatility in 2009. The recent apparent unpredictability of the market has resulted in the loss of millions of dollars of investments.
One of the primary tasks of institutional fund managers and financial analysts is to predict how the market is going to move on a daily basis so that they can better reach their returns goals. To aid the fund managers and financial analysts, this research develops a data-integrated tree-based simulation model to predict how the opening and closing price of the S&P500 move in tandem with the other markets. Classification and Regression Tree (CART) model – a data mining tool for prediction and classification (Breiman, Friedman, Oishen, & Stone, 1984)- is used to develop four regression tree structures: (1) “first” tree predicts S&P500 Open value for Monday mornings based on other market indices, currency exchange rates, and S&P500 open and close values of the prior Friday; (2) “second” tree predicts the S&P500 close value for Monday evenings based on other market indices, currency exchange rates, S&P500 open and close values of the prior Friday, and the predicted S&P500 open value for that morning; (3) “third” tree predicts the S&P500 open values for Tuesday through Friday based on previous day’s predicted S&P500 open and close values; and (4) “fourth” tree predicts the S&P500 close values for Tuesday through Friday based on the predicted value of that day’s S&P500 open value, and previous day’s open and close values. Simulation models developed by sampling from these four trees are better representation of the actual system and more efficient to execute.
Contribution
There are two major contributions made in this research:
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This research introduces a novel approach to the finance modeling community for constructing efficient simulation models based on data mining. This way of simulation modeling avoids misrepresentation of financial dynamics and characteristics because it is entirely based on the pattern learned from a real data set collected from the financial market over a long period of time. Moreover, this approach reduces simulation states and is consequently more efficient to run.
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This research introduces a tool to predict S&P500 open and close values for one week in advance. The simulation model enables fund managers and financial analysts to make investment decisions based on a scientific evidence-based approach.
The rest of this paper is organized as follows: First, we provide a literature review on financial market modeling, data mining, and simulation. Afterwards, a brief introduction is given on data. Then, we describe the data mining tree structures used to build the simulation model. Following that, we present S&P500 predictions and their comparison with the actual data. Finally, we provide concluding remarks including discussions on a possible simulation-based optimization approach to optimize a portfolio.
Literature Review
There are three major components in this research: finance market modeling, data mining, and simulation. This section gives a brief literature review on each of these topics.