Robust stock value prediction using support vector machines with particle swarm optimization

Trevor Sands · Deep Tayal · Matthew E. Morris · Sildomar T. Monteiro

27 citations0/0 files storedIEEE Congress on Evolutionary Computation (CEC)

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

Abstract

Attempting to understand and characterize trends in the stock market has been the goal of numerous market analysts, but these patterns are often difficult to detect until after they have been firmly established. Recently, attempts have been made by both large companies and individual investors to utilize intelligent analysis and trading algorithms to identify potential trends before they occur in the market environment, effectively predicting future stock values and outlooks. In this paper, three different classification algorithms will be compared for the purposes of maximizing capital while minimizing risk to the investor. The main contribution of this work is a demonstrated improvement over other prediction methods using machine learning; the results show that tuning support vector machine parameters with particle swarm optimization leads to highly accurate (approximately 95%) and robust stock forecasting for historical datasets.

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References

1994Artificial economic life: a simple model of a stockmarket 2002Stock market prediction using different neural network classification architectures 2013Support Vector Regression for prediction of stock trend 2005Building neural network models for time series: a statistical approach 2009Advanced Technologies

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