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

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.