Stock price forecasting using PSOSVM

In this research, we propose a machine learning system based on Particle Swarm Optimization (PSO) and Support Vector Machines (SVM) for stock market forecast. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The PSO is used to select the set of most Informative input features from among all the technical indicators. The results show that the PSOSVM system outperforms the stand alone SVM system.