  Data Mining is being actively applied to stock market since 1980s. It has
been used to predict stock prices, stock indexes, for portfolio management,
trend detection and for developing recommender systems. The various algorithms
which have been used for the same include ANN, SVM, ARIMA, GARCH etc. Different
hybrid models have been developed by combining these algorithms with other
algorithms like roughest, fuzzy logic, GA, PSO, DE, ACO etc. to improve the
efficiency. This paper proposes DE-SVM model (Differential EvolutionSupport
vector Machine) for stock price prediction. DE has been used to select best
free parameters combination for SVM to improve results. The paper also compares
the results of prediction with the outputs of SVM alone and PSO-SVM model
(Particle Swarm Optimization). The effect of normalization of data on the
accuracy of prediction has also been studied.
