Support vector machine today belongs to the very top of the machine learning classification algorithm, with successful application in resolution of all kinds of problems. As the first algorithm was the result of the statistical learning theory, it owes its excellent generalization performance to the implementation of the structural risk minimalization principle, based on simultaneous minimalization of the empirical risk and VC-dimension, i.e. the capacity of the class of functions implemented by the learning machine. In this paper, the possibility of prediction of the stock market price movement was researched by means of implementation of the support vector machine, where technical indicators were used as input variables, while the sign of the excess of returns on a specific date in the future represented the output variable. Given that, apart from the algorithm itself, the classification performance also depends on other system elements, the impact of the choice of features on the classification results, i.e. different parameter combinations, data imbalance, as well as the series length, were also examined. Different evaluation measures were compared and the prediction results were also tested in a market trading simulator, where it was shown that a better result could be obtained by the combination of multiple classifiers, in the manner that each one learns how to solve its own task. Out of the three stock market indexes, although chosen for the experiment on the basis of the time series predictability tests, ultimately in only one of them satisfactory results were achieved, given that insufficient predictive power of selected technical indicators proved to represent the main obstacle in obtaining better results.