Semi-supervised learning algorithms have become a hot topic of research as an alternative to traditional classification methods, exploiting the explicit classification information of labeled data with the knowledge hidden in the unlabeled data for building powerful and effective classifiers. In this work, we evaluate the performance of an ensemble semi-supervised learning algorithm for the prediction of stocks movement in the Dow Jones industrial average. Our experimental results indicate that the proposed algorithm outperforms its component semi-supervised learning algorithms, illustrating that reliable and robust prediction models could be developed utilizing a few labeled and many unlabeled data.