A machine learning approach for stock price prediction

Data mining and machine learning approaches can be incorporated into business intelligence (BI) systems to help users for decision support in many real-life applications. Here, in this paper, we propose a machine learning approach for BI applications. Specifically, we apply structural support vector machines (SSVMs) to perform classification on complex inputs such as the nodes of a graph structure. We connect collaborating companies in the information technology sector in a graph structure and use an SSVM to predict positive or negative movement in their stock prices. The complexity of the SSVM cutting plane optimization problem is determined by the complexity of the separation oracle. It is shown that (i) the separation oracle performs a task equivalent to maximum a posteriori (MAP) inference and (ii) a minimum graph cutting algorithm can solve this problem in the stock price case in polynomial time. Experimental results show the practicability of our proposed machine learning approach in predicting stock prices.