Balancing recall and precision in stock market predictors using support vector machines

Computational finance is one of the fields where machine learning and data mining have found in recent years a large application. Neverthless, there are still many open issues regarding the predictability of the stock market, and the possibility to build an automatic intelligent trader able to make forecasts on stock prices, and to develop a profitable trading strategy. In this paper, we propose an automatic trading strategy based on support vector machines, which employs recall-precision curves in order to allow a buying action for the trader only when the confidence of the prediction is high. We present an extensive experimental evaluation which compares our trader with several classic competitors. © Springer-Verlag Berlin Heidelberg 2013.