Prediction of assets behavior in financial series using machine learning algorithms

Abstract

The prediction of financial assets using either classification or regression models, is a challenge that has been growing in the recent years, despite the large number of publications of forecasting models for this task. Basically, the non-linear tendency of the series and the unexpected behavior of assets (compared to forecasts generated in studies of fundamental analysis or technical analysis) make this problem very hard to solve. In this work, we present for this task some modeling techniques using Support Vector Machines (SVM) and a comparative performance analysis against other basic machine learning approaches, such as Logistic Regression and Naive Bayes. We use an evaluation set based on company stocks of the BVM&F, the official stock market in Brazil, the third largest in the world. We show good prediction results, and we conclude that it is not possible to find a single model that generates good results for every asset. We also present how to evaluate such parameters for each model. The generated model can also provide additional information to other approaches, such as regression models.