A comparative study of predictive algorithms for business analytics and decision support systems: Finance as a case study

Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using machine learning techniques in this area. A large number of successful applications have shown that regression algorithms can be very useful tools for time-series modelling and forecasting. In this paper we run a comparative study of three of these algorithms: Multiple Linear Regression, Support Vector Regression and Decision Tree Regression in order to determine their performances in term of implementing financial time series forecasts. To assess the performance of these algorithms, we have conducted experiments using L'Oréal financial dataset. The results exhibit that support vector regression produced the best forecasts.