Stock Market Trend Prediction Based on the LS-SVM Model Update Algorithm

Springer papers

Authors

Ivana Markovic , Miloš Stojanovic , Miloš Božic and Jelena Stankovic

Abstract

The paper proposes a trend prediction model based on an incremental training set update scheme for the BELEX15 stock market index using the Least Squares Support Vector Machines (LS-SVMs) for classification. The basic idea of this updating approach is to add the most recent data to the training set, as become available. In this way, information from new data is taken into account in model training. The test results indicate that the suggested model is suitable for short-term market trend prediction and that prediction accuracy significantly increases after the training set has been updated with new information.

Keywords

Stock market trend prediction Least Squares Support Vector Machines (LS-SVMs) Model update