Epsilon-SVR and decision tree for stock market forecasting

Abstract

Forecasting has become a very essential skill for all those, related to finance. Further, the advent of data mining tool and analytical technologies has changed the way to explore historical data for investment and managerial decision making. The current paper deals with two established technique viz. Epsilon-SVR and Decision Tree for stock market forecasting. The available numerical historical data and some technical indices of BSE-sensex has been used for empirical studies. Overall expected response of the investors has been reflected by certain rules developed and used with input dataset for predicting future trend. Both epsilon-SVR and Decision Tree techniques are run over the dataset, respective efficiencies has been evaluated and explained through established statistical parameters. The work concludes that the SVM has outperformed decision tree in training front and lagged behind in validation in comparison with regression decision tree.