The purpose of this paper is to develop and identify the best hybrid model to predict stock index returns. We develop three different hybrid models combining linear ARIMA and non-linear models such as support vector machines (SVM), artificial neural network (ANN) and random forest (RF) models to...

This study examines the dynamic causal (linear as well as non-linear) relationship between trading volume and return and between volatility and returns. We have used the vector autoregression based Granger causality framework to examine the linear causality, while the non-linear causality have...

The present study, investigates the predictability of Samp;P CNX NIFTY Index returns using Support vector machines (SVM). The performance of the SVM model in forecasting Nifty index returns is rigorously evaluated in terms of widely used statistical metrics like mean absolute error, root mean...