Trading and forecasting performance of different hybrid ARIMA - neural network models for stock returns

Abstract

This study examines the performance of different hybrid methodologies that combine ARIMA and artificial neural network (ANN) to forecast stock market returns. Two new hybrid ARIMA-ANN models are developed and compared with Zhang’s (2003) model on real data sets and the model performance is evaluated using trading performance measures. The study shows that hybrid models outperform independent models and the hybrid ARIMABP model achieves greater accuracy and provides evidence of superiority of the other hybrid models.