Title: Trading and forecasting performance of different hybrid ARIMA - neural network models for stock returns
Authors: Manish Kumar; M. Thenmozhi
CRISIL Global Research and Analytics, TVH Beliccia Tower-II, Block No: 94, MRC Nagar, Chennai 600028, India
Department of Management Studies, Indian Institute of Technology Madras, Chennai 600036, India
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.
Keywords: ARIMA; artificial neural networks; ANNs; hybrid models; stock returns; trading performance; forecasting performance; modelling; stock markets.
Int. J. of Modelling in Operations Management, 2014 Vol.4, No.3/4, pp.137 - 144
Date of acceptance: 08 Apr 2014
Available online: 05 Feb 2015