Title: Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models
Authors: Manish Kumar; M. Thenmozhi
Addresses: CRISIL Global Research and Analytics, Chennai: 600036, India ' Department of Management Studies, Indian Institute of Technology Madras, Chennai: 600036, India
Abstract: 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 predict the stock index returns. The performance of ARIMA-SVM, ARIMA-ANN and ARIMA-RF are compared with performance of ARIMA, SVM, ANN and RF models. The various competing models are evaluated in terms of statistical metrics and trading performance criteria via a trading strategy. The analysis shows that the hybrid ARIMA-SVM model is the best forecasting model to achieve high forecast accuracy and better returns.
Keywords: hybrid models; ARIMA; artificial neural networks; ANNs; support vector machines; SVM; random forest; forecasting; stock market trading; stock index returns; trading performance; trading strategy; stock markets.
International Journal of Banking, Accounting and Finance, 2014 Vol.5 No.3, pp.284 - 308
Accepted: 02 May 2013
Published online: 14 Aug 2014 *