Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models

Manish Kumar and M. Thenmozhi

International Journal of Banking, Accounting and Finance, 2014, vol. 5, issue 3, 284-308

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. (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (7)

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