A hybrid ARIMA-EGARCH and Artificial Neural Network model in stock market forecasting: evidence for India and the USA

Manish Kumar and M. Thenmozhi

International Journal of Business and Emerging Markets, 2012, vol. 4, issue 2, 160-178

Abstract: This study develops a hybrid model that combines Autoregressive Integrated Moving Average (ARIMA), Exponential GARCH (EGARCH) and Artificial Neural Network (ANN) to predict the daily returns of S%P CNX Nifty and S%P 500 indices by modifying Zhang 's (2003) approach. The performance of the hybrid ARIMA-EGARCH-ANN model is benchmarked against the ARIMA-EGARCH and ANN models. The empirical evidence provides superiority of the hybrid ARIMA-EGARCH-ANN model in terms of the traditional forecasting accuracy measures and Sign and directional change and delivers consistent results for the two time series. This endorses hybrid model robustness and provides its practical use in formulating a strategy for trading in the S%P 500 and Nifty indices.

Keywords: ARIMA; autoregressive integrated moving average; ANN; artificial neural networks; stock market forecasting; stock market trading; India; USA; United States; hybrid modelling; stock markets. (search for similar items in EconPapers)
Date: 2012
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