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

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  • Manish Kumar
  • M. Thenmozhi

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.

Suggested Citation

  • Manish Kumar & M. Thenmozhi, 2012. "A hybrid ARIMA-EGARCH and Artificial Neural Network model in stock market forecasting: evidence for India and the USA," International Journal of Business and Emerging Markets, Inderscience Enterprises Ltd, vol. 4(2), pages 160-178.
    • Handle: RePEc:ids:ijbema:v:4:y:2012:i:2:p:160-178
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    Cited by:

    1. Flavio Barboza & Geraldo Nunes Silva & José Augusto Fiorucci, 2023. "A review of artificial intelligence quality in forecasting asset prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1708-1728, November.

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