A hybrid ARIMA-EGARCH and Artificial Neural Network model in stock market forecasting: evidence for India and the USA. (3rd April 2012)

• Record Type: Journal Article
• Authors: Kumar, Manish; Thenmozhi, M.
• Is Part Of: International journal of business and emerging markets. Volume 4:Number 2(2012)
• Page Start: 160
• Page End: 178
• Publication Date: 2012-04-03
• Subjects: ARIMA -- autoregressive integrated moving average -- ANN -- artificial neural network -- forecasting -- stock market trading; International business enterprises -- Developing countries -- Periodicals; Investments -- Developing countries -- Periodicals; Globalization -- Economic aspects -- Periodicals; 338.888172405
• Languages: English
• ISSNs: 1753-6219

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.