A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting

Heni Boubaker, Giorgio Canarella, Rangan Gupta and Stephen Miller
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Heni Boubaker: Institute of High Commercial Studies (IHEC) of Sousse, LaREMFiQ
Giorgio Canarella: University of Nevada, Las Vegas

Computational Economics, 2023, vol. 62, issue 4, No 16, 1843 pages

Abstract: Abstract This paper proposes a hybrid modelling approach for forecasting returns and volatilities of the stock market. The model, called ARFIMA-WLLWNN model, integrates the advantages of the ARFIMA model, the wavelet decomposition technique (namely, the discrete MODWT with Daubechies least asymmetric wavelet filter) and artificial neural network (namely, the LLWNN neural network). The model develops through a two-phase approach. In phase one, a wavelet decomposition improves the forecasting accuracy of the LLWNN neural network, resulting in the Wavelet Local Linear Wavelet Neural Network (WLLWNN) model. The Back Propagation and Particle Swarm Optimization (PSO) learning algorithms optimize the WLLWNN structure. In phase two, the residuals of an ARFIMA model of the conditional mean become the input to the WLLWNN model. The hybrid ARFIMA-WLLWNN model is evaluated using daily returns of the Dow Jones Industrial Average index over 01/05/2010 to 02/11/2020. The experimental results indicate that the PSO-optimized version of the hybrid ARFIMA-WLLWNN outperforms the LLWNN, WLLWNN, ARFIMA-LLWNN, and the ARFIMA-HYAPARCH models and provides more accurate out-of-sample forecasts over validation horizons of one, five and twenty-two days.

Keywords: Wavelet decomposition; WLLWNN; Neural network; ARFIMA; HYGARCH (search for similar items in EconPapers)
JEL-codes: C45 C58 G17 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Working Paper: Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting (2020)
Working Paper: Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting (2020)
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DOI: 10.1007/s10614-022-10320-z

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