Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting
Rangan Gupta () and
Stephen Miller ()
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Heni Boubaker: International University of Rabat, BEAR LAB, Technopolis Rabat-Shore Rocade Rabat-Sale, Morocco
No 202056, Working Papers from University of Pretoria, Department of Economics
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 (BP) 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 closing prices for the Dow Jones Industrial Average (DJIA) index over 01/01/2010 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)
Pages: 57 pages
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ets, nep-for and nep-ore
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Working Paper: Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:202056
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