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Next-Day Bitcoin Price Forecast

1
School of Business and Law, University of Agder, 4630 Kristiansand, Norway
2
Department of Maritime Operations, University of South-Eastern Norway, 3184 Borre, Norway
3
Taylor’s Business School, Taylor’s University, 47500 Subang Jaya, Malaysia
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2019, 12(2), 103; https://doi.org/10.3390/jrfm12020103
Received: 30 April 2019 / Revised: 10 June 2019 / Accepted: 14 June 2019 / Published: 20 June 2019
(This article belongs to the Special Issue Blockchain and Cryptocurrencies)
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Abstract

This study analyzes forecasts of Bitcoin price using the autoregressive integrated moving average (ARIMA) and neural network autoregression (NNAR) models. Employing the static forecast approach, we forecast next-day Bitcoin price both with and without re-estimation of the forecast model for each step. For cross-validation of forecast results, we consider two different training and test samples. In the first training-sample, NNAR performs better than ARIMA, while ARIMA outperforms NNAR in the second training-sample. Additionally, ARIMA with model re-estimation at each step outperforms NNAR in the two test-sample forecast periods. The Diebold Mariano test confirms the superiority of forecast results of ARIMA model over NNAR in the test-sample periods. Forecast performance of ARIMA models with and without re-estimation are identical for the estimated test-sample periods. Despite the sophistication of NNAR, this paper demonstrates ARIMA enduring power of volatile Bitcoin price prediction. View Full-Text
Keywords: ARIMA; artificial neural network; Bitcoin; cryptocurrency; static forecast ARIMA; artificial neural network; Bitcoin; cryptocurrency; static forecast
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Munim, Z.H.; Shakil, M.H.; Alon, I. Next-Day Bitcoin Price Forecast. J. Risk Financial Manag. 2019, 12, 103.

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