Next-day Bitcoin price forecast
Ziaul Haque Munim 1,2,*, Mohammad Hassan Shakil 3 and Ilan Alon 1
1 School of Business and Law, University of Agder, 4630 Kristiansand, Norway; firstname.lastname@example.org
2 Department of Maritime Operations, University of South-Eastern Norway, 3184 Borre, Norway; email@example.com
3 Taylor’s Business School, Taylor’s University, 47500 Subang Jaya, Malaysia; firstname.lastname@example.org
* Correspondence: email@example.com
- An application of ARIMA and ANN models in bitcoin price forecasting.
- We show that model re-estimation using extending training sample for each step ahead forecast does not improve forecast performance.
- A sensitivity analysis on the impact of lag length on the forecast performance for ANN models.
- Increasing lag length not necessarily improves forecast performance of ANN models.
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.
Keywords: ARIMA; artificial neural network; Bitcoin; cryptocurrency; static forecast
Cite as: Munim, Z. H., Shakil, M. H., & Alon, I. (2019). Next-Day Bitcoin Price Forecast. Journal of Risk and Financial Management, 12(2), 103.