Next-Day Bitcoin Price Forecast

Ziaul Haque Munim, Mohammad Hassan Shakil, Ilan Alon

Research output: Contribution to journalArticlepeer-review

65 Scopus citations


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.

Original languageEnglish
Article number103
JournalJournal of Risk and Financial Management
Issue number2
StatePublished - Jun 2019
Externally publishedYes


  • Bitcoin
  • artificial neural network
  • cryptocurrency
  • static forecast


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