Anmerkungen:
In: E Akyildirim, O Cepni, S Corbet, GS Uddin, Forecasting mid-price movement of Bitcoin futures using machine learning, Annals of Operations Research, 1-32
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments February 1, 2022 erstellt
Beschreibung:
In the aftermath of the global financial crisis and on-going COVID-19, investors face challenges in understanding price dynamics across assets. In this paper, we explore the applicability of a large scale comparison of machine learning algorithms (MLA) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intra-day data to evaluate the relative forecasting performances across various time-frequencies, ranging between 5-minutes and 60-minutes. The empirical analysis is based on six different specifications of MLA methods during periods of pandemic. The empirical results show that MLA outperforms the random walk and ARIMA forecasts in Bitcoin futures markets, which may have important implications in the decision-making process of predictability