From regression models to machine learning approaches for long term Bitcoin price forecast

Andrea Caliciotti (), Marco Corazza () and Giovanni Fasano ()
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Andrea Caliciotti: University of Rome “La Sapienza”
Marco Corazza: Ca’ Foscari University of Venice
Giovanni Fasano: Ca’ Foscari University of Venice

Annals of Operations Research, 2024, vol. 336, issue 1, No 12, 359-381

Abstract: Abstract We carry on a long term analysis for Bitcoin price, which is currently among the most renowned crypto assets available on markets other than Forex. In the last decade Bitcoin has been under spotlights among traders all world wide, both because of its nature of pseudo–currency and for the high volatility its price has frequently experienced. Considering that Bitcoin price has earned over five orders of magnitude since 2009, the interest of investors has been increasingly motivated by the necessity of accurately predicting its value, not to mention that a comparative analysis with other assets as silver and gold has been under investigation, too. This paper reports two approaches for a long term Bitcoin price prediction. The first one follows more standard paradigms from regression and least squares frameworks. Our main contribution in this regard fosters conclusions which are able to justify the cyclic performance of Bitcoin price, in terms of its Stock–to–Flow. Our second approach is definitely novel in the literature, and indicates guidelines for long term forecasts of Bitcoin price based on Machine Learning (ML) methods, with a specific reference to Support Vector Machines (SVMs). Both these approaches are inherently data–driven, and the second one does not require any of the assumptions typically needed by solvers for classic regression problems.

Keywords: Bitcoin; Forecast; Least squares problems; Regression; Support vector machines; Bootstrap (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-023-05444-w

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