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

【Author】 Caliciotti, Andrea; Corazza, Marco; Fasano, Giovanni

【Source】ANNALS OF OPERATIONS RESEARCH

【影响因子】4.820

【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

【发表时间】2023 2023 JUL 3

【收录时间】2023-07-23

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-价格预测