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.
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Notes
Note that reliable data in the early years of Bitcoin history may be hardly retrieved, because in 2009–2010 there were not yet observers in charge for accurate data collection. Hence, we decided to completely revise and update our database including more recent data, but also discarding the pairs corresponding to the years 2009–2010. In the attempt to collect more reliable data we downloaded and compared it from the websites: https://www.blockchain.com/charts/total-bitcoinshttps://www.cryptocurrencychart.com/, https://datahub.io/cryptocurrency/bitcoin, https://www.investing.com/crypto/bitcoin/historical-data, https://finance.yahoo.com/cryptocurrencies.
In particular, see the webpage www.buybitcoinworldwide.com/stats/stock-to-flow/.
Note that 463 days is about 95% of \(4 /3\) years..
For further information on data for Bitcoin prices and the number of minted bitcoins, the reader may refer to the footnote at page 2.
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Acknowledgements
Marco Corazza and Giovanni Fasano wish to thank Istituto Nazionale di Alta Matematica (IN\(\delta \)AM), Giovanni Fasano wishes to thank Consiglio Nazionale delle Ricerche – Istituto di Ingegneria del Mare (CNR–INM), for the support they received. Giovanni Fasano also thanks his three lawyer friends Maria, Maristella and Massimo, whose valuable perspective considerably contributed to inspire the contents of this paper.
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Caliciotti, A., Corazza, M. & Fasano, G. From regression models to machine learning approaches for long term Bitcoin price forecast. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05444-w
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DOI: https://doi.org/10.1007/s10479-023-05444-w