Blockchain, the underlying technology of Bitcoin and several other cryptocurrencies, like Ethereum, produces a massive amount of open-access data that can be analyzed, providing important information about the network’s activity and its respective token. The on-chain data have extensively been used as input to Machine Learning algorithms for predicting cryptocurrencies’ future prices; however, there is a lack of study in predicting the future behaviour of on-chain data. This study aims to show how on-chain data can be used to detect cryptocurrency market regimes, like minimum and maximum, bear and bull market phases, and how forecasting these data can provide an optimal asset allocation for long-term investors.

Predicting Cryptocurrencies Market Phases through On-Chain Data Long-Term Forecasting

Bruno Casella
Co-first
;
Lorenzo Paletto
Co-first
2023-01-01

Abstract

Blockchain, the underlying technology of Bitcoin and several other cryptocurrencies, like Ethereum, produces a massive amount of open-access data that can be analyzed, providing important information about the network’s activity and its respective token. The on-chain data have extensively been used as input to Machine Learning algorithms for predicting cryptocurrencies’ future prices; however, there is a lack of study in predicting the future behaviour of on-chain data. This study aims to show how on-chain data can be used to detect cryptocurrency market regimes, like minimum and maximum, bear and bull market phases, and how forecasting these data can provide an optimal asset allocation for long-term investors.
2023
IEEE International Conference on Blockchain and Cryptocurrency
Dubai
01/05/2023 - 05/05/2023
Proceedings of the IEEE International Conference on Blockchain and Cryptocurrency 2023
Adel Ben Mnaouer, Burkhard Stiller, Fakhreddine Karray
1
4
979-8-3503-1019-1
https://ieeexplore.ieee.org/document/10174989
Blockchain, Bitcoin, cryptocurrencies, on-chain data, artificial intelligence, prediction, asset allocation, trading rules, markets
Bruno Casella; Lorenzo Paletto
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1902652
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