Using on-chain data to predict Bitcoin cycles
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Grobys, K., Näsman, S., & Sandretto, D. (2026.) Using on-chain data to predict Bitcoin cycles. Research in international business and finance, 89. https://doi.org/10.1016/j.ribaf.2026.103486
© 2026 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Pysyvä osoite
Kuvaus
There is limited literature studying the predictive power of on-chain data for Bitcoin price cycles. This paper contributes to this literature by assessing whether three on-chain, trading-based measures help predict the Bitcoin price time series across three major market cycles. We find that these indicators outperform both a buy-and-hold benchmark and random-entry strategies simulated through Monte Carlo analysis. For example, the Sharpe ratio increases from 0.45 for the buy-and-hold benchmark to 1.28 when using the Market Value to Realized Value Z-score measure. This study contributes to the literature by showing that blockchain-based behavioral data provides predictive value in decentralized markets that lack intrinsic valuation anchors. The findings also have practical implications for investors, traders, and regulators, and they challenge traditional notions of market efficiency by providing evidence of recurring behavioral patterns embedded in publicly observable blockchain activity.
Emojulkaisu
ISBN
ISSN
1878-3384
0275-5319
0275-5319
Aihealue
Kausijulkaisu
Research in international business and finance|89
OKM-julkaisutyyppi
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä (vertaisarvioitu)