Cryptocurrency Valuation: An Explainable AI Approach

13 Pages Posted: 27 Jul 2020 Last revised: 2 Feb 2022

See all articles by Yulin Liu

Yulin Liu

Bochsler Finance & AssociƩs

Luyao Zhang

Duke Kunshan University

Date Written: July 20, 2020

Abstract

Currently, there are no convincing proxies for the fundamentals of cryptocurrency assets. We propose a new market-to-fundamental ratio, the price-to-utility (PU) ratio, utilizing unique blockchain accounting methods. We then proxy various fundamental-to-market ratios by Bitcoin historical data and find they have little predictive power for short-term bitcoin returns. However, PU ratio effectively predicts long-term bitcoin returns. We verify PU ratio valuation by unsupervised and supervised machine learning. The valuation method informs investment returns and predicts bull markets effectively. Finally, we present an automated trading strategy advised by the PU ratio that outperforms the conventional buy-and-hold and market-timing strategies. We distribute the trading algorithms as open-source software via Python Package Index for future research.
(JEL: G11, G12, G17, C55; ACM: J.4, I.2, G.4)

Keywords: Asset Valuation, Machine Learning, Bitcoin, Cryptocurrency, Market Timing, Automated Trading, Explainable AI, Store of Value, UTXO

JEL Classification: G11, G12, G17, C55

Suggested Citation

Liu, Yulin and Zhang, Luyao, Cryptocurrency Valuation: An Explainable AI Approach (July 20, 2020). Available at SSRN: https://ssrn.com/abstract=3657986 or http://dx.doi.org/10.2139/ssrn.3657986

Yulin Liu

Bochsler Finance & AssociƩs ( email )

Gotthardstrasse 26
Zug, 6300
Switzerland

Luyao Zhang (Contact Author)

Duke Kunshan University ( email )

No. 8 Duke Avenue
Kunshan, Jiangsu 215316
China

HOME PAGE: http://scholars.duke.edu/person/luyao.zhang

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