Cryptocurrency Valuation: An Explainable AI Approach. (arXiv:2201.12893v7 [econ.GN] UPDATED)


Cryptocurrency Valuation: An Explainable AI Approach. (arXiv:2201.12893v7 [econ.GN] UPDATED)
By: <a href="http://arxiv.org/find/econ/1/au:+Liu_Y/0/1/0/all/0/1">Yulin Liu</a>, <a href="http://arxiv.org/find/econ/1/au:+Zhang_L/0/1/0/all/0/1">Luyao Zhang</a> Posted: June 23, 2023

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 existing 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 than
alternative methods. Furthermore, we verify the explainability of PU ratio
using machine learning. Finally, we present an automated trading strategy
advised by the PU ratio that outperforms the conventional buy-and-hold and
market-timing strategies. Our research contributes to explainable AI in finance
from three facets: First, our market-to-fundamental ratio is based on classic
monetary theory and the unique UTXO model of Bitcoin accounting rather than ad
hoc; Second, the empirical evidence testifies the buy-low and sell-high
implications of the ratio; Finally, we distribute the trading algorithms as
open-source software via Python Package Index for future research, which is
exceptional in finance research.

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