Cryptocurrency Valuation: An Explainable AI Approach

TL;DR

This paper introduces the PU ratio, a novel, explainable market-to-fundamental metric for Bitcoin that predicts long-term returns and guides an outperforming automated trading strategy, supported by empirical and theoretical insights.

Contribution

It proposes the PU ratio based on blockchain accounting, demonstrating its effectiveness in long-term Bitcoin return prediction and providing open-source trading algorithms.

Findings

PU ratio predicts long-term Bitcoin returns more effectively.

The trading strategy based on PU outperforms buy-and-hold.

The approach enhances explainability in cryptocurrency valuation.

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 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.