Cryptocurrency Valuation: An Explainable AI Approach
This addresses the problem of cryptocurrency valuation for investors and researchers, offering an explainable AI approach with practical trading applications, though it is incremental in applying existing methods to a new domain.
The authors tackled the lack of fundamental proxies for cryptocurrency valuation by proposing a price-to-utility (PU) ratio, which effectively predicts long-term bitcoin returns and outperforms conventional trading strategies.
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.