Bitcoin Price-to-Utility Valuation and Explainable Trading Signals

Summary

This paper introduces a price-to-utility ratio intended to compare Bitcoin’s market price with a blockchain-based measure of fundamental utility. The authors derive the measure using monetary theory and Bitcoin’s UTXO accounting structure, then compare it with existing market-to-fundamental proxies. In their historical Bitcoin analysis, the alternatives have little short-term predictive power, while the proposed ratio is reported to predict longer-term returns more effectively.

The paper also applies machine learning to examine the ratio’s explanatory role and develops an automated strategy guided by it. The authors report that this strategy outperforms buy-and-hold and market-timing benchmarks, interpreting the ratio as a buy-low, sell-high signal. The supplied description does not provide sample dates, performance statistics, transaction-cost assumptions, or robustness tests, so the reported predictive and trading results cannot be assessed fully from this summary alone.

Key ideas

• The proposed price-to-utility ratio uses Bitcoin blockchain accounting to represent fundamental utility.
• The study reports stronger long-term return prediction from the new ratio than from existing proxies.
• Machine learning is used to assess how the ratio explains Bitcoin valuation signals.
• A strategy based on the ratio is reported to outperform buy-and-hold and market-timing baselines.
• The description omits implementation and robustness details needed to judge the reported results.

Tags

# Cryptocurrency Valuation: An Explainable AI Approach


# Cryptocurrency Valuation: An Explainable AI Approach









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.