加密货币估值：一种可解释人工智能方法
Cryptocurrency Valuation: An Explainable AI Approach
AI总结：
本文提出基于区块链记账的价格与效用（PU）比率，发现其能有效预测比特币长期收益，并通过机器学习验证可解释性，据此构建的自动化交易策略优于买入持有和市场择时策略。
摘要：
目前，尚无令人信服的代理指标能够反映加密货币资产的基本面。我们提出一种新的市场与基本面比率，即价格与效用（PU）比率，利用独特的区块链记账方法。随后，我们利用比特币历史数据代理各种现有的基本面与市场比率，发现它们对比特币短期收益几乎没有预测能力。然而，PU比率在预测比特币长期收益方面比替代方法更有效。此外，我们利用机器学习验证了PU比率的可解释性。最后，我们提出一种由PU比率指导的自动化交易策略，该策略优于传统的买入持有策略和市场择时策略。我们的研究从三个方面对金融领域的可解释人工智能做出贡献：第一，我们的市场与基本面比率基于经典货币理论和比特币记账独特的UTXO模型，而非临时拼凑；第二，实证证据证实了该比率低买高卖的含义；最后，我们通过Python Package Index将交易算法作为开源软件发布，以供未来研究使用，这在金融研究中是不同寻常的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
