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.
翻译:目前,对于加密货币资产的基本面没有令人信服的替代物。 我们提出一种新的市场对基本面,即价格对实用(PU)比率(PU)比率(PU)比率,使用独特的连锁会计方法。 然后,我们通过Bitcoin历史数据替代现有的各种基本对市场比率(Bitcoin历史数据,发现它们对短期比特币回报几乎没有什么预测力。 但是, Puber比率有效预测比替代方法更长期比比比特币回报。 此外,我们用机器学习来核查PU比率的解释性。 最后,我们提出一种由PU比率建议的自动交易战略,它比常规的买购和控股和市场刺激战略都好。 我们的研究有助于从三个方面解释融资的AI:首先,我们的市场对基本面比率基于经典的货币理论和独特的UTXO比特币会计模式,而不是临时性的; 其次,经验证据证明了该比率的买低和卖高影响; 最后,我们通过Python软件将交易算法作为开放源软件传播,这是未来研究的特殊金融研究。