论文标题
加密货币估值：可解释的AI方法
Cryptocurrency Valuation: An Explainable AI Approach
论文作者
论文摘要
目前，对于加密货币资产的基本原理尚无令人信服的代理。我们提出了一种新的市场与基本比率，即使用独特的区块链会计方法的价格与私人（PU）比率。然后，我们通过比特币历史数据代理各种现有的基本市场比率，发现它们对短期比特币回报几乎没有预测能力。但是，与替代方法相比，PU比有效地预测了长期比特币回报。此外，我们使用机器学习验证了PU比的解释性。最后，我们提出了一种自动交易策略，该策略由PU比率提出的效果优于传统的买卖和市场定位策略。我们的研究促进了三个方面的可解释金融中的AI：首先，我们的市场与基本比率基于经典的货币理论和比特币会计的独特UTXO模型，而不是临时；其次，经验证据证明了该比率的买入和卖出最高含义。最后，我们通过Python软件包索引将交易算法作为开源软件，以供未来的研究，这在财务研究中非常出色。
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.
