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比特币价格效用估值与可解释交易信号

文章 arXiv papers · 作者: Yulin Liu et al.
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总结

本文提出价格效用比率，用于比较比特币市场价格与基于区块链衡量的基本效用。作者运用货币理论和比特币的 UTXO 记账结构推导这一指标，再将其与现有的市场基本面代理指标进行比较。在比特币历史数据分析中，其他指标的短期预测能力有限，而据报告，所提比率对长期收益的预测效果更好。

本文还运用机器学习考察该比率的解释作用，并据此开发自动化策略。作者报告称，该策略表现优于买入并持有和择时基准，并将该比率解释为低买高卖信号。所提供的描述没有样本日期、表现统计、交易成本假设或稳健性检验，因此仅凭此摘要无法充分评估所报告的预测和交易结果。

核心观点

  • 所提价格效用比率利用比特币区块链记账来表示基本效用。
  • 研究报告称，新比率对长期收益的预测能力强于现有代理指标。
  • 研究使用机器学习评估该比率对比特币估值信号的解释作用。
  • 据报告，基于该比率的策略表现优于买入并持有和择时基准。
  • 描述缺少评估所报告结果所需的实施和稳健性细节。

标签

加密资产链上数据机器学习统计学回测
全文
# 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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。