When Early Adopters Learn From the Followers: The Cryptocurrency Return Predictability of GBTC Discount and Premium

Lei Huang, Tse-Chun Lin, Fangzhou Lu
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Abstract

We show that change in Grayscale Bitcoin Trust premium is the single most significant predictor of Bitcoin daily return. This sentiment measure is similar to the closed-end fund discount measure as in Baker and Wurgler (2006), but more likely to reflect the excess demand from traditional investors than from blockchain specialists. Although there is a substantial variation in Bitcoin price quotes worldwide, this Grayscale premium and discount predict Bitcoin daily return for the most liquid Bitcoin exchanges. Using K-means clustering and LDA analysis, we find that this predictability is especially significant when there is a large variation in bullish and bearish market sentiment, innovation regarding CBDC, regulations on crypto exchanges, but not when there is innovation regarding blockchain technology or bitcoin mining. A simple long and short strategy based on this signal generates a daily alpha of 40 bps. These findings suggest that Bitcoin prices react with a delay to the information contained in the sentiment of traditional investors and investors who are constrained from directly holding Bitcoin.
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当早期采用者向追随者学习:GBTC折扣和溢价的加密货币回报可预测性
我们表明,灰度比特币信托溢价的变化是比特币日收益的唯一最重要的预测指标。这种情绪度量类似于Baker和Wurgler(2006)中的封闭式基金折扣度量,但更有可能反映传统投资者而不是区块链专家的过度需求。尽管全球范围内的比特币报价存在很大差异,但这种灰度溢价和折扣预测了最具流动性的比特币交易所的比特币每日回报。使用K-means聚类和LDA分析,我们发现,当看涨和看跌市场情绪、CBDC创新、加密交易所监管存在较大变化时,这种可预测性尤其显著,但当区块链技术或比特币挖矿存在创新时,这种可预测性就不那么重要了。一个基于这个信号的简单的多头和空头策略会产生40个基点的日alpha值。这些发现表明,比特币价格对包含在传统投资者和受限制不能直接持有比特币的投资者情绪中的信息有延迟反应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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