Does Dispersed Sentiment Drive Returns, Turnover, and Volatility for Bitcoin?

Ilja Kantorovitch, J. Heineken
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Abstract

We test the theoretical predictions of the differences-of-opinion literature by analyzing the extensive online discussion on Bitcoin to build a time-varying sentiment distribution, defining disagreement as dispersion in sentiment. High disagreement is associated with negative returns, high turnover growth, and high volatility, confirming the theory's predictions. However, we do not find that an increase in disagreement increases the price, which is seemingly at odds with the theoretical prediction of disagreement leading to overpricing. As the theory predicts, the disagreement effect weakens significantly after shorting instruments were introduced at the end of 2017. Our results are economically significant: at the monthly frequency, a one standard deviation increase in disagreement leads to a 9.2 percentage points lower cumulative return over the following eight months, and the adjusted R2 of regressing contemporaneous returns on average sentiment and disagreement is 0.33.
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分散的情绪驱动比特币的回报、成交量和波动性吗?
我们通过分析关于比特币的广泛在线讨论来测试意见分歧文献的理论预测,以建立时变的情绪分布,将分歧定义为情绪的分散。高分歧与负回报、高营业额增长和高波动性相关,证实了该理论的预测。然而,我们没有发现分歧的增加会增加价格,这似乎与理论预测的分歧导致定价过高不一致。正如理论预测的那样,在2017年底引入卖空工具后,分歧效应显著减弱。我们的结果具有经济意义:在每月频率下,分歧增加一个标准差导致接下来八个月的累积回报降低9.2个百分点,并且平均情绪和分歧的回归同期回报的调整R2为0.33。
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