Investor sentiment and bitcoin prices

被引:20
作者
Koutmos, Dimitrios [1 ]
机构
[1] Texas A&M Univ Corpus Christi, Corpus Christi, TX 78412 USA
关键词
Bitcoin; Bootstrap; Investor sentiment; Quantile regression; Robustness; ORDER IMBALANCE; RETURN; VOLATILITY; RETAIL; VOLUME;
D O I
10.1007/s11156-022-01086-4
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
Using a rich data set of transaction-level buy and sell orders from the major digital currency exchange Coinbase, we formulate a measure for investor sentiment and shed new evidence on the sentiment-return relation for bitcoin. Using a bootstrapped quantile regression procedure we show a significant and robust relation between rising sentiment and price increases, and vice versa, across the distribution of bitcoin price changes. This relation is shown to be robust when controlling for a variety of exchange-specific and blockchain-wide variables. This relation is also robust when controlling for aggregate momentum across major cryptocurrencies. This finding is important as our data sample spans a period before and after the introduction of futures markets for bitcoin, which has arguably resulted in a regime shift in the time series behavior of its price. Taken together, our results show that bitcoin prices can undergo regime changes and that conventional regression-type models that focus on the center of the distribution of bitcoin price changes can yield misleading estimates.
引用
收藏
页码:1 / 29
页数:29
相关论文
共 37 条
[31]   Predictable Return Distributions [J].
Pedersen, Thomas Q. .
JOURNAL OF FORECASTING, 2015, 34 (02) :114-132
[32]   CENSORED REGRESSION QUANTILES [J].
POWELL, JL .
JOURNAL OF ECONOMETRICS, 1986, 32 (01) :143-155
[33]  
Signer A., 2002, J ALTERN INVEST, P31, DOI DOI 10.3905/JAI.2002.319041
[34]  
Wenker N., 2014, Tex. Rev. L. & Pol., V19, P145
[35]   Are Bitcoin bubbles predictable? Combining a generalized Metcalfe's Law and the Log-Periodic Power Law Singularity model [J].
Wheatley, Spencer ;
Sornette, Didier ;
Huber, Tobias ;
Reppen, Max ;
Gantner, Robert N. .
ROYAL SOCIETY OPEN SCIENCE, 2019, 6 (06)
[36]  
Yang R., 2020, COMPUT SECUR, V97, P1
[37]  
Yermack D., 2015, Handbook of digital currency, P31, DOI [10.1016/b978-0-12-802117-0.00002-3, DOI 10.1016/B978-0-12-802117-0.00002-3, 10.1016/B978-0-12-802117-0.00002-3]