We find that three factors—cryptocurrency market, size, and momentum—capture the cross-sectional expected cryptocurrency returns. We consider a comprehensive list of price- and market-related return predictors in the stock market and construct their cryptocurrency counterparts. Ten cryptocurrency characteristics form successful long-short strategies that generate sizable and statistically significant excess returns, and we show that all of these strategies are accounted for by the cryptocurrency three-factor model. Lastly, we examine potential underlying mechanisms of the cryptocurrency size and momentum effects.
This paper identifies the bitcoin investor network and studies the relationship between connections and returns. Using transaction data recorded in the bitcoin blockchain from 2015 to 2020, we reach three conclusions. First, connectedness is not strongly correlated with higher returns in the first four years. However, the correlation becomes strong and significant in 2019 and Second, returns also differ among those connected addresses. By dividing the connected addresses into ten decile groups based on their centrality, we find that the top 20% most connected addresses earn higher returns than their peers during most of our sample period. Third, eigenvector centrality is more related to higher returns than degree centrality for the top 20% most-connected addresses, implying that the quality of connections may matter more than quantity among those highly connected addresses.
The Role of Cryptocurrencies in Investor Portfolios Megan Czasonis, Mark Kritzman, Baykan Pamir, and David TurkingtonMIT Sloan School Working Paper 6418-21A version of this paper can be found hereWant to read our summaries of academic finance [...]
In this blog we discuss the academic research surrounding the question of cryptocurrency liquidity. How to Measure the Liquidity of Cryptocurrency? Brauneis, Mestel , Riordan and TheissenJournal of Banking and Finance, 2021A version of this [...]