Cryptocurrency Investing Examined

Abstract

In this work we examine the largest 100 crypto-currency return series ranging from 2015 to early 2018. We concentrate our analysis on daily returns and find several interesting stylized facts. First, principal components analysis reveals a complex return generating process. As we examine our data in the most recent year, we find that surprisingly more than one principal component appears to explain the cross-sectional variation in returns. Second, similar to hedge fund returns, crypto-currency returns suffer from the “beta-in-the-tails” hidden risk. Third, we find that predicting crypto-currency movements with machine learning and artificial intelligence algorithms is marginally attractive with variation in predictability power per crypto-currency. Fourth, lower volatile crypto-currencies are slightly more predictable than more volatile ones. Fifth, evidence exists that efficacy of distinct information sets varies across machine learning algorithms, showing that predictability may be much more complex given a set of machine learning algorithms. Finally, short-term predictability is very tenuous, which suggests that near-term crypto-currency markets are semi-strong form efficient and therefore, day trading crypto-currencies may be very challenging.