Cryptocurrency Investing Examined

Abstract

In this work we examine the largest 100 cryptocurrency returns 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 daily return generating process. As we examine data in the most recent year, we find that surprisingly more than one principal component appears to explain the cross-sectional variation. Second, similar to hedge fund returns, cryptocurrency returns suffer from the "beta-in-the-tails" hidden risk. Third, we find that predicting cryptocurrency movements with machine learning and artificial intelligence algorithms is marginally attractive with variation in predictability power per crypto-currency. Fourth, lower volatile cryptocurrencies 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 cryptocurrency markets are semi-strong form efficient and therefore, day trading cryptocurrencies may be very challenging.

One Cryptocurrency to Explain Them All? Understanding the Importance of Bitcoin in Cryptocurrency Returns

“…The explanatory power is greatest for the largest cryptocurrencies and increases markedly in the latter half of the sample. The proportion of variance explained by this factor appears larger and more persistent than suggested by Liew et al (2019) but is somewhat consistent with the total connectedness index of Antonakakis et al (2019). This first principal component is highly correlated with bitcoin returns, suggesting that bitcoin is a key driver of returns across cryptocurrencies.…”

“…Figure 2 shows an average closer to 65% from October 2018, with the first three PCs now accounting for 85% of variation. This seems to run counter to Liew et al (2019) but is consistent with Koutmos (2018) and Ji et al (2019) who report that return and volatility spillovers among cryptocurrencies have increased over time.…”

“…A similar picture emerges, with interdependence rising substantially during the period since 2018 and reaching a level close to 0.8. This is almost double that reported in Liew et al (2019). Earlier in the sample, we find confirmation of the wide variation in interdependence noted by Antonakakis et al (2019).…”

“…While our paper supplements the studies on cryptocurrency interconnectedness of Koutmos (2018), Antonakakis et al (2019) and Ji et al (2019), it is most related to the work of Liew et al (2019). They find that the number of factors necessary to explain returns increases over time, as the proportion of variance explained by the first principal component declines substantially.…”

Cryptocurrencies and Artificial Intelligence: Challenges and Opportunities

“…Different regression models and statistical models have been tested by researchers to fit this linear model in different ways [11], [12], [15], [17]- [19]. Simple linear time series models sometimes leave certain aspects of economic and financial data unexplained [29].…”