COMMON RISK FACTORS IN CRYPTOCURRENCY

1 Introduction
The cryptocurrency market has experienced rapid growth. This market allows companies
to raise money without engaging with venture capitalists and to be traded without being
listed on stock exchanges. The entire set of coins in the crypto market ranges from well-

known currencies such as Bitcoin, Ripple, and Ethereum to much more obscure coins. There
are two views on the cryptocurrency market. The first is that most and perhaps all of the
coins represent bubbles and fraud. The second is that the blockchain technology embodied
in coins may become an important innovation and that at least some coins may be assets
that represent a stake in the future of this technology. If the latter case is true, analyzing the
cryptocurrency market from the empirical asset pricing point of view is important for at least
two reasons. The first reason is to understand whether the returns of cryptocurrencies share
similarities with other asset classes, most importantly, with equities. The second reason is
to establish a set of empirical regularities that can be used as stylized facts and important
inputs to assess and develop theoretical models of cryptocurrency.
In this paper, we study the cross-section of cryptocurrency returns. Our primary goal
is to examine this market using standard empirical asset pricing tools. We consider all of
the coins with market capitalizations above one million dollars and their returns from the
beginning of 2014 to the end of 2018. The number of such coins grew from 109 in 2014 to
1,583 in 2018.
We examine whether the characteristics that are deemed important in the cross-section
of equity returns are also present in the cryptocurrency market. We find that many of the
known characteristics in the equity market also form successful long-short trading strategies
in the cross-section of cryptocurrencies. In particular, three factors – cryptocurrency market,
size, and momentum – capture most of the cross-sectional expected returns.
The literature on the stock market established a number of factors that explain the
cross-section of stock returns. Among the factors compiled by Feng et al. (2017) and Chen
and Zimmermann (2018), we select those that are constructed based only on price and
market information – 25 such factors in total. We first describe the construction of the
cryptocurrency counterparts for all these factors in the cross-section of cryptocurrencies.
There are broadly four groups of factors: size, momentum, volume, and volatility. We also
construct a coin market return using all of the coins for which the data is readily available.
The coin market return series comprises 1,707 coins weighted by their market capitalization.
We then analyze the performance of all the 25 factors in the cryptocurrency market.
Each week, we sort the returns of individual cryptocurrencies into quintile portfolios based
on the value of a given factor. We track the return of each portfolio in the week that follows
and calculate the average excess return over the risk-free rate of each portfolio. We then
form the long-short strategy based on the difference between the fifth and the first quintiles.
We find that the returns of the zero-investment strategies are statistically significant for 9
out of the 25 factors. Specifically, these are: market capitalization, price, and maximum
price; one-, two-, three-, and four-week momentum; dollar volume; and standard deviation
of dollar volume. We now turn to the detailed description of the results for each group of
factors.
For the statistically significant size related strategies, a zero-investment long-short strat-

egy that longs the smallest coins and shorts the largest coins generates more than 3 percent
excess weekly returns (3.4 percent for the market capitalization, 3.9 percent for the end of
week price, and 4.1 percent for the highest price of the week strategies). For the momen-

tum strategies, a zero-investment long-short strategy that longs the coins with comparatively large price increases and shorts the coins with comparatively small increases generates
about 3 percent excess weekly returns (2.7 percent for one-week momentum, 3.3 percent for
two-week momentum, 4.1 percent for three-week momentum, and 2.5 percent for four-week
momentum strategies). For the volume related strategies, a zero-investment strategy that
longs the lowest volume coins and shorts the highest volume coins generates about 3 per-

cent excess weekly returns (3.2 percent for the dollar volume). For the volatility strategy, a
zero-investment strategy that longs the lowest dollar volume volatility coins and shorts the
highest dollar volume volatility coins generates about 3 percent excess weekly returns. For
all of these factors, the returns on individual quintile portfolios are almost monotonic with
the quintiles. Determining the cryptocurrency factors that predict the cross-section of the
entire cryptocurrency space is the first main result of the paper.
Next, we investigate whether these nine cross-sectional cryptocurrency return predictors
can be spanned by a small number of factors. Our second main result is to develop a factor
model for the cross-section of the cryptocurrency returns. We first consider a one-factor
model with the coin market factor only. This is, in essence, a cryptocurrency CAPM model.
The results are similar to those found in other asset classes – the model performs poorly
in pricing the cross-section of the coin returns. The alphas for most of the successful zero-

investment strategies remain large and statistically significant. The alphas for some of the
strategies decrease marginally. The explanatory power of the model is low, with the R2s of
the long-short strategies ranging from about zero percent for the one-week momentum to

COMMON RISK FACTORS IN CRYPTOCURRENCY