Affiliations: [a] Quantigic® Solutions LLC, Stamford, CT2 | [b] Free University of Tbilisi, Business School and School of Physics, Tbilisi, Georgia
Correspondence:
[*]
Corresponding author: Corresponding author: Zura Kakushadze, Ph.D., is the President and CEO of Quantigic® Solutions LLC, and a Full Professor at Free University of Tbilisi. (Quantigic® Solutions LLC, 1127 High Ridge Road, #135, Stamford, CT 06905. E-mail: zura@quantigic.com. Free University of Tbilisi, Business School & School of Physics, 240, David Agmashenebeli Alley, Tbilisi, 0159, Georgia.)
Note: [1] This address is used by the corresponding author for no purpose other than to indicate his professional affiliation as is customary in publications. In particular, the contents of this paper are not intended as an investment, legal, tax or any other such advice, and in no way represent views of Quantigic® Solutions LLC, the website www.quantigic.com or any of their other affiliates.
Note: [2] By “cryptoassets” here we mean digital cryptography-based assets such as cryptocurrencies (e.g., Bitcoin), as well as the plethora of various other digital “coins” and “tokens” (minable as well as non-minable) that have arisen in the recent years. For our specific purposes here, all digital assets that have data on https://coinmarketcap.com a priori are included in “cryptoassets”.
Abstract: We propose factor models for the cross-section of daily cryptoasset returns and provide source code for data downloads, computing risk factors and backtesting them out-of-sample. In “cryptoassets” we include all cryptocurrencies and a host of various other digital assets (coins and tokens) for which exchange market data is available. Based on our empirical analysis, we identify the leading factor that appears to strongly contribute into daily cryptoasset returns. Our results suggest that cross-sectional statistical arbitrage trading may be possible for cryptoassets subject to efficient executions and shorting.
