Cryptoasset Factor Models

Algorithmic Finance 7(3-4) (2018) 87-104

45 Pages Posted: 13 Sep 2018 Last revised: 22 Apr 2019

See all articles by Zura Kakushadze

Zura Kakushadze

Quantigic Solutions LLC; Free University of Tbilisi

Date Written: September 6, 2018


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.

Keywords: cryptoasset, cryptocurrency, Bitcoin, factor, risk, return, regression, factor loading, residuals, regression coefficients, t-statistic, size, volume, volatility, momentum, minable, cross-section, time series, source code, backtesting, market cap, price, mean-reversion, statistical arbitrage, market

JEL Classification: G00, G10, G11, G12, G23

Suggested Citation

Kakushadze, Zura, Cryptoasset Factor Models (September 6, 2018). Algorithmic Finance 7(3-4) (2018) 87-104, Available at SSRN: or

Zura Kakushadze (Contact Author)

Quantigic Solutions LLC ( email )

680 E Main St #543
Stamford, CT 06901
United States
6462210440 (Phone)
6467923264 (Fax)


Free University of Tbilisi ( email )

Business School and School of Physics
240, David Agmashenebeli Alley
Tbilisi, 0159

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