Could Factors Have Explained Cryptocurrency Risk?

  • Given the proliferation and popularity of cryptocurrencies, we test whether factors that are important in the cross section of equity returns were also important for cryptocurrencies.
  • We created a multifactor model with seven factors and found that basic price- and market-based factors such as size, volatility, beta and momentum were important in explaining systematic risk for cryptocurrencies.
  • The low-liquidity and size factors reported the highest cumulative returns over our study period and also during the last year.

With thousands of cryptocurrencies in universe and their surface in popularity as investment vehicles, the want for standardized investment tools applied to the crypto market has increased adenine well. indeed, academics have already started to apply standard asset-pricing tools, such as factor models.1 These academician studies found that some factors that are important in explaining equity returns, such as grocery store beta, size and momentum ( specifically, reversal or curtly momentum ), were besides important in explaining the hybridization section of cryptocurrency returns. To build on these studies, we created a multifactor model that contains seven technical foul factors based on price, trade volume, old age and grocery store capitalization.2 While the long time agent is not normally used in the fairness market, all the others are traditional fairness factors that have a long history of inclusion in equity models.

Factors in the Multifactor Cryptocurrency Market Model

Factor Description
Market  Intercept of the cross-sectional regression. Exposure=1 for every asset.
Age Log of days since cryptocurrency inception
Size Log of market capitalization
Liquidity Log of average traded volume over the last month
Momentum Relative strength (cumulative return over the last 90 days) and historical alpha
Beta Historical beta from the capital asset-pricing model (CAPM), applying the exponentially weighted moving average (EWMA) within a six-month window and three-month half-life
Residual Volatility Cumulative return range and daily standard deviation of returns

Alpha, beta and sigma are outputs of a previous time-series CAPM-style regression against a market-capitalization-weighted index (arbitrarily reducing weights for Bitcoin and Ethereum) comprising the top 100 cryptocurrencies. Given the high flush of concentration in the crypto market — Bitcoin comprises 44 % of the total market capitalization while Ethereum takes a contribution of 19 % — we had to determine a crypto-specific slant dodge for the regression. We investigated a variety of approaches and narrowed it down to two : equal weights and the traditional squarely beginning of market capitalization modified by randomly underweighting Bitcoin and Ethereum. Equal weights, however, gave disproportionate importance to smaller cryptos with extreme returns and underweighted Bitcoin and Ethereum besides a lot, given their importance in the opportunity universe. In the conclusion, we went with the modified square-root approach. This option ensured that the calculate factors were not dominated by the peak two coins, but alternatively represented a more divers estimate universe .

What Did We Find?

Over our sample period of July 3, 2017, to March 24, 2021, our seven-factor exemplary yielded an average R2 of 0.45, indicating these seven factors explained 45 % of the variation in the cross section of cryptocurrency returns. While this total is well within the historical range found in our equity-factor models, it should be interpreted with caution given the abruptly history of the available data and our limiting the study set to 100 cryptocurrencies. In contrast, our equity models contain thousands of stocks as region of their estimate universe and more than 20 years of historic data. In our short data history for cryptocurrencies, the characteristics that showed a distinct premium were size and low liquidity. Size and low liquidity besides provided the most-positive data ratios ( risk-adjusted reelect ), while abject momentum and low beta followed closely. Factor returns can vary over time and tend to move in cycles, but it ’ s excessively soon to tell if these factor trends will continue or move in cycles equally well .

Cumulative Returns for the Seven-Factor Model

Model estimated from July 3, 2017, to March 24, 2021. Source for prices, volume and market capitalization used as input data to the model: Coinmarketcap.com

Sample Statistics of Tested Factors

  t-stat t-stat>2  Avg Return (%)  Volatility(%)  IR Corr Mkt 
Market 5.94 72.15 89.60 82.19 1.09 1.00&
Beta 2.04  39.60  -14.91  39.30  -0.38  0.38
Age 1.55 27.55 -9.76 96.21  -0.10  -0.08
Residual Volatility 1.46 26.89 8.15 37.22 0.22 0.19
Liquidity 1.43 24.32 -56.05 41.12 -1.36 -0.13
Size 1.41 24.47 22.35 29.39 0.76 0.17
Momentum 1.37 23.81 -18.24 34.73 -0.53 -0.14

Model estimated from July 3, 2017, to March 24, 2021. Source for prices, volume and market capitalization used as input data to the model: Coinmarketcap.com In terms of explanatory baron, we found that beta and age were the strongest factors in our backtest as measured by t-statistics. notably, the old age factor besides presented the largest excitability out of all seven factors, which made it the leading candidate to explain risk of the represent coins .

Individual Coins’ Exposures to Specific Factors

As of March 24, 2021, the final day of our analyze period, we observed that Bitcoin ’ mho exposures to size, liquid, age and momentum were high compared to other cryptocurrencies, as the exchangeable values were above zero, while its exposures to residual volatility and beta were relatively low. In contrast, Dogecoin ’ south exposures to all seven factors increased steeply in 2021 as its popularity spiked, reflecting an increase in full gamble. The historic exposures of Bitcoin and Dogecoin are shown in the expose below. Knowing the exposures of an person currency or portfolio along with the factor returns allows investors to understand which risks had the most influence on the returns of individual assets in any by period.

Factor Exposures for Bitcoin and Dogecoin

Bitcoin Model estimated from July 3, 2017, to March 24, 2021. Source for prices, volume and market capitalization used as input data to the model: Coinmarketcap.com Dogecoin Model estimated from July 3, 2017, to March 24, 2021. Source for prices, volume and market capitalization used as input data to the model: Coinmarketcap.com

Factors Have Potential to Explain Cryptocurrency Risk

We found that traditional asset-pricing tools and factors showed promise in analyzing the cross section of cryptocurrency returns. furthermore, we believe these factors can provide an interest beginning approach for estimating cryptocurrency hazard and the correlations among them. In our future post in this series, we will explore the effective frontier of cryptocurrency portfolios using our prototype model. The authors thank Ian D’Souza for his contributions to this post. 1See Yukun Liu, Yukon, Tsyvinski, Aleh, and Wu, Xi. 2019. “ Common Risk Factors in Cryptocurrency. ” National Bureau of Economic Research.
Shen, Dehua, Urquhart, Andrew, and Wang, Pengfei. 2020. “ A three-factor price model for cryptocurrencies. ” University of Reading. 2It is unmanageable to define “ fundamental ” factors such as value or timbre in the cryptocurrency market.

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Further Reading

Bitcoin : good as Gold ? Foundations of Factor Investing

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