Common Risk Factors In Cryptocurrency

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Common Risk Factors in Cryptocurrency Yukun Liu† Aleh Tsyvinski‡ Xi Wu§April 15, 2019AbstractWe find that three factors – cryptocurrency market, size, and momentum – capture thecross-sectional expected cryptocurrency returns. We consider a comprehensive list ofprice- and market-related factors in the stock market, and construct their cryptocurrency counterparts. Nine cryptocurrency factors form successful long-short strategiesthat generate sizable and statistically significant excess returns. We show that all ofthese strategies are accounted for by the cryptocurrency three-factor model. Wethank Nicola Borri, Markus Brunnermeier, Kent Daniel, Zhiguo He, Andrew Karolyi, Alan Kwan,Ye Li, Nikolai Roussanov, Jinfei Sheng, Michael Sockin, and Jessica Wachter for comments. We are gratefulto Colton Conley and Dean Li for their excellent research assistance.† Yale University.‡ Yale University and NBER.§ Stern School of Business at New York University.

1IntroductionThe cryptocurrency market has experienced rapid growth. This market allows companiesto raise money without engaging with venture capitalists and to be traded without beinglisted on stock exchanges. The entire set of coins in the crypto market ranges from wellknown currencies such as Bitcoin, Ripple, and Ethereum to much more obscure coins. Thereare two views on the cryptocurrency market. The first is that most and perhaps all of thecoins represent bubbles and fraud. The second is that the blockchain technology embodiedin coins may become an important innovation and that at least some coins may be assetsthat represent a stake in the future of this technology. If the latter case is true, analyzing thecryptocurrency market from the empirical asset pricing point of view is important for at leasttwo reasons. The first reason is to understand whether the returns of cryptocurrencies sharesimilarities with other asset classes, most importantly, with equities. The second reason isto establish a set of empirical regularities that can be used as stylized facts and importantinputs to assess and develop theoretical models of cryptocurrency.In this paper, we study the cross-section of cryptocurrency returns. Our primary goalis to examine this market using standard empirical asset pricing tools. We consider all ofthe coins with market capitalizations above one million dollars and their returns from thebeginning of 2014 to the end of 2018. The number of such coins grew from 109 in 2014 to1,583 in 2018.We examine whether the characteristics that are deemed important in the cross-sectionof equity returns are also present in the cryptocurrency market. We find that many of theknown characteristics in the equity market also form successful long-short trading strategiesin 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 crosssection of stock returns. Among the factors compiled by Feng, Giglio, and Xiu (2017) andChen and Zimmermann (2018), we select those that are constructed based only on priceand market information – 25 such factors in total. We first describe the construction ofthe 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 alsoconstruct a market index using all of the coins for which the data is readily available. Theindex comprises 1,707 coins weighted by their market capitalization.We then analyze the performance of all the 25 factors in the cryptocurrency market.1

Each week, we sort the returns of individual cryptocurrencies into quintile portfolios basedon the value of a given factor. We track the return of each portfolio in the week that followsand calculate the average excess return over the risk-free rate of each portfolio. We thenform 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 9out of the 25 factors. Specifically, these are: market capitalization, price, and maximumprice; one-, two-, three-, and four-week momentum; dollar volume; and standard deviationof dollar volume. We now turn to the detailed description of the results for each group offactors.For the statistically significant size related strategies, a zero-investment long-short strategy that longs the smallest coins and shorts the largest coins generates more than 3 percentexcess weekly returns (3.4 percent for the market capitalization, 3.9 percent for the end ofweek price, and 4.1 percent for the highest price of the week strategies). For the momentum strategies, a zero-investment long-short strategy that longs the coins with comparatively large price increases and shorts the coins with comparatively small increases generatesabout 3 percent excess weekly returns (2.7 percent for one-week momentum, 3.3 percent fortwo-week momentum, 4.1 percent for three-week momentum, and 2.5 percent for four-weekmomentum strategies). For the volume related strategies, a zero-investment strategy thatlongs the lowest volume coins and shorts the highest volume coins generates about 3 percent excess weekly returns (3.2 percent for the dollar volume). For the volatility strategy, azero-investment strategy that longs the lowest dollar volume volatility coins and shorts thehighest dollar volume volatility coins generates about 3 percent excess weekly returns. Forall of these factors, the returns on individual quintile portfolios are almost monotonic withthe quintiles. Determining the cryptocurrency factors that predict the cross-section of theentire cryptocurrency space is the first main result of the paper.Next, we investigate whether these nine cross-sectional cryptocurrency return predictorscan be spanned by a small number of factors. Our second main result is to develop a factormodel for the cross-section of the cryptocurrency returns. We first consider a one-factormodel 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 poorlyin pricing the cross-section of the coin returns. The alphas for most of the successful zeroinvestment strategies remain large and statistically significant. The alphas for some of thestrategies decrease marginally. The explanatory power of the model is low, with the R2 s ofthe long-short strategies ranging from about zero percent for the one-week momentum to2

6.8 percent for the maximum day price strategies.We next show that a three-factor model with the cryptocurrency market factor (CMKT),a cryptocurrency size factor (CSMB), and a cryptocurrency momentum factor (CMOM), accounts for the excess returns of all of the nine successful zero-investment strategies. Adjustedfor the cryptocurrency three-factor model, none of the alphas of the nine strategies remainsstatistically significant. The CSMB factor accounts for the following strategies: market capitalization, price, maximum day price, dollar volume, and the standard deviation of dollarvolume. The CMOM factor accounts for the two-week, three-week, and four-week momentum strategies. Both CSMB and CMOM account for the one-week momentum strategy. Weconclude that the cryptocurrency three-factor model captures the cross-section of expectedreturns of cryptocurrencies.Finally, we note several additional results. First, as the construction of the long-shortstrategies relies on the ability to short coins, a natural criticism of our findings is that shortselling is either not possible or limited for most of the coins. We thus analyze each strategythat shorts Bitcoin instead of shorting the relevant quintile portfolio. The results virtuallydo not change. Second, we find that the momentum strategies perform significantly betteramong the larger coins. The momentum strategy in the below median size group generatesstatistically insignificant 0.6 percent weekly excess returns; the momentum strategy in theabove median size group generates statistically significant 4.2 percent weekly returns. Wealso show that the stock market factor models, such as the Fama-French 3-factor, Carhart4-factor, and the Fama-French 5-factor models, do not account for the cross-section of cryptocurrency returns. Additionally, we show that the procedure that removes the unpricedrisks similar to Daniel, Mota, Rottke, and Santos (2018) strengthens the cryptocurrencysize factor but not the cryptocurrency momentum factor. One possible explanation is thatloadings on the cryptocurrency momentum factor are more transient than loadings on thecryptocurrency size factor.We briefly discuss the relationship to the literature. Size and momentum are among themost studied strategies in asset pricing. The size effect in the stock market is first documentedin Banz (1981). Fama and French (1992) show that size and value are important factorsin explaining the cross-section of expected stock returns. Our findings on momentum arerelated to many papers on the topic such as Jegadeesh and Titman (1993), Moskowitz andGrinblatt (1999), Moskowitz, Ooi, and Pedersen (2012), Asness, Moskowitz, and Pedersen(2013). The use of factor models to analyze asset returns dates back to the papers of Famaand French (1993) and Fama and French (1996). Lustig, Roussanov, and Verdelhan (2011),3

Szymanowska, De Roon, Nijman, and Van Den Goorbergh (2014), and Bai, Bali, and Wen(2018) develop factor models for the currency, commodity, and corporate bond markets,respectively.Yermack (2015) is one of the first papers that brings academic attention to the field ofcryptocurrency. A number of recent papers develop models of cryptocurrencies (see, e.g.,Weber, 2016; Biais, Bisiere, Bouvard, and Casamatta, 2018; Chiu and Koeppl, 2017; Congand He, 2018; Cong, He, and Li, 2018; Cong, Li, and Wang, 2018; Sockin and Xiong, 2018;Schilling and Uhlig, 2018; Abadi and Brunnermeier, 2018; Routledge and Zetlin-Jones, 2018).Several recent papers document empirical facts related to cryptocurrency investments (e.g.,Stoffels, 2017; Hubrich, 2017; Borri, 2018; Borri and Shakhnov, 2018a; Borri and Shakhnov,2018b; Hu, Parlour, and Rajan, 2018; Makarov and Schoar, 2018; Liu and Tsyvinski, 2018;Li and Yi, 2018).2DataWe collect trading data of all cryptocurrencies available from is a leading source of cryptocurrency price and volume data. It aggregatesinformation from over 200 major exchanges and provides daily data on opening, closing, high,low prices, volume and market capitalization (in dollars) for most of the cryptocurrencies.1For each cryptocurrency on the website, its price is calculated by taking the volume weightedaverage of all prices reported at each market. A cryptocurrency needs to meet a list of criteria to be listed, such as being traded on a public exchange with an API that reports thelast traded price and the last 24-hour trading volume, and having a non-zero trading volumeon at least one supported exchange so that a price can be determined. Coinmarketcap listsboth active and defunct cryptocurrencies, thus alleviating concerns about survivorship bias.We use daily close prices to construct weekly coin returns. Specifically, we divide eachyear into 52 weeks. The first week of the year consists of the first seven days of the year. Thefirst 51 weeks of the year consist of seven days each and the last week of the year consistsof the last eight days of the year.2 Our sample includes 1,707 coins from the beginning of2014 to the end of 2018. The trading volume data became available in the last week of 2013,and thus our sample period starts from the beginning of 2014. We require that the coinshave information on price, volume, and market capitalization. We further exclude coins1 Some2 Thecoins are not tracked by the website because the coins’ exchanges do not provide accessible APIs.last week of 2016 consists of the last nine days of the year.4

with market capitalizations of less than 1,000,000. To alleviate concerns for outliers, wewinsorize all non-return variables by the 1st and 99th percentiles each week.The summary statistics are presented in Panel A of Table 1. The number of coins in oursample that satisfy all the filters increases from 109 in 2014 to 1,583 in 2018. The mean(median) market capitalization in the sample is 356.71 (8.17) million dollars. The mean(median) daily dollar volume in our sample is 18,305.83 (103.89) thousand dollars.We construct a cryptocurrency market index as the value-weighted price of all the underlying available coins. The cryptocurrency excess market return (CMKT) is constructed asthe difference between the cryptocurrency market index return and the risk-free rate measured as the one-month Treasury bill rate. The summary statistics are presented in Panel Bof Table 1. During the sample period, the average coin market index return is 1.3 percentper week, which is higher than the average Bitcoin return (1.2 percent per week) but is lowerthan the average Ripple return (3.5 percent per week) or Ethereum return (4.6 percent perweek).3 The weekly standard deviation of the coin market index return is 0.117, which isslightly higher than that of Bitcoin (0.114) but much lower than those of Ripple (0.267)and Ethereum (0.241). The coin market index returns have positive skewness and kurtosis.Figure 1 plots the cryptocurrency market index against Bitcoin, Ripple, and Ethereum. Thevalues are presented as the US dollar value of investing one dollar from the inception of thegiven cryptocurrency to facilitate comparisons. The Figure shows strong correlations amongthe cryptocurrency market index and the investment values of the major coins.We obtain the stock market factors for the Fama French 3-factor, Carhart 4-factor, andFama French 5-factor models from Kenneth French’s website.3 Bitcoin,Ripple, and Ethereum are the three largest cryptocurrencies by market capitalization and thusform a natural reference group.5

Table 1: Summary StatisticsPanel A reports the number of coins, the mean and median of market capitalization, and the mean andmedian of daily trading dollar volume by year. Panel B reports the characteristics of coin market indexreturns, Bitcoin returns, Ripple returns, and Ethereum returns. The coin market index returns, Bitcoinreturns, and Ripple returns start from the first week of 2014. The Ethereum returns start from the thirtysecond week of 2015.Panel AMarket Cap (mil)MeanMedianYearNumber of 3.56124.02Full1,707356.718.1718,305.83103.89Panel BMean MedianCoin Market ReturnBitcoin ReturnRipple ReturnEthereum lume 0.2410.2940.3673.4781.8414.5744.58021.2639.843

Figure 1: Cryptocurrency Market Index and Major CoinsThis figure plots the cryptocurrency market index against Bitcoin, Ripple, and Ethereum.7

3Cross-Sectional FactorsWe consider a comprehensive list of the established factors in the cross-section of stock returns, compiled by Feng, Giglio, and Xiu (2017) and Chen and Zimmermann (2018). Amongthese, we select all the factors that can be directly constructed using only the informationon price, volume, and market capitalization. The reason we consider only the market-basedfactors is that financial and accounting data for the cross-section of coins is either not readilyavailable or not applicable. We hence investigate 25 factors, which we present in Table 2.We further group them into four broad categories: size, momentum, volume, and volatility.3.1Size FactorsWe analyze the performance of the zero-investment long-short strategies based on thesize-related factors: market capitalization, price, maximum price, and age. Each week, wesort 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. We then calculate the averageexcess returns over the risk-free rate of each portfolio, and the excess returns of the longshort strategies based on the difference between the fifth and the first quintiles. We findthat the first three factors generate statistically significant long-short strategy returns. Theresult of the zero-investment long-short strategy for age is not statistically significant and issummarized in the last part of the section.Table 3 presents the results. For the first three factors, the average mean excess returnsdecrease from the top to the bottom quintiles. The differences in the average returns of thehighest and lowest quintiles are -3.4 percent for market capitalization, -3.9 percent for theend of week price, and -4.1 percent for the highest price of the week. All of these differencesare statistically significant at the 5 percent level. In other words, a zero-investment strategythat longs the smallest coins and shorts the largest coins generates about 3 percent excessweekly returns. Of course, this strategy does not take into account trading costs and thefeasibility of short selling. We consider strategies that short Bitcoin, and present results thatlong the smallest coins and short Bitcoin in Section 5. In the Appendix, we also presentresults based on tercile instead of quintile portfolios.4 The results based on tercile portfolios4 Thesame robustness results are presented for all other strategies in the Appendix.8

are qualitatively similar.Table 2: Factor olumeVolumeVolumeFactorMCAPPRCMAXDPRCAGEr 1,0r 2,0r 3,0r 4,0r 8,0r 16,0r 50,0r VolatilityVolatilitySTDPRCVOLDAMIHUDDefinitionLog last day market capitalization in the portfolio formation weekLog last day price in the portfolio formation weekThe maximum price of the portfolio formation weekThe number of weeks that have been listed on Coinmarketcap.comOne-week momentumTwo-week momentumThree-week momentumFour-week momentumEight-week momentumSixteen-week momentumFifty-week momentumHundred-week momentumLog average daily volume in the portfolio formation weekLog average daily volume times price in the portfolio formation weekLog average daily volume times price scaled by market capitalization inthe portfolio formation weekiiiThe regression coefficient βCM KT in Ri R f α βC M KT CMKT i .The model is estimated using daily returns of the previous 365 daysbefore the formation week.Beta squaredThe idiosyncratic volatility is measured as the standard deviation ofithe residual after estimating Ri R f αi βCM KT CMKT i . Themodel is estimated using daily returns of the previous 365 days beforethe formation week.The standard deviation of daily returns in the portfolio formation weekThe skewness of daily returns in the portfolio formation weekThe kurtosis of daily returns in the portfolio formation weekMaximum daily return of the portfolio formation weekThe improvement of R2 iniiiRi R f αi βCM KT CMKT βC M KT 1 CMKT 1 βC M KT 2 CMKT 2 i ,where CMKT 1 and CMKT 2 are the lagged one and two day coinmarket index returns, compared to using only current coin marketexcess returns. The model is estimated using daily returns of theprevious 365 days before the formation week.Log standard deviation of dollar volume in the portfolio formation weekThe average absolute daily return divided by dollar volume in theportfolio formation week9

Table 3: Size Factor ReturnsThis table reports the mean quintile portfolio returns based on

excess returns over the risk-free rate of each portfolio, and the excess returns of the long- . Journal of Financial Economics, Journal of Financial Markets Journal of Financial Economics. Journal of Financial Economics. Journal of Financial Economics Journal of Financial Economics Journal of Financial Economics Journal of Financial Economics .

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