Pairs Trading in Cryptocurrency Markets

Pairs trading is a strategy based on exploiting mean reversion in prices of securities. Even though these strategies have been shown to perform well for equities, their performance is unknown for the field of cryptocurrencies, usually perceived as inefficient and predictable. We apply the distance and cointegration methods to a basket of 26 liquid cryptocurrencies traded on the Binance exchange, specifically at 5-minute, 1-hour and daily frequencies. In our backtests, the strategies underperform classical benchmarks. However, the results are quite sensitive to parameter settings and external factors such as transaction costs or execution windows. Higher-frequency trading delivers significantly better performance, and while the most common daily distance method returns −0.07% monthly, this increases to 11.61% monthly for 5-minute frequency. Additionally, we find evidence of simple mean-reverting behavior in intraday prices that is missing in daily data, and which provides further support for the inefficiency of cryptocurrency markets.

[1]  Bao Rong Chang,et al.  An Intelligent Model for Pairs Trading Using Genetic Algorithms , 2015, Comput. Intell. Neurosci..

[2]  Khalil Taheri,et al.  Pairs trading strategy optimization using the reinforcement learning method: a cointegration approach , 2016, Soft Computing.

[3]  Satoshi Nakamoto Bitcoin : A Peer-to-Peer Electronic Cash System , 2009 .

[4]  Ahmet Sensoy,et al.  The Effectiveness of Technical Trading Rules in Cryptocurrency Markets , 2019, Finance Research Letters.

[5]  Asymmetric mean reversion of Bitcoin price returns , 2020 .

[6]  Ladislav Kristoufek,et al.  On Bitcoin markets (in)efficiency and its evolution , 2018, Physica A: Statistical Mechanics and its Applications.

[7]  Hio Loi The Liquidity of Bitcoin , 2017 .

[8]  Roland Mestel,et al.  Price discovery of cryptocurrencies: Bitcoin and beyond , 2018 .

[9]  Turan G. Bali,et al.  Do Hedge Funds Outperform Stocks and Bonds? , 2012, Manag. Sci..

[10]  Ladislav Kristoufek,et al.  What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis , 2014, PloS one.

[11]  Eng-Tuck Cheah,et al.  Negative bubbles and shocks in cryptocurrency markets , 2016 .

[12]  Yuan Wu,et al.  Pairs trading: A copula approach , 2013 .

[13]  Timofei Bogomolov,et al.  Pairs trading based on statistical variability of the spread process , 2013 .

[14]  W. Li,et al.  A single-stage approach for cointegration-based pairs trading , 2017, Finance Research Letters.

[15]  Tim Leung,et al.  Constructing Cointegrated Cryptocurrency Portfolios for Statistical Arbitrage , 2018, Studies in Economics and Finance.

[16]  E. Fama,et al.  Permanent and Temporary Components of Stock Prices , 1988, Journal of Political Economy.

[17]  E. Fama EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[18]  Hossein Rad,et al.  The Profitability of Pairs Trading Strategies: Distance, Cointegration, and Copula Methods , 2015 .

[19]  A. Menkveld High frequency trading and the new market makers , 2013 .

[20]  Johannes Stübinger,et al.  Pairs trading with a mean-reverting jump–diffusion model on high-frequency data , 2018 .

[21]  A. Tourin,et al.  Model-based pairs trading in the bitcoin markets , 2017 .

[22]  Tyler Moore,et al.  Beware the Middleman: Empirical Analysis of Bitcoin-Exchange Risk , 2013, Financial Cryptography.

[23]  Seong‐Min Yoon,et al.  Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets , 2018, Finance Research Letters.

[24]  E. Fama,et al.  Common risk factors in the returns on stocks and bonds , 1993 .

[25]  Fernando Henrique de Paula e Silva Mendes,et al.  Testing for mean reversion in Bitcoin returns with Gibbs-sampling-augmented randomization , 2020 .

[26]  Alfonso Gómez-Espinosa,et al.  Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning , 2019, Entropy.

[27]  Feng Fu,et al.  Sentiment-Based Prediction of Alternative Cryptocurrency Price Fluctuations Using Gradient Boosting Tree Model , 2018, Front. Phys..

[28]  R. Faff,et al.  Does Simple Pairs Trading Still Work? , 2010 .

[29]  P. Samuelson Proof that Properly Anticipated Prices Fluctuate Randomly , 2015 .

[30]  S. Corbet,et al.  Datestamping the Bitcoin and Ethereum Bubbles , 2017, Finance Research Letters.

[31]  Nicolas Huck,et al.  Pairs trading and selection methods: is cointegration superior? , 2015 .

[32]  T. Leirvik,et al.  Efficiency in the Markets of Crypto-Currencies , 2019 .

[33]  R. Faff,et al.  Are Pairs Trading Profits Robust to Trading Costs , 2012 .

[34]  Ahmet Sensoy,et al.  The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies , 2019, Finance Research Letters.

[35]  W. Fung,et al.  The risk in hedge fund strategies: Theory and evidence from long/short equity hedge funds , 2011 .

[36]  Christopher Krauss,et al.  Statistical Arbitrage in Cryptocurrency Markets , 2019, Journal of Risk and Financial Management.

[37]  Hélyette Geman,et al.  Intraday pairs trading strategies on high frequency data: the case of oil companies , 2017 .

[38]  Mark C. Hutchinson,et al.  Pairs trading in the UK equity market: risk and return , 2014 .

[39]  Christopher Krauss,et al.  Statistical Arbitrage Pairs Trading Strategies: Review and Outlook , 2017 .

[40]  A. Kyle,et al.  The Flash Crash: High-Frequency Trading in an Electronic Market , 2017 .

[41]  Niranjan Sapkota,et al.  Technical trading rules in the cryptocurrency market , 2020 .

[42]  Robert Hudson,et al.  Technical trading and cryptocurrencies , 2019, Annals of Operations Research.

[43]  J. E. Trinidad-Segovia,et al.  Introducing Hurst exponent in pair trading , 2017 .

[44]  Andrea Baronchelli,et al.  Machine Learning the Cryptocurrency Market , 2018, Complex..

[45]  P. Ciaian,et al.  The economics of BitCoin price formation , 2014, 1405.4498.

[46]  Nicolas Huck The high sensitivity of pairs trading returns , 2013 .

[47]  William N. Goetzmann,et al.  Pairs Trading: Performance of a Relative Value Arbitrage Rule , 1998 .

[48]  Martin Weber,et al.  On the Determinants of Pairs Trading Profitability , 2014 .

[49]  Narasimhan Jegadeesh,et al.  Evidence of Predictable Behavior of Security Returns , 1990 .

[50]  G. D'Avolio,et al.  The Market for Borrowing Stock , 2002 .

[51]  S. Basu,et al.  Investment Performance of Common Stocks in Relation to their Price-Earnings Ratios , 1977 .

[52]  C. Granger,et al.  Co-integration and error correction: representation, estimation and testing , 1987 .

[53]  Seong‐Min Yoon,et al.  Why cryptocurrency markets are inefficient: The impact of liquidity and volatility , 2020 .

[54]  A. Sensoy,et al.  Intraday efficiency-frequency nexus in the cryptocurrency markets , 2020 .

[55]  C. Granger,et al.  Efficient Market Hypothesis and Forecasting , 2002 .

[56]  G. Vidyamurthy Pairs Trading: Quantitative Methods and Analysis , 2004 .

[57]  Ladislav Kristoufek,et al.  BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era , 2013, Scientific Reports.

[58]  E. Fama The Behavior of Stock-Market Prices , 1965 .

[59]  Xiaowu Zhu,et al.  Cryptocurrency momentum effect: DFA and MF-DFA analysis , 2019, Physica A: Statistical Mechanics and its Applications.

[60]  Wang Chun Wei Liquidity and market efficiency in cryptocurrencies , 2018, Economics Letters.

[61]  Denise Gorse,et al.  Predicting cryptocurrency price bubbles using social media data and epidemic modelling , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[62]  W. P. Malcolm,et al.  Pairs trading , 2005 .

[63]  Niall O’Sullivan,et al.  High-Frequency Equity Pairs Trading: Transaction Costs, Speed of Execution, and Patterns in Returns , 2010, The Journal of Trading.

[64]  Andrew Urquhart The Inefficiency of Bitcoin , 2016 .