Herding and feedback trading in cryptocurrency markets

This paper examines the extent to which herding and feedback trading behaviors drive price dynamics across nine major cryptocurrencies. Using sample price data from bitcoin, ethereum, XRP, bitcoin cash, EOS, litecoin, stellar, cardano and IOTA, respectively, we document heterogeneity in the types of feedback trading strategies investors utilize across markets. Whereas some cryptocurrency markets show evidence of herding, or, ‘trend chasing’, behaviors, in other markets we show evidence of contrarian-type behaviors. These findings are important because they elucidate upon, firstly, what forces drive cryptocurrency markets and, secondly, how this type of trading behavior affects autocorrelation patters for cryptocurrencies. Finally, and from our intertemporal asset pricing model, we shed new light on the observed nature of the risk-return tradeoffs for each of our sampled cryptocurrencies.

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

[2]  Enrique Sentana,et al.  Feedback Traders and Stock Return Autocorrelations: Evidence from a Century of Daily Data , 1992 .

[3]  Ying Jiang,et al.  Contrarian strategy and herding behaviour in the Chinese stock market , 2015 .

[4]  John Fry,et al.  Booms, busts and heavy-tails: The story of Bitcoin and cryptocurrency markets? , 2018, Economics Letters.

[5]  Mathias Drehmann,et al.  Herding and Contrarian Behavior in Financial Markets: An Internet Experiment , 2005 .

[6]  John L. Thompson,et al.  The Asian crisis and calendar effects on stock returns in Thailand , 2005, Eur. J. Oper. Res..

[7]  Alex Preda,et al.  Does a scopic regime produce conformism? Herding behavior among trade leaders on social trading platforms , 2017 .

[8]  Laurent Favre,et al.  The Difficulties of Measuring the Benefits of Hedge Funds , 2002 .

[9]  Ian G. McHale,et al.  Anyone for Tennis (Betting)? , 2007 .

[10]  Bartel Van de Walle,et al.  Fuzzy relations for the analysis of traders' preferences in an information market game , 2009, Eur. J. Oper. Res..

[11]  Francois R. Velde.  Bitcoin: a primer , 2013 .

[12]  D. Hirshleifer,et al.  Herd Behavior and Cascading in Capital Markets: A Review and Synthesis , 2001, SSRN Electronic Journal.

[13]  Marc Joëts,et al.  Heterogeneous Beliefs, Regret, and Uncertainty: The Role of Speculation in Energy Price Dynamics , 2013, Eur. J. Oper. Res..

[14]  Jean-Philippe Serbera,et al.  Quantifying the sustainability of Bitcoin and Blockchain , 2020, J. Enterp. Inf. Manag..

[15]  Paolo Giudici,et al.  Crypto price discovery through correlation networks , 2019, Annals of Operations Research.

[16]  R. Mehra,et al.  THE EQUITY PREMIUM A Puzzle , 1985 .

[17]  A. Lo,et al.  An Econometric Analysis of Nonsynchronous Trading , 1989 .

[18]  Daniel B. Nelson.  CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH , 1991 .

[19]  Hui Ou-Yang,et al.  Feedback Trading between Fundamental and Nonfundamental Information , 2015 .

[20]  D. Yermack.  Is Bitcoin a Real Currency? An Economic Appraisal , 2013 .

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

[22]  Amélie Charles,et al.  The day-of-the-week effects on the volatility: The role of the asymmetry , 2010, Eur. J. Oper. Res..

[23]  M. Cipriani,et al.  Estimating a Structural Model of Herd Behavior in Financial Markets , 2010, SSRN Electronic Journal.

[24]  A. Tversky,et al.  Loss Aversion in Riskless Choice: A Reference-Dependent Model , 1991 .

[25]  Gianna Figà-Talamanca,et al.  Detecting bubbles in Bitcoin price dynamics via market exuberance , 2019, Ann. Oper. Res..

[26]  Enrico Gerding,et al.  It takes all sorts: A heterogeneous agent explanation for prediction market mispricing , 2018, Eur. J. Oper. Res..

[27]  T. Chiang,et al.  An empirical analysis of herd behavior in global stock markets , 2010 .

[28]  John R. Nofsinger,et al.  Herding and Feedback Trading by Institutional and Individual Investors , 1999 .

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

[30]  H. Sabourian,et al.  Herding and Contrarian Behavior in Financial Markets , 2009 .

[31]  Dimitrios Koutmos.  An intertemporal capital asset pricing model with heterogeneous expectations , 2012 .

[32]  D. Hirshleifer,et al.  Herd Behaviour and Cascading in Capital Markets: A Review and Synthesis , 2003 .

[33]  Dimitrios Koutmos.  Is There a Positive Risk‐Return Tradeoff? A Forward‐Looking Approach to Measuring the Equity Premium , 2014 .

[34]  Marc R. Reinganum.  The anomalous stock market behavior of small firms in January: Empirical tests for tax-loss selling effects , 1983 .

[35]  Eric G. Falkenstein,et al.  Preferences for Stock Characteristics As Revealed by Mutual Fund Portfolio Holdings , 1996 .

[36]  Christoph Schumacher,et al.  Estimating risk preferences of bettors with different bet sizes , 2016, Eur. J. Oper. Res..

[37]  Jennifer Conrad,et al.  Profitability of Momentum Strategies: An Evaluation of Alternative Explanations , 2001 .

[38]  Andrea Frazzini,et al.  The Disposition E ff ect and Underreaction to News , 2006 .

[39]  Frank McGroarty,et al.  Time is money: Costing the impact of duration misperception in market prices , 2016, European Journal of Operational Research.

[40]  Greg N. Gregoriou,et al.  Risk-Adjusted Performance of Funds of Hedge Funds Using a Modified Sharpe Ratio , 2003 .

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

[42]  Gregory Koutmos.  Feedback trading and the autocorrelation pattern of stock returns: further empirical evidence , 1997 .

[43]  R. C. Merton,et al.  On Estimating the Expected Return on the Market: An Exploratory Investigation , 1980 .

[44]  I. Welch,et al.  Rational herding in financial economics , 1996 .

[45]  Gina Pieters,et al.  Financial Regulations and Price Inconsistencies across Bitcoin Markets , 2016, Inf. Econ. Policy.

[46]  Suresh Radhakrishnan,et al.  Investor Sophistication and Patterns in Stock Returns after Earnings Announcements , 2000 .

[47]  Harald Vranken,et al.  Sustainability of bitcoin and blockchains , 2017 .

[48]  P. Dent.  Animal Spirits – How Human Psychology Drives the Economy, and Why it Matters for Global Capitalism , 2010 .

[49]  D. Bernhardt,et al.  Who Herds? , 2004 .

[50]  R. Shiller.  Stock Prices and Social Dynamics , 1984 .

[51]  David Yermack,et al.  Chapter 2 – Is Bitcoin a Real Currency? An Economic Appraisal , 2015 .

[52]  G. Calvo,et al.  Rational contagion and the globalization of securities markets , 2000 .

[53]  Raimo P. Hämäläinen,et al.  Behavioural operational research: Returning to the roots of the OR profession , 2016, Eur. J. Oper. Res..

[54]  A. F. Bariviera.  The Inefficiency of Bitcoin Revisited: A Dynamic Approach , 2017, 1709.08090.

[55]  Dimitrios Koutmos,et al.  Does investor sentiment really matter , 2016 .

[56]  S. Spyrou,et al.  Bond market investor herding: Evidence from the European financial crisis , 2016 .

[57]  Aviral Kumar Tiwari,et al.  Informational efficiency of Bitcoin—An extension , 2018 .

[58]  Dimitrios Koutmos,et al.  Bitcoin returns and transaction activity , 2018, Economics Letters.

[59]  S. Williamson.  Is Bitcoin a Waste of Resources? , 2018 .

[60]  Neil Gandal,et al.  Price Manipulation in the Bitcoin Ecosystem , 2017 .

[61]  Donald B. Keim,et al.  Predicting returns in the stock and bond markets , 1986 .

[62]  Chung-Ching Tai,et al.  Improving prediction market forecasts by detecting and correcting possible over-reaction to price movements , 2019, Eur. J. Oper. Res..

[63]  Marco Cipriani,et al.  Herd Behavior in Financial Markets: An Experiment with Financial Market Professionals , 2008 .

[64]  Brett Trueman.  Analyst Forecasts and Herding Behavior , 1994 .

[65]  J. Graham.  Herding Among Investment Newsletters: Theory and Evidence , 1998 .

[66]  R. Shiller.  Speculative Prices and Popular Models , 1990 .

[67]  Bing Han,et al.  Prospect Theory, Mental Accounting, and Momentum , 2004 .

[68]  Andrew Urquhart.  Price clustering in Bitcoin , 2017 .

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

[70]  Marco Cipriani,et al.  Herd Behavior and Contagion in Financial Markets , 2008 .

[71]  Erdinc Akyildirim,et al.  Prediction of cryptocurrency returns using machine learning , 2020, Annals of Operations Research.

[72]  I. Welch.  Herding among security analysts , 2000 .

[73]  Paraskevi Katsiampa.  Volatility estimation for Bitcoin: A comparison of GARCH models , 2017 .

[74]  Andrea Prat,et al.  Information aggregation in financial markets with career concerns , 2008, J. Econ. Theory.

[75]  Eng-Tuck Cheah,et al.  Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin , 2015 .

[76]  Alexandros Kostakis,et al.  Cross-Country Effects in Herding Behaviour: Evidence from Four South European Markets , 2011 .

[77]  Alexander E. Cassuto,et al.  Herding in the Italian Stock Market: A Case of Behavioral Finance , 2004 .

[78]  Gur Huberman,et al.  Contagious Speculation and a Cure for Cancer: A Nonevent that Made Stock Prices Soar , 2001 .