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On the determinants of pairs trading profitability

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  • Jacobs, Heiko
  • Weber, Martin

Abstract

We perform a large-scale empirical analysis of pairs trading, a popular relative-value arbitrage approach. We start with a cross-country study of 34 international stock markets and uncover that abnormal returns are a persistent phenomenon. We then construct a comprehensive U.S. data set to explore the sources behind the puzzling profitability in more depth. Our findings indicate that the type of news leading to pair divergence, the dynamics of investor attention as well as the dynamics of limits to arbitrage are important drivers of the strategy׳s time-varying performance.

Suggested Citation

  • Jacobs, Heiko & Weber, Martin, 2015. "On the determinants of pairs trading profitability," Journal of Financial Markets, Elsevier, vol. 23(C), pages 75-97.
  • Handle: RePEc:eee:finmar:v:23:y:2015:i:c:p:75-97
    DOI: 10.1016/j.finmar.2014.12.001
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    Cited by:

    1. Hain, Martin & Hess, Julian & Uhrig-Homburg, Marliese, 2018. "Relative value arbitrage in European commodity markets," Energy Economics, Elsevier, vol. 69(C), pages 140-154.
    2. Christian Finke & Florian Weigert, 2017. "Does Foreign Information Predict the Returns of Multinational Firms Worldwide?," Review of Finance, European Finance Association, vol. 21(6), pages 2199-2248.
    3. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    4. Matthew Clegg & Christopher Krauss, 2018. "Pairs trading with partial cointegration," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 121-138, January.
    5. Binh Do & Robert Faff, 2021. "Pairs trading and idiosyncratic cash flow risk," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(2), pages 3171-3206, June.
    6. Jeff Stephenson & Bruce Vanstone & Tobias Hahn, 0. "A Unifying Model for Statistical Arbitrage: Model Assumptions and Empirical Failure," Computational Economics, Springer;Society for Computational Economics, vol. 0, pages 1-22.
    7. Jacobs, Heiko, 2015. "What explains the dynamics of 100 anomalies?," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 65-85.
    8. Clegg, Matthew & Krauss, Christopher, 2016. "Pairs trading with partial cointegration," FAU Discussion Papers in Economics 05/2016, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    9. Sant'Anna, Leonardo Riegel & de Oliveira, Alan Delgado & Filomena, Tiago Pascoal & Caldeira, João Frois, 2020. "Solving the index tracking problem based on a convex reformulation for cointegration," Finance Research Letters, Elsevier, vol. 37(C).
    10. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    11. Hossein Rad & Rand Kwong Yew Low & Robert Faff, 2016. "The profitability of pairs trading strategies: distance, cointegration and copula methods," Quantitative Finance, Taylor & Francis Journals, vol. 16(10), pages 1541-1558, October.
    12. Wang, Jai-Jen & Lee, Jin-Ping & Zhao, Yang, 2018. "Pair-trading profitability and short-selling restriction: Evidence from the Taiwan stock market," International Review of Economics & Finance, Elsevier, vol. 55(C), pages 173-184.
    13. Miroslav Fil, 2020. "Gold Standard Pairs Trading Rules: Are They Valid?," Papers 2010.01157, arXiv.org.
    14. Sánchez-Granero, M.A. & Balladares, K.A. & Ramos-Requena, J.P. & Trinidad-Segovia, J.E., 2020. "Testing the efficient market hypothesis in Latin American stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    15. Zhe Huang & Franck Martin, 2017. "Optimal pairs trading strategies in a cointegration framework," Economics Working Paper Archive (University of Rennes 1 & University of Caen) 2017-08, Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS.
    16. Krauss, Christopher, 2015. "Statistical arbitrage pairs trading strategies: Review and outlook," FAU Discussion Papers in Economics 09/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    17. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    18. Hillert, Alexander & Jacobs, Heiko & Müller, Sebastian, 2018. "Journalist disagreement," Journal of Financial Markets, Elsevier, vol. 41(C), pages 57-76.
    19. Hannes Mohrschladt, 2018. "The impact of size and book-to-market among paired stocks," Journal of Asset Management, Palgrave Macmillan, vol. 19(6), pages 384-393, October.
    20. Fischer, Thomas & Krauss, Christopher, 2017. "Deep learning with long short-term memory networks for financial market predictions," FAU Discussion Papers in Economics 11/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    21. Jia Miao & Jason Laws, 2016. "Profitability Of A Simple Pairs Trading Strategy: Recent Evidences From A Global Context," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(04), pages 1-18, June.
    22. Vladim'ir Hol'y & Petra Tomanov'a, 2018. "Estimation of Ornstein-Uhlenbeck Process Using Ultra-High-Frequency Data with Application to Intraday Pairs Trading Strategy," Papers 1811.09312, arXiv.org, revised Dec 2019.

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    More about this item

    Keywords

    Pairs trading; Relative-value arbitrage; Return predictability; International stock markets; Limited attention; Limits to arbitrage;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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