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Pairs trading in the UK equity market: risk and return

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  • David A. Bowen
  • Mark C. Hutchinson

Abstract

In this paper, we provide the first comprehensive UK evidence on the profitability of the pairs trading strategy. Evidence suggests that the strategy performs well in crisis periods, so we control for both risk and liquidity to assess performance. To evaluate the effect of market frictions on the strategy, we use several estimates of transaction costs. We also present evidence on the performance of the strategy in different economic and market states. Our results show that pairs trading portfolios typically have little exposure to known equity risk factors such as market, size, value, momentum and reversal. However, a model controlling for risk and liquidity explains a far larger proportion of returns. Incorporating different assumptions about bid-ask spreads leads to reductions in performance estimates. When we allow for time-varying risk exposures, conditioned on the contemporaneous equity market return, risk-adjusted returns are generally not significantly different from zero.

Suggested Citation

  • David A. Bowen & Mark C. Hutchinson, 2016. "Pairs trading in the UK equity market: risk and return," The European Journal of Finance, Taylor & Francis Journals, vol. 22(14), pages 1363-1387, November.
    • Handle: RePEc:taf:eurjfi:v:22:y:2016:i:14:p:1363-1387
    DOI: 10.1080/1351847X.2014.953698
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    Citations

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    Cited by:

    1. Endres, Sylvia & Stübinger, Johannes, 2017. "Optimal trading strategies for Lévy-driven Ornstein-Uhlenbeck processes," FAU Discussion Papers in Economics 17/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    2. Isabel Figuerola‐Ferretti & Ioannis Paraskevopoulos & Tao Tang, 2018. "Pairs‐trading and spread persistence in the European stock market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(9), pages 998-1023, September.
    3. 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).
    4. Zura Kakushadze & Juan Andrés Serur, 2018. "151 Trading Strategies," Springer Books, Springer, number 978-3-030-02792-6, September.
    5. Stübinger, Johannes, 2018. "Statistical arbitrage with optimal causal paths on high-frequencydata of the S&P 500," FAU Discussion Papers in Economics 01/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    6. Stübinger, Johannes & Walter, Dominik & Knoll, Julian, 2017. "Financial market predictions with Factorization Machines: Trading the opening hour based on overnight social media data," FAU Discussion Papers in Economics 19/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    7. Jing Niu & Chao Ma & Chun-Ping Chang, 2023. "The arbitrage strategy in the crude oil futures market of shanghai international energy exchange," Economic Change and Restructuring, Springer, vol. 56(2), pages 1201-1223, April.
    8. Johannes St binger & Jens Bredthauer, 2017. "Statistical Arbitrage Pairs Trading with High-frequency Data," International Journal of Economics and Financial Issues, Econjournals, vol. 7(4), pages 650-662.
    9. Knoll, Julian & Stübinger, Johannes & Grottke, Michael, 2017. "Exploiting social media with higher-order Factorization Machines: Statistical arbitrage on high-frequency data of the S&P 500," FAU Discussion Papers in Economics 13/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    10. Thomas Günter Fischer & Christopher Krauss & Alexander Deinert, 2019. "Statistical Arbitrage in Cryptocurrency Markets," JRFM, MDPI, vol. 12(1), pages 1-15, February.
    11. Fernando Caneo & Werner Kristjanpoller, 2021. "Improving statistical arbitrage investment strategy: Evidence from Latin American stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4424-4440, July.

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