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Some Notes on the Formation of a Pair in Pairs Trading

Author

Listed:
  • José Pedro Ramos-Requena

    (Department of Economics and Business, University of Almería, Ctra. Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain)

  • Juan Evangelista Trinidad-Segovia

    (Department of Economics and Business, University of Almería, Ctra. Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain)

  • Miguel Ángel Sánchez-Granero

    (Department of Matematics, University of Almería, Ctra. Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain)

Abstract

The main goal of the paper is to introduce different models to calculate the amount of money that must be allocated to each stock in a statistical arbitrage technique known as pairs trading. The traditional allocation strategy is based on an equal weight methodology. However, we will show how, with an optimal allocation, the performance of pairs trading increases significantly. Four methodologies are proposed to set up the optimal allocation. These methodologies are based on distance, correlation, cointegration and Hurst exponent (mean reversion). It is showed that the new methodologies provide an improvement in the obtained results with respect to an equal weighted strategy.

Suggested Citation

  • José Pedro Ramos-Requena & Juan Evangelista Trinidad-Segovia & Miguel Ángel Sánchez-Granero, 2020. "Some Notes on the Formation of a Pair in Pairs Trading," Mathematics, MDPI, vol. 8(3), pages 1-17, March.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:3:p:348-:d:328579
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    References listed on IDEAS

    as
    1. Ladislav Kristoufek & Miloslav Vosvrda, 2014. "Measuring capital market efficiency: long-term memory, fractal dimension and approximate entropy," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(7), pages 1-9, July.
    2. Kristoufek, Ladislav & Vosvrda, Miloslav, 2013. "Measuring capital market efficiency: Global and local correlations structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 184-193.
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    4. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    5. 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).
    6. Evan Gatev & William N. Goetzmann & K. Geert Rouwenhorst, 2006. "Pairs Trading: Performance of a Relative-Value Arbitrage Rule," Review of Financial Studies, Society for Financial Studies, vol. 19(3), pages 797-827.
    7. Zunino, Luciano & Zanin, Massimiliano & Tabak, Benjamin M. & Pérez, Darío G. & Rosso, Osvaldo A., 2010. "Complexity-entropy causality plane: A useful approach to quantify the stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(9), pages 1891-1901.
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    9. Miguel Ángel Sánchez & Juan E Trinidad & José García & Manuel Fernández, 2015. "The Effect of the Underlying Distribution in Hurst Exponent Estimation," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-17, May.
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    11. Hanxiong Zhang & Andrew Urquhart, 2019. "Pairs trading across Mainland China and Hong Kong stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(2), pages 698-726, April.
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    Cited by:

    1. Lucas Schneider & Johannes Stübinger, 2020. "Dispersion Trading Based on the Explanatory Power of S&P 500 Stock Returns," Mathematics, MDPI, vol. 8(9), pages 1-22, September.

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