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A multi-objective pair trading strategy: integrating neural networks and cyclical insights for optimal trading performance

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Abstract

This paper introduces a comprehensive multidimensional pair trading strategy that integrates a multi-objective programming approach, cyclical insights, and neural networks to optimize trading performance. The strategy aims to exploit market inefficiencies by identifying statistical arbitrage opportunities in highly-correlated pairs of stocks. By incorporating multiple objectives, including maximizing returns and minimizing risk, the multi-objective programming framework enables the exploration of a diverse set of Pareto-optimal solutions. The inclusion of cyclical insights enhances the understanding of market dynamics, while the neural network methodology captures complex patterns and accurately predicts trading signals.

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Notes

  1. The specific value of c is not clearly specified.

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Authors and Affiliations

  1. Institut Supérieur de Gestion, 8 Rue de Lota, 75116, Paris, France

    Federico Platania

  2. Paris School of Business, Paris, France

    Francesco Appio

  3. ISC Grande École de Commerce, 22 Bd du Fort de Vaux, 75017, Paris, France

    Celina Toscano Hernandez

  4. Membre du LEJEP, CY Cergy Paris Université, Site des Chênes 1. 33, Boulevard du Port, 95011, Cergy-Pontois, France

    Celina Toscano Hernandez

  5. Pôle Universitaire Léonard de Vinci, Research Center, 92 916, Paris La Défense, France

    Imane El Ouadghiri

  6. ICD International Business School, 12 Rue Alexandre Parodi, 75010, Paris, France

    Jonathan Peillex

Authors
  1. Federico Platania
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  2. Francesco Appio
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  3. Celina Toscano Hernandez
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  4. Imane El Ouadghiri
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Correspondence to Federico Platania.

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Platania, F., Appio, F., Toscano Hernandez, C. et al. A multi-objective pair trading strategy: integrating neural networks and cyclical insights for optimal trading performance. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05754-z

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