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

Author

Listed:
  • Federico Platania

    (Institut Supérieur de Gestion)

  • Francesco Appio

    (Paris School of Business)

  • Celina Toscano Hernandez

    (ISC Grande École de Commerce
    Membre du LEJEP, CY Cergy Paris Université)

  • Imane El Ouadghiri

    (Pôle Universitaire Léonard de Vinci, Research Center)

  • Jonathan Peillex

    (ICD International Business School)

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.

Suggested Citation

  • Federico Platania & Francesco Appio & Celina Toscano Hernandez & Imane El Ouadghiri & Jonathan Peillex, 2025. "A multi-objective pair trading strategy: integrating neural networks and cyclical insights for optimal trading performance," Annals of Operations Research, Springer, vol. 346(2), pages 1553-1572, March.
    • Handle: RePEc:spr:annopr:v:346:y:2025:i:2:d:10.1007_s10479-023-05754-z
    DOI: 10.1007/s10479-023-05754-z
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    References listed on IDEAS

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