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

Federico Platania (), Francesco Appio (), Celina Toscano Hernandez (), Imane El Ouadghiri () and Jonathan Peillex ()
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Federico Platania: Institut Supérieur de Gestion
Francesco Appio: Paris School of Business
Celina Toscano Hernandez: ISC Grande École de Commerce
Imane El Ouadghiri: Pôle Universitaire Léonard de Vinci, Research Center
Jonathan Peillex: ICD International Business School

Annals of Operations Research, 2025, vol. 346, issue 2, No 30, 1553-1572

Abstract: 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.

Keywords: Pair trading; Multi-objective optimization; Neural network; Cointegration (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-023-05754-z

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