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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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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DOI: https://doi.org/10.1007/s10479-023-05754-z