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Portfolio Optimization with Prediction-Based Return Using Long Short-Term Memory Neural Networks: Testing on Upward and Downward European Markets

Author

Listed:
  • Xavier Martínez-Barbero

    (Universitat Politècnica de València)

  • Roberto Cervelló-Royo

    (Universitat Politècnica de València)

  • Javier Ribal

    (Universitat Politècnica de València)

Abstract

In recent years, artificial intelligence has helped to improve processes and performance in many different areas: in the field of portfolio optimization, the inputs play a crucial role, and the use of machine learning algorithms can improve the estimation of the inputs to create robust portfolios able to generate returns consistently. This paper combines classical mean–variance optimization and machine learning techniques, concretely long short-term memory neural networks to provide more accurate predicted returns and generate profitable portfolios for 10 holding periods that present different financial contexts. The proposed algorithm is trained and tested with historical EURO STOXX 50® Index data from January 2015 to December 2020, and from January 2021 to June 2022, respectively. Empirical results show that our LSTM neural networks are able to achieve minor predictive errors since the average of the MSE of the 10 holding periods is 0.00047, the average of the MAE is 0.01634, and predict the direction of returns with an average accuracy over the 10 investment periods of 95.8%. Our prediction-based portfolios consistently beat the EURO STOXX 50® Index, achieving superior positive results even during bear markets.

Suggested Citation

  • Xavier Martínez-Barbero & Roberto Cervelló-Royo & Javier Ribal, 2025. "Portfolio Optimization with Prediction-Based Return Using Long Short-Term Memory Neural Networks: Testing on Upward and Downward European Markets," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1479-1504, March.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:3:d:10.1007_s10614-024-10604-6
    DOI: 10.1007/s10614-024-10604-6
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    References listed on IDEAS

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