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Machine Learning for Sustainable Portfolio Optimization Applied to a Water Market

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Listed:
  • María Antonia Truyols-Pont

    (Department of Quantitative Economics, Faculty of Economics and Business Administration, University of Oviedo, Avda. del Cristo s/n, 33006 Oviedo, Spain)

  • Amelia Bilbao-Terol

    (Department of Quantitative Economics, Faculty of Economics and Business Administration, University of Oviedo, Avda. del Cristo s/n, 33006 Oviedo, Spain)

  • Mar Arenas-Parra

    (Department of Quantitative Economics, Faculty of Economics and Business Administration, University of Oviedo, Avda. del Cristo s/n, 33006 Oviedo, Spain)

Abstract

This study introduces a novel methodology that integrates the Black–Litterman model with Long Short-Term Memory Neural Networks (BL–LSTM). We use predictions from the LSTM as views in the Black–Litterman model. The resulting portfolio performs better than the traditional mean-variance (MV) and exchange-traded funds (ETFs) used as benchmarks. The proposal empowers investors to make more insightful decisions, drawing from a synthesis of historical data and advanced predictive techniques. This methodology is applied to a water market. Investing in the water market allows investors to actively support sustainable water solutions while potentially benefiting from the sector’s growth, contributing to achieving SDG 6. In addition, our modeling allows for companies’ environmental, social, and governance (ESG) scores to be considered in the portfolio construction process. In this case, investors’ decisions take into account companies’ socially responsible behavior in a broad sense, including aspects related to decent work, respect for indigenous communities and diversity, and the absence of corruption, among others. Therefore, this proposal provides investors with a tool for promoting sustainable investment practices.

Suggested Citation

  • María Antonia Truyols-Pont & Amelia Bilbao-Terol & Mar Arenas-Parra, 2024. "Machine Learning for Sustainable Portfolio Optimization Applied to a Water Market," Mathematics, MDPI, vol. 12(24), pages 1-17, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:24:p:3975-:d:1546308
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    References listed on IDEAS

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    1. Nhi N.Y.Vo & Xue-Zhong He & Shaowu Liu & Guandong Xu, 2019. "Deep Learning for Decision Making and the Optimization of Socially Responsible Investments and Portfolio," Published Paper Series 2019-3, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    2. Lihki Rubio & Keyla Alba, 2022. "Forecasting Selected Colombian Shares Using a Hybrid ARIMA-SVR Model," Mathematics, MDPI, vol. 10(13), pages 1-21, June.
    3. Fernandes, Betina & Street, Alexandre & Fernandes, Cristiano & Valladão, Davi, 2018. "On an adaptive Black–Litterman investment strategy using conditional fundamentalist information: A Brazilian case study," Finance Research Letters, Elsevier, vol. 27(C), pages 201-207.
    4. Amelia Bilbao-Terol & Mar Arenas-Parra & Verónica Cañal-Fernández & Celia Bilbao-Terol, 2013. "Selection of Socially Responsible Portfolios Using Hedonic Prices," Journal of Business Ethics, Springer, vol. 115(3), pages 515-529, July.
    5. Cabello, J.M. & Ruiz, F. & Pérez-Gladish, B. & Méndez-Rodríguez, P., 2014. "Synthetic indicators of mutual funds’ environmental responsibility: An application of the Reference Point Method," European Journal of Operational Research, Elsevier, vol. 236(1), pages 313-325.
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