Machine Learning for Sustainable Portfolio Optimization Applied to a Water Market

María Antonia Truyols-Pont (), Amelia Bilbao-Terol () and Mar Arenas-Parra
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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

Mathematics, 2024, vol. 12, issue 24, 1-17

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.

Keywords: sustainable finance; Black–Litterman model; water market; machine learning; LSTM neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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