Machine Learning for Sustainable Portfolio Optimization Applied to a Water Market
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 excha...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/24/3975 |
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| author | María Antonia Truyols-Pont Amelia Bilbao-Terol Mar Arenas-Parra |
| author_facet | María Antonia Truyols-Pont Amelia Bilbao-Terol Mar Arenas-Parra |
| author_sort | María Antonia Truyols-Pont |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7eb040ab479b4158adeb3c62c55e1f3b |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-7eb040ab479b4158adeb3c62c55e1f3b2025-08-20T02:57:14ZengMDPI AGMathematics2227-73902024-12-011224397510.3390/math12243975Machine Learning for Sustainable Portfolio Optimization Applied to a Water MarketMaría Antonia Truyols-Pont0Amelia Bilbao-Terol1Mar Arenas-Parra2Department of Quantitative Economics, Faculty of Economics and Business Administration, University of Oviedo, Avda. del Cristo s/n, 33006 Oviedo, SpainDepartment of Quantitative Economics, Faculty of Economics and Business Administration, University of Oviedo, Avda. del Cristo s/n, 33006 Oviedo, SpainDepartment of Quantitative Economics, Faculty of Economics and Business Administration, University of Oviedo, Avda. del Cristo s/n, 33006 Oviedo, SpainThis 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.https://www.mdpi.com/2227-7390/12/24/3975sustainable financeBlack–Litterman modelwater marketmachine learningLSTM neural network |
| spellingShingle | María Antonia Truyols-Pont Amelia Bilbao-Terol Mar Arenas-Parra Machine Learning for Sustainable Portfolio Optimization Applied to a Water Market Mathematics sustainable finance Black–Litterman model water market machine learning LSTM neural network |
| title | Machine Learning for Sustainable Portfolio Optimization Applied to a Water Market |
| title_full | Machine Learning for Sustainable Portfolio Optimization Applied to a Water Market |
| title_fullStr | Machine Learning for Sustainable Portfolio Optimization Applied to a Water Market |
| title_full_unstemmed | Machine Learning for Sustainable Portfolio Optimization Applied to a Water Market |
| title_short | Machine Learning for Sustainable Portfolio Optimization Applied to a Water Market |
| title_sort | machine learning for sustainable portfolio optimization applied to a water market |
| topic | sustainable finance Black–Litterman model water market machine learning LSTM neural network |
| url | https://www.mdpi.com/2227-7390/12/24/3975 |
| work_keys_str_mv | AT mariaantoniatruyolspont machinelearningforsustainableportfoliooptimizationappliedtoawatermarket AT ameliabilbaoterol machinelearningforsustainableportfoliooptimizationappliedtoawatermarket AT mararenasparra machinelearningforsustainableportfoliooptimizationappliedtoawatermarket |