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<p>Environmental, social, and governance considerations are becoming more relevant for investors, suggesting the need to include ESG metrics in optimizing portfolios to comply with investor requirements. Genetic algorithms offer the flexibility, adaptability, and effectiveness needed to deal with complex multi-objective problems with nonlinear constraints that traditional deterministic methods could not resolve. This paper aims to construct ESG-responsible portfolios that are able to replicate the index’s returns and maintain low volatility. We propose a hybrid approach that combines long short-term memory (LSTM) neural networks to predict closing prices and genetic algorithms to solve a multi-objective problem that minimizes tracking error, ESG scores, and volatility. Our analysis uses LSTM-predicted data of the stocks that compose the IBEX 35 and EURO STOXX 50 during 2021 and the first half of 2022. The results show that optimized portfolios present low ESG risk; the returns follow the index closely and have small volatility. This allows investors to replicate the index performance while enhancing the ESG score of the portfolios. Our study addresses the limitations of traditional ESG portfolio optimization by explicitly incorporating predicted returns from long short-term memory networks and leveraging genetic algorithms (GAs) enhanced by NSGA-III.</p>

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Constructing ESG-driven portfolios using genetic algorithms based on LSTM-predicted data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月18日

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  • Xavier Martínez-Barbero,
  • Roberto Cervelló-Royo,
  • Jaume Jordán,
  • Javier Ribal

摘要

Environmental, social, and governance considerations are becoming more relevant for investors, suggesting the need to include ESG metrics in optimizing portfolios to comply with investor requirements. Genetic algorithms offer the flexibility, adaptability, and effectiveness needed to deal with complex multi-objective problems with nonlinear constraints that traditional deterministic methods could not resolve. This paper aims to construct ESG-responsible portfolios that are able to replicate the index’s returns and maintain low volatility. We propose a hybrid approach that combines long short-term memory (LSTM) neural networks to predict closing prices and genetic algorithms to solve a multi-objective problem that minimizes tracking error, ESG scores, and volatility. Our analysis uses LSTM-predicted data of the stocks that compose the IBEX 35 and EURO STOXX 50 during 2021 and the first half of 2022. The results show that optimized portfolios present low ESG risk; the returns follow the index closely and have small volatility. This allows investors to replicate the index performance while enhancing the ESG score of the portfolios. Our study addresses the limitations of traditional ESG portfolio optimization by explicitly incorporating predicted returns from long short-term memory networks and leveraging genetic algorithms (GAs) enhanced by NSGA-III.