Sustainability in LSTM Price Prediction for Portfolio Optimization in the European Market

Ardelia Amardana, Diana Barro () and Marco Corazza
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Ardelia Amardana: Ca’ Foscari University of Venice
Diana Barro: Ca’ Foscari University of Venice
Marco Corazza: Ca’ Foscari University of Venice

No 2025: 25, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

Abstract: Sustainability in financial markets has gained attention. This study addresses it by enhancing portfolio optimization through as additional inputs alongside price data that can improve stock return prediction. Using LSTM models with RMSProp optimizer performs best in consistency of minimizing prediction errors and given the ability to capture complex pattern between price, greenhouse gas (GHG) emissions and environmental scores (E-Scores). This study uses data from the EURO STOXX 50 between 2016 and 2022, focusing on out-of-sample weekly return predictions in 2022. Four model setups are tested: price-only, and price combined with GHG, E-score, or both. Our findings show that incorporating the E-Score improves price and return predictions in several sectors, whereas some sectors show limited benefit, indicating sustainability information may already be priced in. Additionally, in portfolio optimization shows that models including E-Score gives better performance across different holding periods by setting more effective weightings and aligning closely with our benchmark. This results provides further evidence in the following year 2023 and EURO STOXX 50 ESG performance.

Keywords: Sustainable Indicators; Long Short Term Memory; Return Prediction; Portfolio Optimization (search for similar items in EconPapers)
JEL-codes: C45 C53 C63 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2025
New Economics Papers: this item is included in nep-eec, nep-env, nep-fmk, nep-for and nep-inv
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