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Machine learning in Esg investing: predictive analysis for stock performance
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 1.71 MB | Adobe PDF |
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Resumo(s)
This strategy explores the use of ESG metrics within machine learning frameworks,
particularly Gradient Boosting Trees and SHAP-value analysis, to predict stock performance
in the Nordic Markets. Focusing on data from 2007-2022, it examines the efficiency of ESG
metrics in forecasting Active Return. Utilizing the model, it reveals that long portfolios,
especially those informed by SHAP analysis, consistently outperform the OMX Nordic Large
cap Index, while short portfolios show underperformance. The study highlights the potential
of machine learning in enhancing ESG-focused investment strategies, suggesting the need for
broader datasets and diverse market analysis for more robust and comprehensive investment
insights.
Descrição
Palavras-chave
Machine learning Esg Equities Nordic market Gradient boosting tree Sustainable finance