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A comparative study of portfolio optimization techniques in sustainable investing
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Resumo(s)
This Master's thesis navigates the intersection of machine learning (ML) and sustainable
investing, specifically focusing on portfolio optimization. The study analyses conventional and
ML-based methodologies, underlining the role and complications of incorporating ESG factors
into the investment process. The research outlines the effectiveness of ML in ESG integration,
offering a discussion on model selection tailored to investor-specific needs. The thesis also
underlines the requirement for continuous adaptation in this rapidly changing field of sustainable
investing. This work, serving as a guide for asset managers aiming to integrate sustainability
principles effectively, is based on an extensive literature review.
Descrição
Palavras-chave
Machine learning Sustainable investing Portfolio optimization Esg factors