Predictors of Sustainable Investment Motivation: An Interpretable Machine Learning Approach
Sergey Sosnovskikh and Danila Valko and Raphael Meyer-Alten
Sustainable Development
This study investigates the determinants influencing retail investors' capital allocation to sustainable financial products, focusing specifically on Germany — a pertinent case due to its strong commitment to sustainability, supportive regulatory environment and substantial market growth. We utilised two surveys conducted in 2020 by the 2° Investing Initiative and choyze GmbH in collaboration with the German Environment Agency to obtain a measurement invariant combined dataset. Utilising robust and generalised linear modelling and interpretable machine learning techniques, our analysis identified that three primary motivation components — personal values, social and environmental impact and investment return — exhibit significant overlap. The findings demonstrate that investment motivations are consistently predicted by sustainability interests. In contrast, socio-demographic factors (age, gender, education, household income) exhibit inconsistent patterns across investment motivations and exert weaker influence. As a result, we propose robust models to predict sustainable investment motivation and highlight their consistency and applicability in the green finance sector.
February / 2025
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