How green are SRI labelled funds? Insights from a Machine Learning based clustering approach

Rannou, Yves; Boutabba, Mohamed Amine; Mercadier, Mathieu

doi:10.2139/ssrn.4037373

Cited by 3 publications

(2 citation statements)

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“…However, the vague definition of SFDR fund categories gives fund managers some flexibility in their approach to self-assigning financial products 15 , leaving room for a degree of misalignment. In this regard, a recent study by Rannou et al (2022) crossed the SFDR classification of French ISR labeled funds with their holdings, documenting discrepancies on the environmental levels of article 8 funds. In view of these arguments, we formulate the following hypothesis:…”

Section: Esg Ratings Than Single Labeled Fundsmentioning

confidence: 99%

“… 18. As Morningstar (2022) notes, mutual fund companies take different approaches to classifying funds into Articles 8 and 9, based on their own interpretation of SFDR. In this regard, a recent study by Rannou et al (2022) crossed the SFDR classification of French ISR labeled funds with their holdings, documenting discrepancies in the environmental levels of Article 8 funds. …”

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confidence: 99%

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Do Sustainability Signals Diverge? An Analysis of Labeling Schemes for Socially Responsible Investments

Brito-Ramos,

Cortez,

Silva

2023

Business & Society

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This article investigates whether sustainability labels for mutual funds in Europe provide consistent signals regarding funds’ sustainable characteristics. Specifically, we assess the alignment of signals conveyed by third-party and self-declared labels. Among the first typology, we consider labels sponsored by government and nonprofit organizations (GNPOs) alongside Environmental, Social, and Governance (ESG) ratings from commercial data vendors. The latter category includes the Sustainable Finance Disclosure Regulation (SFDR) classification and an ESG-related name. Our findings indicate that equity funds with GNPO labels are more likely to exhibit top-tier ESG ratings and alignment with self-declared sustainability signals, namely Article 9 of SFDR and fund names. Furthermore, holding government and multiple GNPO labels is linked to other signals indicating higher sustainability standards. In addition, funds tend to experience an improvement in Morningstar globes after receiving a GNPO label, consistent with GNPO labels signaling funds of the high-quality type. The results regarding label alignment in fixed-income funds are less conclusive. Our findings underscore the need for credible signals in view of the growing number of sustainability labels available to investors.

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Section: Esg Ratings Than Single Labeled Fundsmentioning

confidence: 99%

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confidence: 99%

Do Sustainability Signals Diverge? An Analysis of Labeling Schemes for Socially Responsible Investments

Brito-Ramos,

Cortez,

Silva

2023

Business & Society

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Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways

Lim

2024

Artif Intell Rev

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The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation focuses on three primary research questions: the main research themes concerning ESG and AI in finance, the evolution of research intensity and interest in these areas, and the application and evolution of AI techniques specifically in research studies within the ESG and AI in finance domain. Eight archetypical research domains were identified: (i) Trading and Investment, (ii) ESG Disclosure, Measurement and Governance, (iii) Firm Governance, (iv) Financial Markets and Instruments, (v) Risk Management, (vi) Forecasting and Valuation, (vii) Data, and (viii) Responsible Use of AI. Distinctive AI techniques were found to be employed across these archetypes. The study contributes to consolidating knowledge on the intersection of ESG, AI, and finance, offering an ontological inquiry and key takeaways for practitioners and researchers. Important insights include the popularity and crowding of the Trading and Investment domain, the growth potential of the Data archetype, and the high potential of Responsible Use of AI, despite its low publication count. By understanding the nuances of different research archetypes, researchers and practitioners can better navigate this complex landscape and contribute to a more sustainable and responsible financial sector.

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Environmental, Social, and Governance (ESG) and Artificial Intelligence in Finance: State-of-the-Art and Research Takeaways

Lim

2023

Preprint

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The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation centers around three research questions: key research themes for ESG and AI in finance, research intensity and interest evolution, and the use and progression of AI techniques within these themes. Eight archetypical research domains were identified: (i) Trading and Investment, (ii) ESG Disclosure, Measurement and Governance, (iii) Firm Governance, (iv) Financial Markets and Instruments, (v) Risk Management, (vi) Forecasting and Valuation, (vii) Data, and (viii) Responsible Use of AI. Distinctive AI techniques were found to be employed across these archetypes. The study contributes to consolidating knowledge on the intersection of ESG, AI, and finance, offering an ontological inquiry and key takeaways for practitioners and researchers. Important insights include the popularity and crowding of the Trading and Investment domain, the growth potential of the Data archetype, and the high potential of Responsible Use of AI, despite its low publication count. By understanding the nuances of different research archetypes, researchers and practitioners can better navigate this complex landscape and contribute to a more sustainable and responsible financial sector.

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How green are SRI labelled funds? Insights from a Machine Learning based clustering approach

Cited by 3 publications

References 0 publications

Do Sustainability Signals Diverge? An Analysis of Labeling Schemes for Socially Responsible Investments

Do Sustainability Signals Diverge? An Analysis of Labeling Schemes for Socially Responsible Investments

Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways

Environmental, Social, and Governance (ESG) and Artificial Intelligence in Finance: State-of-the-Art and Research Takeaways

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