Measuring Sustainability Intention of ESG Fund Disclosure using Few-Shot Learning

• URL: http://arxiv.org/abs/2407.06893v1
• Date: Tue, 9 Jul 2024 14:25:23 GMT
• Title: Measuring Sustainability Intention of ESG Fund Disclosure using Few-Shot Learning
• Authors: Mayank Singh, Nazia Nafis, Abhijeet Kumar, Mridul Mishra,
• Abstract summary: This paper proposes a unique method and system to classify and score the fund prospectuses in the sustainable universe. We employ few-shot learners to identify specific, ambiguous, and generic sustainable investment-related language. We construct a ratio metric to determine language score and rating to rank products and quantify sustainability claims.
• Score: 1.1957520154275776
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Global sustainable fund universe encompasses open-end funds and exchange-traded funds (ETF) that, by prospectus or other regulatory filings, claim to focus on Environment, Social and Governance (ESG). Challengingly, the claims can only be confirmed by examining the textual disclosures to check if there is presence of intentionality and ESG focus on its investment strategy. Currently, there is no regulation to enforce sustainability in ESG products space. This paper proposes a unique method and system to classify and score the fund prospectuses in the sustainable universe regarding specificity and transparency of language. We aim to employ few-shot learners to identify specific, ambiguous, and generic sustainable investment-related language. Additionally, we construct a ratio metric to determine language score and rating to rank products and quantify sustainability claims for US sustainable universe. As a by-product, we publish manually annotated quality training dataset on Hugging Face (ESG-Prospectus-Clarity-Category under cc-by-nc-sa-4.0) of more than 1K ESG textual statements. The performance of the few-shot finetuning approach is compared with zero-shot models e.g., Llama-13B, GPT 3.5 Turbo etc. We found that prompting large language models are not accurate for domain specific tasks due to misalignment issues. The few-shot finetuning techniques outperform zero-shot models by large margins of more than absolute ~30% in precision, recall and F1 metrics on completely unseen ESG languages (test set). Overall, the paper attempts to establish a systematic and scalable approach to measure and rate sustainability intention quantitatively for sustainable funds using texts in prospectus. Regulatory bodies, investors, and advisors may utilize the findings of this research to reduce cognitive load in investigating or screening of ESG funds which accurately reflects the ESG intention.

Related papers

• Trustworthiness in Retrieval-Augmented Generation Systems: A Survey [59.26328612791924]
  Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs) We propose a unified framework that assesses the trustworthiness of RAG systems across six key dimensions: factuality, robustness, fairness, transparency, accountability, and privacy.
  arXiv  Detail & Related papers  (2024-09-16T09:06:44Z)
• ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSA [50.90538760832107]
  This research presents a novel task, Review-Level Opinion Aspect Sentiment Target (ROAST) ROAST seeks to close the gap between sentence-level and text-level ABSA by identifying every ABSA constituent at the review level. We extend the available datasets to enable ROAST, addressing the drawbacks noted in previous research.
  arXiv  Detail & Related papers  (2024-05-30T17:29:15Z)
• ESG-FTSE: A corpus of news articles with ESG relevance labels and use cases [1.3937696730884712]
  We present ESG-FTSE, the first corpus comprised of news articles with Environmental, Social and Governance (ESG) relevance annotations. This has led to the rise of ESG scores to evaluate an investment's credentials as socially responsible. Quantitative techniques can be applied to improve ESG scores, thus, responsible investing.
  arXiv  Detail & Related papers  (2024-05-30T16:19:02Z)
• Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation Language Model [50.339632513018934]
  supervised fine-tuning (SFT) has been a straightforward approach for tailoring the output of foundation large language model (LLM) to specific preferences. We critically examine this hypothesis within the scope of cross-lingual generation tasks. We introduce a novel training-free alignment method named PreTTY, which employs minimal task-related prior tokens.
  arXiv  Detail & Related papers  (2024-04-25T17:19:36Z)
• Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet? [82.02076369811402]
  Supervised fine-tuning (SFT), supervised instruction tuning (SIT) and in-context learning (ICL) are three alternative, de facto standard approaches to few-shot learning. We present an extensive and systematic comparison of the three approaches, testing them on 6 high- and low-resource languages, three different NLU tasks, and a myriad of language and domain setups. Our observations show that supervised instruction tuning has the best trade-off between performance and resource requirements.
  arXiv  Detail & Related papers  (2024-03-04T10:48:13Z)
• Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents [80.5213198675411]
  Large language models (LLMs) have dramatically enhanced the field of language intelligence. LLMs leverage the intriguing chain-of-thought (CoT) reasoning techniques, obliging them to formulate intermediate steps en route to deriving an answer. Recent research endeavors have extended CoT reasoning methodologies to nurture the development of autonomous language agents.
  arXiv  Detail & Related papers  (2023-11-20T14:30:55Z)
• Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models [16.231171704561714]
  This study uses Information Extraction (IE) methods to extract structured insights related to ESG aspects from companies' sustainability reports. We then leverage graph-based representations to conduct statistical analyses concerning the extracted insights.
  arXiv  Detail & Related papers  (2023-10-09T11:34:41Z)
• Leveraging BERT Language Models for Multi-Lingual ESG Issue Identification [0.30254881201174333]
  Investors have increasingly recognized the significance of ESG criteria in their investment choices. The Multi-Lingual ESG Issue Identification (ML-ESG) task encompasses the classification of news documents into 35 distinct ESG issue labels. In this study, we explored multiple strategies harnessing BERT language models to achieve accurate classification of news documents across these labels.
  arXiv  Detail & Related papers  (2023-09-05T12:48:21Z)
• Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality Assessment [54.31355080688127]
  We introduce a text-prompted Semantic Affinity Quality Index (SAQI) and its localized version (SAQI-Local) using Contrastive Language-Image Pre-training (CLIP) BVQI-Local demonstrates unprecedented performance, surpassing existing zero-shot indices by at least 24% on all datasets. We conduct comprehensive analyses to investigate different quality concerns of distinct indices, demonstrating the effectiveness and rationality of our design.
  arXiv  Detail & Related papers  (2023-04-28T08:06:05Z)
• Bayesian Optimization of ESG Financial Investments [0.0]
  ESG (Economic, Social and Governance) criteria have become more significant in finance. This paper combines mathematical modelling, with ESG and finance.
  arXiv  Detail & Related papers  (2023-02-10T15:17:36Z)
• ESGBERT: Language Model to Help with Classification Tasks Related to Companies Environmental, Social, and Governance Practices [0.0]
  Non-financial factors such as environmental, social, and governance (ESG) are attracting attention from investors. We see a need for sophisticated NLP techniques for classification tasks for ESG text. We explore doing this by fine-tuning BERTs pre-trained weights using ESG specific text and then further fine-tuning the model for a classification task.
  arXiv  Detail & Related papers  (2022-03-31T04:22:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.