Board Diversity Appearance and Firm Performance: An Image-Based Deep Learning Approach - Greger, Lukas; Scholz, Hendrik; Stiller, Anja; Webersinke, Nicolas (2024)
We introduce a new measure of board diversity appearance leveraging machine learning methods. Using portrait pictures of board members from S&P 100 firms, we receive a score that quantifies the level of visually observable diversity on a board. We argue that stakeholders may use this board diversity appearance to approximate unobservable attributes of board members that affect firm performance. Further, we suggest that this signal is particularly valuable for firms that do not receive much public attention and could positively alter the behavior of stakeholders towards a firm. Using factor model analyses, we find indication that f irms with more diverse appearing boards and less public attention indeed tend to outperform. In addition, we also find support for improved firm performance of these firms based on accounting measure
Keywords: board diversity; firm performance; machine learning; stakeholder attention

ChatClimate: Grounding conversational AI in climate science - Vaghefi, Saeid Ashraf; Stammbach, Dominik; Muccione, Veruska; Bingler, Julia; Ni, Jingwei; Kraus, Mathias; Allen, Simon; Colesanti-Senni, Chiara; Wekhof, Tobias; Schimanski, Tobias; Gostlow, Glen; Yu, Tingyu; et al. (Communications Earth & Environment, 2023)
Large Language Models have made remarkable progress in question-answering tasks, but challenges like hallucination and outdated information persist. These issues are especially critical in domains like climate change, where timely access to reliable information is vital. One solution is granting these models access to external, scientifically accurate sources to enhance their knowledge and reliability. Here, we enhance GPT-4 by providing access to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the most comprehensive, up-to-date, and reliable source in this domain (refer to the ’Data Availability’ section). We present our conversational AI prototype, available at www.chatclimate.ai , and demonstrate its ability to answer challenging questions in three different setups: (1) GPT-4, (2) ChatClimate, which relies exclusively on IPCC AR6 reports, and (3) Hybrid ChatClimate, which utilizes IPCC AR6 reports with in-house GPT-4 knowledge. The evaluation of answers by experts show that the hybrid ChatClimate AI assistant provide more accurate responses, highlighting the effectiveness of our solution.
CRIS-Team Scopus Importer:2023-12-29

Cheap Talk and Cherry-Picking: What ClimateBert has to say on Corporate Climate Risk Disclosures - Bingler, Julia Anna; Kraus, Mathias; Leippold, Markus; Webersinke, Nicolas (Finance Research Letters, 2022)
Disclosure of climate-related financial risks greatly helps investors assess companies' preparedness for climate change. Voluntary disclosures such as those based on the recommendations of the Task Force for Climate-related Financial Disclosures (TCFD) are being hailed as an effective measure for better climate risk management. We ask whether this expectation is justified. We do so with the help of a deep neural language model, which we christen ClimateBert. In analyzing the disclosures of TCFD-supporting firms, ClimateBert comes to the sobering conclusion that the firms' TCFD support is mostly cheap talk and that firms cherry-pick to report primarily non-material climate risk information.<br
Keywords: Climate-risk disclosure; Voluntary reporting; TCFD recommendations; Natural language processing

Cheap Talk in Corporate Climate Commitments: The effectiveness of climate initiatives - Bingler, Julia Anna; Kraus, Mathias; Leippold, Markus; Webersinke, Nicolas (Journal of Banking & Finance, 2024)
Corporate climate disclosures are considered an essential prerequisite to managing climate-related financial risks. At the same time, current disclosures are imprecise, inaccurate, and greenwashing-prone. We introduce a deep learning approach to enable comprehensive climate disclosure analyses by fine-tuning the ClimateBERT model. From 14,584 annual reports of the MSCI World index firms from 2010 to 2020, we extract the amount of cheap talk, defined as the share of precise versus imprecise climate commitments. We then test various hypotheses by linking three different climate initiatives, namely the Task Force on Climate-Related Financial Disclosure, the Science-Based Targets Initiative, and the Climate Action 100+, to the economic channels of signaling, credibility, and active engagement. In particular, we ask whether these initiatives decrease cheap talk by disciplining companies in how they define and disclose actionable climate commitments in their annual reports.<br
Keywords: Corporate climate disclosures; voluntary reporting; commitments; TCFD recommendations; textual analysis; natural language processing

ClimateBert: A Pretrained Language Model for Climate-Related Text - Webersinke, Nicolas; Kraus, Mathias; Bingler, Julia Anna; Leippold, Markus (Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges, 2022)
Over the recent years, large pretrained language models (LM) have revolutionized the field of natural language processing (NLP). However, while pretraining on general language has been shown to work very well for common language, it has been observed that niche language poses problems. In particular, climate-related texts include specific language that common LMs can not represent accurately. We argue that this shortcoming of today's LMs limits the applicability of modern NLP to the broad field of text processing of climate-related texts. As a remedy, we propose ClimateBERT, a transformer-based language model that is further pretrained on over 2 million paragraphs of climate-related texts, crawled from various sources such as common news, research articles, and climate reporting of companies. We find that ClimateBERT leads to a 48% improvement on a masked language model objective which, in turn, leads to lowering error rates by 3.57% to 35.71% for various climate-related downstream tasks like text classification, sentiment analysis, and fact-checking.<br

Environmental Claim Detection - Stammbach, Dominik; Webersinke, Nicolas; Bingler, Julia Anna; Kraus, Mathias; Leippold, Markus (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2023)
To transition to a green economy, environmental claims made by companies must be reliable, comparable, and verifiable. To analyze such claims at scale, automated methods are needed to detect them in the first place. However, there exist no datasets or models for this. Thus, this paper introduces the task of environmental claim detection. To accompany the task, we release an expert-annotated dataset and models trained on this dataset. We preview one potential application of such models: We detect environmental claims made in quarterly earning calls and find that the number of environmental claims has steadily increased since the Paris Agreement in 2015.<br
Keywords: environmental claims; climate change; natural language processing; nlp

Natural Language Processing meets Accounting and Finance: Review and Performance Comparison of Textual Analysis Approaches - Webersinke, Nicolas (2023)
Deep learning approaches have revolutionized Natural Language Processing (NLP) and textual analysis in the last decade. The performance of the popular chatbot ChatGPT is an impressive demonstration of what deep learning is capable of. However, this remarkable progress with the potential to significantly reduce measurement error has apparently not yet reached accounting and finance. We review a large corpus of accounting and finance literature that uses textual analysis and observe that rule-based and traditional machine learning approaches still dominate. We then compare the performance of rule-based, traditional machine learning, and deep learning approaches on four datasets and find deep learning approaches to consistently perform best. However, we also find that for simpler topics requiring less context, rule-based approaches can produce decent results. These findings argue for more evaluation and increased, but not universal, use of deep learning for textual analysis in accounting and finance.<br
Keywords: Natural language processing; textual analysis; computational linguistics; machine learning; deep learning

Permutation Tests for Stock Index Performance: Evidence from ESG Indices - Hübel, Benjamin; Scholz, Hendrik; Webersinke, Nicolas (2022)
The ongoing shift towards sustainable asset management fuels the demand for stock indices with improved ESG profiles. Such indices exhibit different performance metrics and risk characteristics compared to their conventional parent indices. At first sight, it might be tempting to assume that their improved ESG profiles cause the main differences. We introduce Monte Carlo permutation tests and apply them to two S&P 500 ESG indices and find, however, that claims about ESG ratings impacting index performance metrics are not supported by our results. We observe that the better ESG profiles of these indices go hand in hand with exposures to small or value companies, depending on the index construction. Thus, it is essential for investors to adequately assess the methodologies used in constructing ESG indices to make expedient investment and benchmark decisions.<br
Keywords: Permutations tests; ESG; stock indices; benchmarks

Towards Climate Awareness in NLP Research - Hershcovich, Daniel; Webersinke, Nicolas; Kraus, Mathias; Bingler, Julia Anna; Leippold, Markus (Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022)
The climate impact of AI, and NLP research in particular, has become a serious issue given the enormous amount of energy that is increasingly being used for training and running computational models. Consequently, increasing focus is placed on efficient NLP. However, this important initiative lacks simple guidelines that would allow for systematic climate reporting of NLP research. We argue that this deficiency is one of the reasons why very few publications in NLP report key figures that would allow a more thorough examination of environmental impact. As a remedy, we propose a climate performance model card with the primary purpose of being practically usable with only limited information about experiments and the underlying computer hardware. We describe why this step is essential to increase awareness about the environmental impact of NLP research and, thereby, paving the way for more thorough discussions.<br