Understanding CSR Champions: A Machine Learning Approach
Alona Bilokha, Mingying Cheng, Mengchuan Fu, Iftekhar Hasan
Annals of Operations Research,
forthcoming
Abstract
In this paper, we study champions of corporate social responsibility (CSR) performance among the U.S. publicly traded firms and their common characteristics by utilizing machine learning algorithms to identify predictors of firms’ CSR activity. We contribute to the CSR and leadership determinants literature by introducing the first comprehensive framework for analyzing the factors associated with corporate engagement with socially responsible behaviors by grouping all relevant predictors into four broad categories: corporate governance, managerial incentives, leadership, and firm characteristics. We find that strong corporate governance characteristics, as manifested in board member heterogeneity and managerial incentives, are the top predictors of CSR performance. Our results suggest policy implications for providing incentives and fostering characteristics conducive to firms “doing good.”
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European Real Estate Index (EREI) 2025
Michael Koetter, Felix Noth, Fabian Wöbbeking
IWH Technical Reports,
No. 1,
2025
Abstract
This Technical Report documents the construction and coverage of the IWH European Real Estate Index (EREI). Since 2018, we have used machine-learning methods to collect monthly listings of residential real estate available for sale or rent in up to 20 European countries. The Technical Report documents the cleaning and selection process and describes the data regarding coverage, moments, and frequencies to construct the EREI.
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Courses
Courses Courses are organised in coordination with partner institutions within the Central-German Doctoral Program Economics (CGDE) network. IWH organises First-Year Courses in…
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DPE Course Programme Archive
DPE Course Programme Archive 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2025 Microeconomics several lecturers winter term 2024/2025 (IWH) Econometrics…
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Exploring Accounting Research Topic Evolution: An Unsupervised Machine Learning Approach
June Cao, Zhanzhong Gu, Iftekhar Hasan
Journal of International Accounting Research,
No. 3,
2023
Abstract
This study explores the evolution of accounting research by utilizing an unsupervised machine learning approach. We aim to identify the latent topics of accounting from the 1980s up to 2018, the dynamics and emerging topics of accounting research, and the economic reasons behind those changes. First, based on 23,220 articles from 46 accounting journals, we identify 55 topics using the latent Dirichlet allocation model. To illustrate the connection between topics, we use HistCite to generate a citation map along a timeline. The citation clusters demonstrate the “tribalism” phenomenon in accounting research. We then implement the dynamic topic model to reveal the dynamics of topics to show changes in accounting research. The emerging research trends are identified from the topic analytics. We further explore the economic reasons and in-depth insights into the topic evolution, indicating the economic development embeddedness nature of accounting research.
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"Let Me Get Back to You" — A Machine Learning Approach to Measuring NonAnswers
Andreas Barth, Sasan Mansouri, Fabian Wöbbeking
Management Science,
No. 10,
2023
Abstract
Using a supervised machine learning framework on a large training set of questions and answers, we identify 1,364 trigrams that signal nonanswers in earnings call questions and answers (Q&A). We show that this glossary has economic relevance by applying it to contemporaneous stock market reactions after earnings calls. Our findings suggest that obstructing the flow of information leads to significantly lower cumulative abnormal stock returns and higher implied volatility. As both our method and glossary are free of financial context, we believe that the measure is applicable to other fields with a Q&A setup outside the contextual domain of financial earnings conference calls.
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Department Profiles
Research Profiles of the IWH Departments All doctoral students are allocated to one of the four research departments (Financial Markets – Laws, Regulations and Factor Markets –…
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DPE Course Programme Archive
DPE Course Programme Archive 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2023 Microeconomics several lecturers winter term 2023/2024 (IWH) Econometrics several…
See page
Department Profiles
Research Profiles of the IWH Departments All doctoral students are allocated to one of the four research departments (Financial Markets – Laws, Regulations and Factor Markets –…
See page
23.05.2023 • 14/2023
Analysis of earnings calls: Blathering managers harm their company
If a senior executive refuses to give information to professional investors, the company's stock market value drops afterwards. This is shown in a study by the Halle Institute for Economic Research (IWH) after evaluating
1.2 million answers from tele-phone conferences.
Fabian Wöbbeking
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