On SGX's Voyage to corporate sustainability: Exploring emerging topics in multi-industry corpora

Ni Xinwen, Lin Min-Bin (), Schillebeeckx Simon J. D. and Härdle Wolfgang Karl
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Ni Xinwen: International Research Training Group 1792, School of Business and Economics, Humboldt-Universität zu Berlin, Berlin, Germany
Lin Min-Bin: IDA Institute Digital Assets, Bucharest University of Economic Studies, Bucharest, Romania
Schillebeeckx Simon J. D.: Lee Kong Chian School of Business, Singapore, Management University, Singapore, Singapore
Härdle Wolfgang Karl: Blockchain Research Center, Humboldt-Universität zu Berlin, Berlin, Germany

Management & Marketing, 2025, vol. 20, issue 2, 47-80

Abstract: Topic modeling, particularly latent Dirichlet allocation (LDA), is widely recognized as a valuable technique for identifying key topics and trends across dynamic content in various fields. LDA’s strength lies in its ability to efficiently capture emerging themes from large text corpora, making it a popular choice for categorization. It facilitates the automation of report reviews, assisting in corporate evaluations and management assessments by uncovering key trends and topics with minimal manual intervention. However, our analysis of sustainability within the corpora of SGX-listed companies reveals limitations when using LDA on sparse data. Specifically, the dynamic LDA approach (dynamic topic modeling, or DTM), applied to an 11-year dataset of annual reports, struggles to detect the rise of sustainability as a significant corporate focus following policy changes. Despite the mandate for sustainability reporting, actual engagement with sustainability issues within these reports remains limited, i.e., highlighting the need for substantial improvements in how companies address sustainability topics.

Keywords: Unsupervised learning; Latent Dirichlet allocation; Sustainability; Singapore exchange (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:manmar:v:20:y:2025:i:2:p:47-80:n:1001

DOI: 10.2478/mmcks-2025-0006

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