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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ni Xinwen & Lin Min-Bin & Schillebeeckx Simon J. D. & Härdle Wolfgang Karl, 2025. "On SGX's Voyage to corporate sustainability: Exploring emerging topics in multi-industry corpora," Management & Marketing, Sciendo, vol. 20(2), pages 47-80.
    • Handle: RePEc:vrs:manmar:v:20:y:2025:i:2:p:47-80:n:1001
    DOI: 10.2478/mmcks-2025-0006
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    References listed on IDEAS

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    1. Xinwen Ni & Taojun Xie & Wolfgang Karl Härdle & Xiaorui Zuo, 2025. "A machine learning based regulatory risk index for cryptocurrencies," Computational Statistics, Springer, vol. 40(7), pages 3563-3583, September.
    2. Lawrence Loh & Thomas Thomas & Yu Wang, 2017. "Sustainability Reporting and Firm Value: Evidence from Singapore-Listed Companies," Sustainability, MDPI, vol. 9(11), pages 1-12, November.
    3. Dyer, Travis & Lang, Mark & Stice-Lawrence, Lorien, 2017. "The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation," Journal of Accounting and Economics, Elsevier, vol. 64(2), pages 221-245.
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