On SGX’s Voyage to Corporate Sustainability: Exploring Emerging Topics in Multi-Industry Corpora

Abstract

Topic modeling and LDA (Latent Dirichlet Allocation) have proven valuable in various fields as an innovative approach to studying areas of interest and identifying topics in a dynamic content. The underlying assumption is that techniques like LDA can swiftly capture emerging topics in textual documents compared to other categorization tools. These unsupervised approaches have been used to identify new industries and technological domains. However, our study on the nascent topic of “sustainability” within the corpora of SGX-listed companies highlights clear limitations in employing techniques like LDA on sparse data. The dynamic LDA approach, also called DTM (Dynamic Topic Modelling),based on an 11-year database of annual reports from publicly listed companies in Singapore, could not detect sustainability’s rise as a critical topic in corporate practice following policy changes. Moreover, despite sustainability reporting becoming mandatory, sustainability-related topics may still not receive significant attention.

Keywords: nsupervised learning, Latent Dirichlet Allocation (LDA), sustainability, Singapore Exchange (SGX)

JEL Classification: C19, C55, O16, Q56