Using FinBERT as a refined approach to measuring impression management in corporate reports during a crisis
Copyright (c) 2023 Caroline Cherry, Waheeda Mohamed, Yogesh Brahmbhatt
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This paper presents the findings of sentiment analysis as a refined approach to detecting impression management that may be present in the Chairman’s Statements of companies during an exogenous event such as the Corona virus pandemic. FinBERT, a more advanced machine learning model of natural language processing (NLP), was used to investigate the change in net sentiment expressed in the Chairman’s Statements of a sample of South African JSE-listed companies before and during the pandemic. A computation of net sentiment for each report was performed. Overall, no generic pattern of communication in the Chairman’s Statements emerged between the periods researched. Impression management tactics may vest within reports on an entity-specific basis and may be the exception, not the rule. Considering the increasing amount of unaudited narrative disclosure presented in formal corporate communication, consideration must be given to whether the sentiment expressed in these formal corporate reports is balanced, clear and transparent. Content analysis has historically been labour-intensive. More accurate ways of analysing growing bodies of financial text would be relevant to investors, analysts, key stakeholders, policy makers and academics. Pre-trained NLP models such as FinBERT offer a specialised way of understanding the sentiment of financial text. Research exploring impression management in corporate narrative sections using FinBERT is still gaining momentum across the world and is limited in South Africa. To the researchers’ knowledge, this is one of the first South African studies to employ FinBERT as an innovative, accurate and efficient approach to analysing the sentiment in the Chairman’s Statement.
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