The impact of corporate governance on narrative disclosure tone: a machine learning approach

Arshad Hasan, Usman Sufi, Mahmoud Elmarzouky and Khaled Hussainey

Journal of Applied Accounting Research, 2024, vol. 26, issue 3, 577-602

Abstract: Purpose - This study examines the influence of corporate governance indicators (CGIs) on the textual tone of nonfinancial firms in a developing economy. Design/methodology/approach - The data from 1,250 annual reports of listed nonfinancial firms in Pakistan are collected for 10 years. The narrative disclosure tone (NDT) is derived using the sentiment analysis of annual reports, resulting in six distinct NDT scores. The CGIs data are also extracted from the annual reports. The fixed effects model is used as the primary analytical tool, supplemented by machine learning-based linear regression. System GMM and two-stage least squares regressions are employed for robustness checks. Findings - The findings reveal that most CGIs significantly influence all six NDTs. These results align with the existing theoretical literature, except those related to audit committee independence and gender diversity. Research limitations/implications - The study is limited to the use of annual reports as a source of narrative disclosures. Future research might employ other sources, such as earning press releases and social media. Practical implications - Within the unique regulatory environment of Pakistan, the study offers insights for regulators to enhance the efficacy of independent directors, discourage concentrated ownership and promote the inclusion of women in board subcommittees to establish the authenticity of textual disclosures. Originality/value - The study adds to the limited literature on the determinants of NDT. It underscores the importance of understanding textual tone for informed investor decision-making and restoring investor confidence. Moreover, it contributes by focusing on six NDTs and exploring the interplay between CGIs and textual tone.

Keywords: Narrative disclosure tone; Corporate governance; Firm performance; System GMM; Machine learning; Natural language processing (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eme:jaarpp:jaar-10-2023-0323

DOI: 10.1108/JAAR-10-2023-0323

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