Corporate Governance and Credit Risk : Evidence on Indian Firms Using Mixed Method
DOI:
https://doi.org/10.17010/pijom/2024/v17i7/173633Keywords:
corporate governance
, bankruptcy, textual, ratios, logistic, predictive.JEL Classification Codes
, G32, G33, G34Paper Submission Date
, February 20, 2024, Paper sent back for Revision, May 30, Paper Acceptance Date, June 8, Paper Published Online, July 15, 2024Abstract
Purpose : The research study focused on analyzing the impact of word sentiments, corporate governance, and financial variables on the credit risk of Indian companies. The increasing significance of credit risk in lending decisions and the numerous financial crises motivated us to conduct this study.
Design/Methodology : In addition to the 55 Indian enterprises with the best credit ratings, we included 57 manufacturing companies under the insolvency and bankruptcy code. Finding offensive terms in the organizations’ corporate governance (CG) reports, according to the Loughran–McDonald vocabulary, was the first goal. Additionally, panel logistic regression was run on three models: A combined model containing all factors, a model containing financial variables, and a model containing CG variables.
Findings : We discovered through textual analysis that IBC firms had a significantly more negative tone than solvent firms. CG elements are thought to be significant for accurately predicting credit risk, and their combination with financial measures improved predictive capacity, according to the results of the quantitative models. In conclusion, the findings were thought to be the most reliable in explaining occurrences related to bankruptcy or liquidation.
Practical Implications : It was suggested that stakeholders examine beyond financial ratios, which corporations may have “window dressed†in order to avoid default, by employing a mixed-method approach that focused on corporate disclosures and attitudes alongside quantitative information.
Originality : In contrast to earlier studies on CG, the current study used a mixed-method approach to analyze a company’s creditworthiness from the perspective of lenders.
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