Predicting First Observed Appearance in a Corporate Governance Rating List and Subsequent Upgrades with Interpretable Machine Learning
DOI:
https://doi.org/10.31181/jidmgc31202754Keywords:
Corporate governance, Machine learning, Explainable artificial intelligence, Index certification, Rare eventsAbstract
Corporate governance certification provides a public assessment of organizational practices. Previous research has primarily examined the relationship between certification and firm outcomes, while the timing of certification adoption represents a distinct prediction problem. This study examines whether the first observed appearance on a governance rating list and subsequent rating upgrades can be predicted using statutory accounting disclosures. Rating history is also incorporated into the upgrade model. The institutional setting is the Borsa Istanbul Corporate Governance Index, where licensed agencies evaluate four governance sections and eligibility is determined by published thresholds. The dataset combines rating records for 49 non-financial firms from 2013 to 2024 with financial statements from 438 listed non-financial firms. Two interpretable modeling modules employ an out-of-time design, with predictor variables measured before the corresponding outcomes. The entry module compares L2-regularized logistic regression, Firth-penalized logistic regression, and gradient-boosted trees within a discrete-time hazard specification. The trajectory module estimates next-year rating upgrade scores for rated firms using accounting information and rating history. Both linear entry models achieve areas under the receiver operating characteristic (ROC) curve above 0.50, while the regularized logistic model shows a small positive difference in average precision relative to the training-prevalence benchmark. A single deflated firm-size measure provides discriminatory performance comparable to that of the multivariate entry models. The combined upgrade models achieve ROC areas close to 0.60, while models incorporating rating history achieve higher ROC areas than accounting-only models. Feature attributions also differ between the two tasks, with firm size playing a prominent role in entry predictions and rating history contributing more strongly to upgrade predictions. These findings identify the information sets associated with observed rating adoption and progression. The proposed framework provides rankings of candidate firms and feature-level explanations to support governance monitoring and outreach planning.
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