The Influence of Transparency and Disclosure on the Valuation of Banks in India: The Moderating Effect of Environmental, Social, and Governance Variables, Shareholder Activism, and Market Power
Bibliographic record
Abstract
Research on the impact of transparency and disclosures (TD) on the firm’s valuation presents an ambiguous result. The effect of disclosure on value is a concern because disclosure is not an economic activity. It grows further due to the embellishment of positive disclosures and the suppression of hostile facts. This situation has motivated the authors to conduct the current research. The study aims to empirically find the influence of TD on the valuation of banks in India while the Environmental, Social, and Governance Index (esgi), Shareholder activism index (shai), and Lerner Index (li) act as moderators. A panel data regression (PDR) is adopted to analyse the data in the study. Panel data for 31 public/private banks for ten years (2010–2019) are collated. The authors used econometric models to understand the linear, quadratic, and interaction association of Transparency and Disclosure (TD) with the valuation of the banks in India. It is empirically found that TD alone does not impact the valuation of banks but is positively associated with a bank’s value under the influence of the moderators, Environmental, Social, and Governance variables (esgi), and shareholder activism (shai).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".