Does the Impact of Transparency and Disclosure on the Firm’s Valuation Depend on the ESG?
Bibliographic record
Abstract
The global economic crisis in 1997 significantly impacted all corporate firms. Measuring valuation is becoming increasingly important in corporate firm analysis. Transparency in disclosures enables a company to meet market expectations while also adhering to regulatory requirements. The study’s primary purpose is to measure the impact of transparency and disclosures on the valuation of non-financial firms in India and explore the role of Environmental, social and Governance (ESG) as a moderator variable in determining the firm’s value. Panel data regression is the methodology adopted for the data analysis in the study. Panel Data of seventy-six non-financial firms was collected for ten years (2011–2020). Market capitalization is considered as a proxy variable for the valuation. The study results indicate that transparency and disclosures (TD) have a negative and significant influence on the value of the firms. Inferring that a higher degree of TD reduces the firm value. At the same time, the interaction term of TD and ESG show a positive significant association. This finding implies that high ESG reduces the negative impact of high TD on the valuation.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".