Corporate Disclosures and Financial Performance of Selected Indian Manufacturing and Non- Manufacturing Companies
Bibliographic record
Abstract
Disclosures play a pivotal role in addressing the issues related to information asymmetry and agency costs of firms. Voluntary disclosures are those disclosures which are beyond the compulsory ones and are communicated portraying a better image of the company and its prospects in front of its investors. Therefore this study intends to extend various aspects of corporate disclosure practices, mainly voluntary disclosures of selected Indian companies. Under contract theory of economics, information asymmetry leads to superfluous decisions because of information gap between the parties. It is also important to assess the possible economic consequence of voluntary disclosures i.e. the incentive by way of enhanced firm performance. The results of the study obtained through correlation and regression approach are very encouraging and are evident of stock returns responding to corporate voluntary disclosures, financial as well as non- financial, particularly in the recent years. The effect of increased disclosures on stock prices is of possible interest to investing community and stakeholders at large including the policy makers and regulators. It is not just about the level of disclosures but also the type of disclosures e.g. non-financial disclosure like forward looking, social, and environmental which play an important role in enunciating the association between the two variables. The paper sufficiently contributes towards literature on voluntary disclosures. Its major contribution is focused towards economic consequences of disclosures by way of better stock returns and implications for Indian stock market regulator to assess the impact of its policy on manufacturing and non-manufacturing companies listed in CNX 100 index of National Stock Exchange of India.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".