Impact of Corporate Scams on share prices: A study of Indian Stock market
Bibliographic record
Abstract
This paper studied the impact of the corporate scams on the share prices of the companies. In the pre-fraud period, the typical fraudulent firm has a higher valuation, invests more and exhibits higher sensitivity of investment than industry peers. The fraud period, by contrast, is characterized by sign cant drops in valuation and investment.Corporate scandals around the world in recent years especially Satyam scandal in India created a need contributed to produce quantitative measures on ownership and to estimate their impact on the value and decision-making process of companies. The study of 8 companies has been made which has undergone the scam in the past 8 years& their impact on Indian Stock Market. Event study has been used to examine the impact of corporate scams on stock returns. The AABRs and CAARRs of overall sample are insignificant at 5% level of significance. The study concluded that the market is very efficient they absorb all the information regarding the event.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".