A STUDY ON IMPACT OF BUY BACK OF SHARES ON COMPANY EPS P/E RATIO AND MARKET PRICE OF THE SHARES.
Bibliographic record
Abstract
Share repurchase was evolved as an alternative method of payout and a corporate finance tool in 1950 in the USA. Then, gradually it spread to other countries like UK, Canada, etc. It has achieved a significant growth in the last two decades when compared to dividend payment by companies. Buy back of shares is just the opposite of raising capital through issue of shares. It is a process of capital restructuring which allows a company to buy back its own shares, which were issued by it earlier. Companies go for buy back shares either to increase the value of shares still available, or to eliminate any threats by shareholders who may be looking for a controlling stake. However, in India, a continuous demand has been rising from the corporate sector to buy-back shares in order to increase the rate of earning and also the market price per share for the remaining shares after cancelling a part of the shares.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".