Price Behaviour around Share Buyback in the Indian Equity Market
Bibliographic record
Abstract
Share repurchase is becoming an important corporate practice in India of late. But, there exists a paucity of systematic study regarding the motives, nature and impact of buyback on share prices of respective companies. This article makes an attempt to examine the effect of share repurchase announcement by Indian companies through open market route during 2008–2012 on their share prices around the announcement date. The article contributes to the literature by analyzing the market reaction to share buyback announcement, by applying the market model not used so far in the Indian context and by undertaking a rigorous analysis of share repurchase. Though share repurchase has not come up yet as a regular or useful practice by Indian companies like those in the US or Canada, our analysis does throw some light on the issue with interesting findings. First, unlike the US market, the trend in average additional return does not support any motive like undervaluation or maximizing shareholders’ value. Second, the cumulative abnormal returns (CARs) also do not reveal any increase in share price of the company after the repurchase announcement. Third, the sample shows that more of small and unknown companies go for share buybacks compared to known or large companies. Fourth, most importantly, the average abnormal returns (AAR) are not statistically different from zero in most of the cases both in pre- and post-announcement periods, implying that this corporate activity does not carry much information to the investors, possibly because of the ownership structure of Indian companies being majority owned or otherwise controlled by promoters. The lesson for the company is that it cannot revive the share prices through repurchase announcements in India. The implication for the regulator might be to check the real motives of such buybacks in India and accordingly formulate policies.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".