Bibliographic record
Abstract
This study intends to examine, from an economic and accounting standpoint, how the share repurchase procedure affect financial markets by specifically testing their effects on the financial performance of the specific companies. The OLS regression analysis was used on 66 companies that were traded on the American Stock Exchange between 2009 and 2020 as part of the study sample. The results reveal that share repurchases improve financial results, as evidenced by return on equity (ROE) and Added Economic Value (EVA). The results, however, show that share repurchases have little effect on the return on assets (ROA). The study found that the management's justifications for share buybacks affect a company's financial success. The study also found that the management's aim to produce a cash surplus improves the company's financial performance. The management objective of increasing earnings per share, which also improves the firm's financial success, was found to be one of the most significant motivations for the company to repurchase shares, according to the study. The study also showed that share repurchases significantly outperform returns on assets or returns on equity in terms of the Economic Value Added (EVA), one of the most important measures of financial success. The study, however, found little proof that the companies' share repurchases caused by higher financial leverage have an effect on their financial performance. This research, therefore, provided an insight on how share purchases affect the capital markets is dependent on the source of the surplus cash, and relatedly, share purchases through improved earnings per share increases the desirability of the company from their improving their financial standpoint.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".