Other People’s Money: How CEOs Create Value for Shareholders During Good Times or Bad
Bibliographic record
Abstract
The chief executive officer (CEO) of any enterprise has a tremendous role to play in determining the direction of the organization. His choice of funding pattern for the business could determine the level of profitability and robustness of the enterprise. Whether the business should be funded with debt (i.e., other people’s money) or equity is a decision the CEO has to repeatedly make in the course of piloting the ship of the organization. This paper looks at the various ways the CEO can create value for the shareholders in good times or bad, and what risks he must confront squarely in order to ensure that his efforts yield the desired results. The paper takes a critical look at funding with debts in the light of the Modigliani & Miller Theory, and concludes that the CEO does constantly explore ways by which to increase the profi tability of the business, and employs other people’s money to maximize wealth for the shareholders. His key approaches include constant improvement of the annual returns and taking appropriate risks aimed at attaining the enterprise’s set growth goals. This paper will be beneficial to corporate fi nance managers and entrepreneurs who repeatedly face decision-making on what fi nancial portfolios to engage in order to attain maximum wealth for the shareholders. Key words: Chief Executive Officer; Shareholders; CEO’s role
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".