Interdependence between Managerial Ownership, Leverage and Firm Value: Theory and Empirical Validation
Bibliographic record
Abstract
This paper test the interdependence between managerial ownership, debt and firm value. To this end, we examined a sample of 246 French firms over a period of 11 years is built. In addition, we use two estimation methods: simultaneous equations and data panels methods. The empirical results support the interaction between these three variables. We concluded a nonlinear relationship between insider ownership and shareholder wealth. An inverse U-shaped relationship was found between debt and managerial ownership. However, an increase in debt leads to an increase in managerial ownership. Moreover, the share capital held by managers is a significant factor in explaining debt ratio of French firms. Finally, we conclude that the disciplinary role of debt is valid only for the data panels method. al Jordanian firms listed at Amman Stock Exchange (ASE) for the period of 2005-2013, by applying panel data regression analysis. It depends on building three OLS models: Pooled, Fixed Effects Model and Random Effects Model. In addition, a test for Breusch and Pagan Lagrangian multiplier (LM), and Hausamn test to choose among the three models which model is most suitable for our data. A main finding of the panel data analysis is that; fixed effect regression is the most convenient model. As a result, there is no strong evidence that there is a relationship between both institutional ownership and firm performance for Jordanian listed firms. This conclusion can be due to the fact that institutional ownership has its own pros and cons, therefore, their existence and influence could affect materially the types and risk level of investment decisions taken by the management which in return will affect the firm’s performance as a whole. ociation with external reserve and net credit to the economy. Based on these results; it is recommended that, the Nigeria government should designed programmes and incentives to boost industrial capacity utilization in the country. Markets determine nominal exchange rate should prevail in the economy. The country should regulate its foreign reserve policy by setting a threshold, above which excess deposit should be plough back to the domestic economy inform of investments rather than support excessive importation.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".