The Effect of Institutional Ownership on Firm Performance: Evidence from Jordanian Listed Firms
Bibliographic record
Abstract
<p>Last decade witnessed successive corporate scandals for various firms that points to a failure of corporate control. Expertize and interested parties all over the world proposed to focus on monitoring the management decisions to reduce such failure in firms. Therefore, the structure of ownership became more and more as an important issue to increase both efficiency and effectiveness of management decisions. This study seeks to investigate whether institutional ownership affects the firm’s performance for one of the emerging markets; Jordan. Firm’s performance is measured through applying two accounting measures Return on Assets (ROA) and Return on Equity (ROE), with 6 explanatory variables. Our sample is unique and contains 82 non-financial 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.</p><p> </p>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".