The Impact of Economic Value Added, Market Value Added and Traditional Accounting Measures on Shareholders’ Value: Evidence from Jordanian Commercial Banks
Bibliographic record
Abstract
The purpose of this is study is to test the impact of economic value added, market value added and traditional accounting measures on the shareholders’ value in the Jordanian commercial banks, based on a sample of 13 banks during the period 2010-2016. The study used the shareholders’ value as a dependent variable, while five independent variables were used, including Economic Value Added (EVA), Market value added (MVA), and three traditional accounting measures, namely; the rate of return on assets (ROA), rate of return on equity (ROE), and the Earning per share (EPS). The study found, by using the common regression analysis, that the rate of return on assets (ROA) and the economic value added (EVA) had a positive and statistically significant effect on maximizing the shareholders’ value, while the rest of the traditional accounting standards or the market added value had no any significant impact on shareholder’ value. The study concluded that traditional accounting standards are still constitute an important input for assessing shares, and maximizing the shareholders’ value along with modern performance assessment measures, especially economic value added. The study recommended that the performance assessment of banks should be based on two criteria: the rate of return on assets and the economic value added.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".