The Effect of Accounting and Market Indicators on Predicting the Stock Prices for Jordanian Banks: An Econometric Study for the Period (2010-2015)
Bibliographic record
Abstract
This study aims at examine the ability of a group of financial ratios, which is derived from the financial statements of Jordanian commercial banks, to predict the prices of the market shares for the period (2010-2015). Besides, it investigates the explanatory power and the nature of the relation between some accounting and market indices, including compound and individual indicators, and the market share price. In order to achieve the objectives of this study, the researcher used the Panel Data and the time series data. While the Hussmann Test is used to choose the appropriate model whether it is a static effect modal or a random effect one based on Chi-Square probability value.Throughout the discussion and the data analysis, the study highlights a set of results. One of the most important results shows that the effects of the independent variables as a single package on predicting the stock market prices were very strong.In addition, the researcher comes up with some recommendations which emphasize the importance of disclosing the financial statements under study to investors and analysts periodically due to the importance of transparency in the financial sector. Moreover, while distributing earnings on participants, investors’ preferences should be taken into consideration due to their influence on the share price since it is a futurist result of the investors’ evaluations of the earnings distribution policy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".