Proposed Model for Forecasting the Intrinsic Value of Commercial Applied to Commercial Banks Listed on the Bahrain Stock Exchange
Bibliographic record
Abstract
Here we propose a model to evaluate and forecast the intrinsic value of banks, a more appropriate approach as opposed to considering their valuation based on market value. This is because the capital markets in the Arab region, when viewed within the framework of a set of explained variables, prove to be inefficient. These variables include: profitability, capital adequacy, weights of the bank assets that indicate the associated size and various risks according to the Basel committee’s two and the operational efficiency variable. The last variable in question reflects the efficiency of the bank’s internal operations, and the total investments of a commercial bank as proxy of the size of bank assets and financial leverage: the impact of the financial risk on the intrinsic value of the commercial bank. The study used the multi-regression panel data to forecast the value of banks using cash flows approach discounted at the weighted average cost of capital of both equity and borrowed capital. The study found that the three variables: capital adequacy, operational efficiency, and financial leverage explained the intrinsic value of Bahrain commercial banks. The study structure included the following: introduction, review of the relevant literature, hypotheses, methodology and data, mathematical model, empirical results, conclusion, and finally recommendations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".