A Study of Various Technique’s for Measuring Banking Efficiency – A Significant Look at Data Envelopment Analysis
Bibliographic record
Abstract
The following research article discusses the technique to measure the technical efficiency of Decision Making Units(DMUs). Efficiency means to measure how well the DMUs are doing given the circumstance & inputs. Decision Making Units are similar enterprises ranging from publically held companies , Privately owned corporation, Banks, Non-profit organization, airports etc. There are many ways to calculate efficiency of DMUs. This research paper will focus on the Banking sector. Financial Ratios is one of leading methods to calculate efficiency of Banks. Liquidity, profitability, & leverage position of the enterprises are calculated to understand the ranking of various Banks. Data envelopment Analysis technique is the latest methodology to benchmark performance of Banks. Data envelopment Analysis is a non-parametric approach used through linear programming to decide efficiency of similar enterprises. The following research paper discusses the various studies conducted by authors using both financial ratios& DEA. The article also discusses research work carried out by authors on Data Envelopment Analysis, specifically.
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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 | 0.050 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".