Using Disaggregated Return on Assets to Conduct a Financial Analysis of a Commercial Bank Using an Extension of the DuPont System of Financial Analysis
Bibliographic record
Abstract
In this paper, we use an expanded version of the DuPont system of financial analysis to perform a financial analysis of a bank using disaggregated data to computer return on assets. The DuPont system of financial analysis is based on return on equity which is based on net profit margin, total asset turnover, and the equity multiplier. We further disaggregate net profit margin into three components: return on loans, return on securities, and return on other assets using supplementary data provided in the SEC filings of Monarch Bank. The analysis covers the period from 2003 to 2010. Our analysis demonstrates that return on assets for Monarch Bank derives primarily from return on loans. That is, 86% of the investment weighted return on assets for Monarch Bank derives from return on loans.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".