Performance and Assets and Liabilities Management in the U.S. Credit Union
Bibliographic record
Abstract
Abstract Our main research objective is to study the influence of different decisions inherent to allocating assets, and to the weights given to some types of loans on the profitability of credit unions. Only few studies having been carried out on these financial institutions and on their asset portfolio structural change. Another objective is to analyse the influence of increasing deposits, as part of liabilities, on their financial performance. In order to reach our research objectives, we carried out statistical analyses and panel regressions by using biannual data from a large sample of credit unions in the United States. In fact, we analyzed the influence of the choices of allocating some assets and liabilities, represented by ratios, on the profit of credit unions in the United States for 20 years, represented by the return on assets. The results of our analysis enable us to conclude that attracting more deposits would ensure better profitability for these credit unions. As regards to loan types, the increase of the first mortgage loans weight is a profitable strategy. Our research add value to the field of financial institutions management, since most studies concern banks profitability and cannot be generalized to credit unions. Keywords: Credit unions, Credit unions performance, Credit unions asset portfolio structure, Credit union deposits, Assets and liabilities management in banking industry, First mortgage loan.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".