Methodology
QuarterCall Research is built on publicly available NCUA 5300 Call Report data: the quarterly financial filings that every federally insured credit union submits to the National Credit Union Administration. This page sets out the data sources, analytical choices, proprietary metrics, and known limitations behind our research.
Data Source
NCUA 5300 Call Report
Quarterly (March, June, September, December)
All federally insured credit unions (4,215 as of latest quarter)
March 2019 – June 2026 (30 quarters)
The Call Report is a mandatory supervisory filing: every federally insured credit union, regardless of size, charter type, or geography, submits the same form on the same schedule. The dataset therefore covers the whole population, with no selection bias, no opt-in, and no voluntary disclosure.
QuarterCall Research ingests the raw Call Report archives published by the NCUA, maps account codes through temporal transitions (the NCUA periodically adds, removes, and renumbers fields), and produces a normalized analytical database spanning the full history.
Interpretation Principles
QuarterCall Research's editorial review holds every report to these principles for interpreting data and framing analysis.
Data over narrative
Where the numbers and the conventional wisdom disagree, we follow the numbers. Every directional claim in a QuarterCall Research report, such as "delinquency rose," "margins compressed," or "capital improved," is backed by a specific computed value. Claims without numbers are not published.
Direction matters more than level
A system at 1.00% delinquency and falling is in better shape than one at 0.60% and rising. QuarterCall Research analysis therefore emphasizes velocity (quarter-over-quarter change) and acceleration (whether the rate of change is itself increasing or decreasing) over the current level alone. The CCPI is built entirely on this principle: the level gives context, and the direction carries the information.
Aggregates mask dispersion
System-wide ratios are computed from aggregated dollar totals, not by averaging institution-level ratios. That weights larger institutions correctly, but it also means a $200B credit union moving 5‱ can offset thousands of small credit unions moving in the opposite direction. QuarterCall Research therefore breaks out every key metric by five asset-size cohorts (see below) and flags cases where the system aggregate diverges materially from the cohorts.
Conflicting signals are the norm
A healthy credit union system does not move uniformly across every CAMELS dimension. Capital can strengthen while credit quality deteriorates, as it did in 2024–2025, when earnings retention rebuilt buffers even as delinquency hit dataset highs. QuarterCall Research reports each dimension independently and lets the reader weigh them against one another; it does not reduce them to a single "good" or "bad" verdict. The CUFSI gives a composite reading for those who want one, presented alongside its pillar-level decomposition.
No forecasts
QuarterCall Research describes what the data shows and identifies the trajectory. We do not project future values, predict regulatory actions, or model hypothetical scenarios. When we write "if this trend continues," we are describing the arithmetic, not making a prediction.
Analytical Framework
CAMELS Alignment
All QuarterCall Research analysis is organized under the CAMELS framework — Capital adequacy, Asset quality, Management, Earnings, Liquidity, and Sensitivity to market risk — the same structure used by NCUA examiners. It was chosen so that QuarterCall Research output maps directly to the categories that boards, examiners, and ALM teams already think in.
Aggregation Method
System-wide ratios are always computed from aggregated dollar totals, never by averaging institution-level ratios. For example:
A $50B credit union contributes more to system-wide capital adequacy than a $5M credit union, and summing the dollars before dividing gives each that weight. The tbl_ratios table in the source data contains pre-computed ratios for each credit union, but QuarterCall Research does not average these for system-wide figures.
Annualization
Income-statement items in the Call Report are cumulative year-to-date. QuarterCall Research annualizes using the standard factor:
All annualized figures are labeled as such. First-quarter annualization amplifies single-quarter noise by a factor of four; we flag it when a first-quarter metric shows an unusual move and the adjacent quarters do not confirm the trend.
Cohort Segmentation
System-wide aggregates can mask meaningful variation by institution size. QuarterCall Research breaks out key metrics by five asset-size cohorts:
Cohort assignment is based on total assets as of the reporting quarter. A credit union that crosses a threshold (e.g., from $90M to $110M) moves to the new cohort in that quarter. Cohort boundaries are fixed and do not adjust for inflation.
Quarter-over-Quarter vs. Year-over-Year
QuarterCall Research uses quarter-over-quarter change as the primary lens for trend analysis. It captures turns faster than year-over-year comparisons, which smooth out short-term movements and can mask inflection points. Year-over-year change is a secondary reference, used particularly for income-statement items, where it eliminates the annualization artifacts that make quarter-over-quarter comparisons of first-quarter figures noisy.
Status Labels
QuarterCall Research uses three status labels for trend assessments:
Metric performing well; trend is positive or stable at healthy levels
Trend deviating from norms; may deteriorate if uncorrected
Active deterioration; metric at or beyond stress thresholds
Statuses are editorial assessments informed by the data, not mechanically derived thresholds. A metric can be at a historically high level and still receive a "Favorable" status if the trend is stable or improving. Conversely, a metric at a normal level can receive "Watch" if it is deteriorating rapidly. These are analytical assessments, not investment recommendations.
Proprietary Metrics
CCPI
Credit Cycle Position Indicator™ (CCPI™)
Classifies the industry's position in the credit cycle based on velocity and acceleration of credit-sensitive metrics.
Components:
| Component | Weight | What it measures |
|---|---|---|
| Delinquency (60+ days) | 30% | Borrower distress — the leading indicator |
| Net charge-offs | 25% | Realized credit losses |
| Provision expense | 20% | Management's forward-looking loss estimate |
| Loan growth | 25% | Lending momentum (inverted: declining growth = worsening) |
How it works: For each metric, the CCPI computes the quarter-over-quarter velocity (first derivative) and acceleration (second derivative), z-scores both against the metric's own expanding history, and blends them (70% velocity / 30% acceleration) into a component score on a [-1, +1] scale. The composite CCPI is the weighted average of the four components.
Phase thresholds:
| Phase | CCPI Range | Interpretation |
|---|---|---|
| Expansion | Below -0.25 | Credit quality improving at an above-average pace |
| Late Cycle | -0.25 to 0.09 | Mixed signals; cycle may be turning |
| Contraction | 0.10 to 0.49 | Credit quality deteriorating at an above-average pace |
| Stress | 0.50 and above | Acute deterioration across multiple components |
The CCPI measures direction and momentum, not absolute level: a system with high delinquency but stable (non-rising) rates would score near zero.
CUFSI
Credit Union Financial Stress Index™ (CUFSI™)
A composite 0–100 index measuring system-wide financial stress across six pillars aligned with the CAMELS examination framework.
Pillars:
| Pillar | Weight | Variables |
|---|---|---|
| Credit Quality | 30% | Delinquency ratio, delinquency severity, NCO ratio, non-accrual ratio |
| Capital Adequacy | 20% | Net worth ratio, allowance coverage, allowance-to-loans |
| Earnings Coverage | 15% | ROAA, NIM, provision ratio, operating expense ratio |
| Liquidity | 15% | Borrowing reliance, loans-to-shares, cash buffer |
| Concentration Risk | 10% | Commercial concentration, indirect concentration, participation concentration |
| Growth Stress | 10% | Loan growth, growth-capital divergence |
How it works: Each of the 19 input variables is z-scored against its expanding history, oriented so that positive = more stress, averaged within its pillar, then combined into a weighted composite. The composite z-score is mapped to 0–100 via hyperbolic tangent:
Severity bands:
| Score | Severity | Interpretation |
|---|---|---|
| 0–30 | Low Stress | Unusually benign conditions |
| 30–45 | Below Average | Mildly favorable |
| 45–55 | Normal | Consistent with historical norms |
| 55–70 | Elevated | Multiple indicators above norms |
| 70–100 | High Stress | Acute system-wide strain |
A score of 50 represents the historical average. The CUFSI measures how stressed the system is now; the CCPI measures which direction the cycle is moving.
Data Limitations
The data behind QuarterCall Research has the following limitations.
Self-reported data
Credit unions file their own Call Reports. The NCUA examines and audits these filings, but errors, restatements, and late corrections occur. QuarterCall Research uses the data as published and does not adjust for suspected reporting errors. In system-wide aggregates, an error at one credit union tends to wash out; in analysis of a single credit union (where offered), it does not.
Account code transitions
The NCUA periodically adds, removes, and renumbers Call Report fields. A given financial concept (e.g., "All Other Investments") may map to different account codes depending on the reporting date. QuarterCall Research maintains a temporal mapping layer that resolves these transitions programmatically — the same field may be read from account code A766E before Q1 2022 and from AAS0016 afterward. These transitions are the core complexity of the data pipeline and the primary reason raw Call Report data is difficult to use in time-series analysis.
Survivorship bias
When a credit union merges, liquidates, or loses its federal insurance, it drops from subsequent quarters. The dataset does not backfill or adjust historical figures, so multi-year comparisons reflect only institutions that survived to the later date. For system-wide aggregates the effect is modest, since mergers typically transfer assets and members. For cohort-level analysis it is larger, particularly in the "Under $100M" cohort, where merger and consolidation rates are highest.
Publication lag
Credit unions file Call Reports within 30 days of quarter-end. The NCUA publishes the aggregated data approximately 60–75 days after the filing deadline. QuarterCall Research publishes research as soon as the data is available, but the most recent quarter in any report is inherently backward-looking by roughly one quarter.
First-quarter annualization noise
Income-statement items reset to zero each January. First-quarter figures represent a single quarter of activity, annualized by multiplying by four. This amplifies noise: a one-time fee income spike in January becomes a 4x-inflated annualized figure. QuarterCall Research flags first-quarter annualization effects in its commentary when they produce outlying values, and uses year-over-year comparisons as a cross-check.
What is not in the data
The Call Report does not capture interest rate risk modeling outputs, internal stress test results, or board governance assessments. CAMELS "M" (Management) and "S" (Sensitivity) dimensions receive less quantitative coverage than "C," "A," "E," and "L." QuarterCall Research acknowledges this asymmetry rather than constructing proxies with false precision.
Update and Revision Policy
Publication cadence. QuarterCall Research publishes Pulse, Signals, and associated research articles within two weeks of the NCUA's Call Report data release, typically in March, June, September, and December.
Restatements. If the NCUA issues a material correction to a prior quarter's data, QuarterCall Research updates the underlying database in the next publication cycle. We do not retroactively revise published reports — instead, the correction is noted in the subsequent quarter's editorial. Historical charts and tables always reflect the latest database state.
Methodology changes. Changes to proprietary metric definitions (e.g., CCPI component weights, CUFSI pillar composition) are documented at the time of change and noted in the affected publication. Historical values are recomputed under the new methodology to maintain comparability. We do not maintain parallel series under old and new definitions.