Detecting Bank-Level Liquidity Shifts: Evidence from U.S. Regulatory Data
Bibliographic record
Abstract
In the wake of the 2008–2009 Global Financial Crisis, the Federal Reserve began paying interest on reserves (IOR) on 1 October 2008—an intervention that, along with others, constituted a regime change for U.S. banks. In this study, we investigate whether banks’ liquidity adjustment was progressive and continuous or abrupt and regime-defining, and how adjustment timing differed across institutions. Using quarterly regulatory call reports from 2002:Q4 to 2015:Q4, we estimate a Gaussian hidden Markov model (HMM) to detect bank-specific regime shifts. We then use the inferred break dates in a regression framework that classifies banks’ liquidity behavior over time. We find a discrete upward shift in liquidity around 2008–2009 with pronounced cross-bank heterogeneity. The patterns persist when we stratify by asset size and remain highly concordant across geographic regions and primary regulators. To illustrate its broader relevance, we extend the framework to the COVID-19 era (2017–2023) for the four largest U.S. banks, showing that it captures comparable regime dynamics across successive phases of quantitative tightening and easing.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".