Income smoothing in banks: Obfuscation or information?
Bibliographic record
Abstract
Abstract Discretionary income smoothing has been argued to increase bank opacity and degrade financial system stability by making banks more difficult to monitor. However, no direct empirical association between discretionary smoothing and opacity has been established to date. We argue that smoothing could reflect either the opportunistic exercise of discretion that disconnects loan loss provisions (LLPs) from changes in underlying credit quality, consistent with smoothing increasing opacity, or an informative exercise of discretion to communicate forward‐looking information about loan losses. We examine the association between discretionary smoothing and the informativeness of LLPs for a sample of banks from 1994 to 2019 and find that discretionary smoothing is, on average, associated with more informative LLPs. However, this association is nuanced, with cross‐sectional differences and changes over time. We find evidence that an intervention by the SEC into bank LLP practices in the late 1990s curbed opportunistic smoothing via provisioning for homogeneous loans. Subsequently, smoothing is associated with more informative provisions, including for banks with both more homogeneous and more heterogeneous loan portfolios. Our findings are inconsistent with the notion that smoothing may be associated with greater opacity.
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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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".