Joint Tests of Signaling and Income Smoothing through Bank Loan Loss Provisions*
Bibliographic record
Abstract
Abstract We examine whether and how managers use loan loss provisions to smooth income and to signal their private information about their banks' future prospects. Our paper highlights that the use of the loan loss provision to accomplish more than one objective gives rise to situation‐specific costs and benefits of manipulating the provision up or down. We hypothesize that relatively undervalued banks have greater incentives to signal their future prospects than fairly valued banks and that banks' incentives to smooth intensify as premanaged earnings deviate from norms. On the basis of these conjectures, we categorize sample banks into subgroups that are predicted to use loan loss provisions consistent with their situation‐specific incentives. This allows us to refine the research methods used in prior research to examine heterogeneous incentives. While we find evidence consistent with the use of loan loss provisions to smooth earnings, particularly when premanaged earnings are extreme, our evidence on signaling is less consistent. In particular, our signaling results depend on the introduction of an interaction term that has not been used in prior research. We also document that the intensity of smoothing (signaling) is not uniform across the sample. In addition to being a function of the incentive to smooth (signal), it also is a function of the incentive to signal (smooth).
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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 | 0.016 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".