Effects of Informal Institutions on the Relationship between Accounting Measures of Risk and Bank Distress
Bibliographic record
Abstract
ABSTRACT We investigate the effects of informal institutions on the relationship between accounting-based risk measures and bank distress. We conduct our analysis in two stages. In the first stage, we extend the prior literature by documenting a link between accounting-based risk measures and bank distress during the 2008–2009 financial crisis. In particular, given the environment characterized by rapid growth in financial innovation and complex financial transactions prior to the crisis, simple accounting-based risk measures continue to predict bank distress during this crisis period. In the second stage, we address our main research question related to the effects of selected informal institutions (societal trust, religiosity, and the media) in enhancing the predictive ability of accounting-based risk measures. As hypothesized, we find that these informal institutions enhance the predictive ability of accounting-based risk measures. Our results inform regulators that the focus on strengthening formal institutions should not ignore country-specific informal institutional structures. JEL Classifications: G21; G28; G34. Data Availability: Data are available from the sources cited in the text.
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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 | 0.004 | 0.053 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".