Why does bank screening matter? Private information and publicly traded securities
Bibliographic record
Abstract
Purpose The purpose of this paper is to use an equilibrium model to identify the public and private informational requirements for equilibrium pricing and shows that unless these informational requirements are met, skin-in-the-game policies will not be fully effective against moral hazard for banks with relatively large market share. Selling securitizations with recourse can be. Design/methodology/approach The single-period model shows equilibrium prices depend on both public and private information, the latter produced as banks screen loans. If bank has a sufficiently large market share, it can profit by omitting the screening unless investors can detect the change. The author derives the profit function for not screening, shows that a skin-in-the-game policy cannot fully offset its incentives, and proposes a sale with recourse policy that can. Findings To value securitizations correctly, investors require both publicly and privately available information. If investors cannot monitor banks closely, correct pricing can be frustrated by profit maximization incentives, since banks with large market shares can profit from not screening. Skin-in-the-game policies cannot fully offset these incentives. Research limitations/implications The equilibrium model identifies the public and private informational requirements for equilibrium pricing and shows that unless these informational requirements are met, skin-in-the-game policies will not be fully effective for banks with relatively large market share. Selling securitizations with recourse can be more fully effective. Practical implications If it is difficult for investors to obtain private information, skin-in-the-game policies are not provide fully effective remedies against moral hazard. Sales with recourse policies offer promise because they are easy for investors to understand and difficult to evade. Social implications Trading on the basis of private information can create perverse incentives, and appropriate corrective policies can help offset them. Originality/value The general equilibrium methodology, the findings of incentives to avoid screening, the flaws with skin-in-the-game policies, and the proposal for sale with recourse are all new.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".