Saint Mary’s University DO BANKS ALWAYS MAKE INFORMED LOANS?--- A CANADIAN STUDY *
Bibliographic record
Abstract
Bank lending is an important investment activity in an economy. It is widely held that banks are well informed about their borrowers, and banks use such information in their lending decisions to preserve the qualities of their loan portfolios. In this study, we propose a model of the interplay between banks and financial analysts who follow their stocks, and characterize equilibrium with a view to examine how diversely able (heterogeneous) banks lending to the same customer may impact banks ’ loan decisions. Distinguishing between H-banks and L-banks, those with high or low lending ability, respectively, we illustrate how two factors can work to bring an L-bank to act rationally against its valuable signal about its borrower in its loan decision-making, thereby blunting the claim of informed lending by banks. The factors are: 1) the asymmetry arising from banks having private information about their own lending abilities; and 2) the concern of the banks to have financial analysts assess favorably such abilities. After identifying the equilibrium conditions under which L-banks would make uninformed loans, we test the model’s implications using a sample of Canadian bank loan announcements. Our results reveal that while we confirm the well-documented evidence that bank financing in general conveys information about the borrowers, we also find support for the key predictions of our model: 1) the bank-financing announcements cause market reactions that are the strongest for deals involving single banks only, as opposed to multiple banks; 2) market reactions to bank financing are inversely related to the borrower firms ’ prior prospects, indicating that bank financing agreements convey most information only for uninspiring borrower firms. These results shed some light on bank lending practices in Canada. 1.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".