Inferring Accounting Information from Corporate Financing Choices: An Examination of Security Issuances in the Banking Industry*
Bibliographic record
Abstract
Abstract This study examines the impact of regulatory capital and several of its determinants (i.e., earnings, loan loss provisions, charge‐offs and growth) on bank managers' financing decisions and investors' interpretations of those decisions. The analysis is related to two streams of research. We add to the corporate finance literature that seeks to explain the market's reaction to security issuances by developing and testing a refined set of predictions of the demand for debt and equity capital using a sample of capital‐regulated firms (banks). We extend the accounting literature that links regulatory capital‐management decisions with bank performance by examining whether investors infer that performance. We find that bank managers' financing choices reflect their private information regarding the levels of regulatory capital, earnings, and charge‐offs in the issuance year. We document a negative market reaction to capital‐increasing issuances and a positive reaction to capital‐decreasing issuances. A cross‐sectional analysis of that market reaction indicates that investors infer managers' expectations of earnings in the issuance year.
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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 | 0.005 | 0.048 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".