Private information and bank‐loan pricing: The effect of upcoming corporate spinoffs
Bibliographic record
Abstract
Abstract Corporate spinoffs are important events that are accompanied by valuation and credit‐risk implications for the parent firm. Among other benefits, spinoffs can improve corporate focus and enhance valuation transparency. In the debt‐contracting context, however, spinoffs can also be associated with negative outcomes for the divesting firms. We examine whether banks, due to their timely access to material private information, are able to ascertain the likelihood and the implications of impending spinoffs for the parent firm before a formal public announcement of the spinoff. Our empirical analyses indicate that, in the 365‐day pre‐spinoff announcement period, banks charge incrementally higher (lower) spreads to borrowers with increased (decreased) post‐spinoff riskiness relative to nondivesting firms. This suggests that, while lenders recognize the value‐ and transparency‐enhancing effects of spinoffs, they are also able to foresee potentially negative implications of these divestitures. Cross‐sectional analyses indicate that banks charge incrementally lower loan spreads if spinoffs result in high‐risk borrowers having either higher reporting quality or lower reporting or operational complexity. These results suggest that the post‐spinoff increase in riskiness is compensated by the divestiture benefits typically associated with spinoffs. Similarly, high‐risk borrowers incur larger spreads if they do not undergo “focus‐increasing” spinoffs. Overall, our findings suggest that banks are able to ex ante determine the implications of important corporate events such as spinoffs.
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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 | 0.002 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".