Expected Credit Spreads and Market Choice: Evidence from Japanese Bond Issuers
Bibliographic record
Abstract
This study explores the impact of credit spreads—defined as the difference between corporate bond yields and matched government bond yields—and macro-financial conditions on Japanese firms’ decision-making regarding whether to issue corporate bonds in domestic or international markets. Using firm-level panel data from 2010 to 2019, we employ fixed-effects regressions to identify the determinants of credit spreads and assess their influence on issuance location. The results suggest that firms strategically opt for foreign markets when anticipating narrower spreads, despite the typically higher borrowing costs associated with overseas issuance. Sensitivity to credit spreads systematically varies with issuer characteristics—such as leverage and credit ratings—and market elements—including the United States volatility and stock performance. Interaction models further demonstrate that market selection dynamically responds to pricing signals and uncertainty. By connecting credit spread formation to venue choice, this study provides a new perspective on cross-border financing in segmented capital markets. These findings offer theoretical insights and practical implications for understanding how firms adapt their debt strategies in response to global financial conditions.
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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 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".