Equity and Debt Market Responses to Sovereign Credit Ratings Announcements
Bibliographic record
Abstract
We study the impact of changes in sovereign ratings and outlooks on international capital markets using a comprehensive database of 34 countries, covering the major regions in the world over the period 1990-2000. We find the rating agencies provide financial markets with new tradable information. Specifically, they affect not only the instrument being rated (bonds) but also stocks. Interestingly, bond markets react differently than stock markets in many respects. We find, only for bond market returns, a positive impact is significant when the economic outlook is upgraded and outlook changes appear to be at least as important as rating changes. In addition, downgraded ratings and economic outlooks occur mainly during bond market downturns, raising a possibility that rating agencies may exacerbate a bond bear market. Only downgrade has a discernible impact on equity and bond returns and the effects of rating announcement are significantly asymmetric. On equity returns, the market responses of downgrade are more pronounced in the cases of high inflation, low fiscal balance, and local currency debt; in contrast, the market responses of downgrade across class are more pronounced in the cases of low current account and foreign currency debt. On bond returns, the market responses of downgrade are more pronounced in the cases of a relatively ailing economy as proxied by emerging market, high inflation, and low current account; on the other hand, the market responses of downgrade across class are more pronounced in the cases of a relatively healthy economy as proxied by low inflation, high liquidity, and during non-crisis period. This study has important implications for investors' international asset allocation and for regulatory agents such as the Basel Committee increasingly depending on credit rating agencies such as Moody's and S&P's in their regulatory deliberations.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".