Credit Rating Changes and Stock Market Reaction in the Kingdom of Bahrain
Bibliographic record
Abstract
Between 2014 and 2015, the oil price almost halved. Since then, it has fallen a further 40%. Consequently, Moody’s Investors Service has downgraded Bahrain’s long-term issuer rating from Baa3 to Ba1with a negative outlook and placed it on review for further downgrade. In this context, previous literature reaches no agreement about the impact of credit rating changes on stock prices. Some studies indicate that credit rating changes do not affect stock prices, while others conclude they do. Therefore, this study aims to examine whether credit rating change has a significant impact on Bahraini stock prices. We conducted an event study to analyze stock market reaction to such news in the Kingdom of Bahrain. Even though Bahrain has witnessed a series of sovereign downgrades over the past five years, the latest downgrading event in February 17, 2016, has been followed by a credit rating downgrade of its banking sector in March 7, 2016. Hence the choice of the sample period of the event study includes both these downgrading events over the period of study from January 2, 2014 till March 22, 2016. Three sectors were selected from the Bahrain all share index: banks, service and industrial. The findings of the study reveal that sovereign rating downgrade has some mixed pre-announcement and post-announcement effects and credit rating downgrade provides useful information. Overall, the results indicate that downgrades and negative outlook announcements have an adverse impact on long-term equity returns, but little impact on short-term performance.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".