Does CDS Slope Predict Future Stock Returns? Evidence from the Korean Market
Bibliographic record
Abstract
We provide evidence that current CDS slope negatively predicts future stock returns over several months in the Korean market. The entire sample period covers January 2003 through June 2009. The empirical results are as follows. First, when constructing quartile portfolios based on the slope of CDS spreads, we find that predictive power of CDS slope lasts for seven months. In addition, the lower the CDS slope is, the higher average stock return is. Specifically, a slope-based strategy of buying the lowest slope and selling the highest slope makes profits over 2% each month. The profitability is statistically and economically significant even after controlling for some risk factors. We also find that the results are robust to various sub-samples, portfolio-weighting schemes, as well as the number of sorted portfolios. This abnormal return cannot be explained by standard risk factors, default risk, and expectation hypothesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".