The Effect of COVID-19 on the Relationship between Idiosyncratic Volatility and Expected Stock Returns
Why this work is in the frame
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Bibliographic record
Abstract
This study examines the effect of the COVID-19 pandemic on the relationship between idiosyncratic volatility and expected stock returns. Using daily stock return data in the US market from the Center for Research in Security Prices (CRSP), we estimate monthly idiosyncratic volatility and investigate the effect of the COVID-19 pandemic at the portfolio and firm level. The results of portfolio analysis and cross-sectional regression show that the relationship between idiosyncratic volatility and subsequent stock returns switches from negative to positive during the pandemic period. Furthermore, we find that the relationship is robust to skewness for the “before the pandemic” and “after pandemic” periods. On the contrary, when we control for the one-month return reversal, the effect of idiosyncratic volatility on the subsequent stock returns becomes insignificant in both periods. Therefore, the short-term return reversal effect is the underlying reason for the relationship switching from negative to positive in the pandemic period. Our results are beneficial for investors and researchers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it