Average Stock Variance and Market Returns: <i>Evidence of Time-Varying Predictability at the</i><i>Daily Frequency</i>
Bibliographic record
Abstract
Chen, Ortiz-Molina, and Zhang develop a daily measure of average stock variance and study whether it can predict market returns one day ahead. Using a time-invariant prediction model, they find a robust predictive relation between these variables that cannot be used to profitably time the market. A closer look reveals that the strength and even the direction of the predictive relation vary significantly over short periods of time. Moreover, a simple timing strategy that exploits this variation over time significantly outperforms the market buy-and-hold strategy in terms of the mean-variance trade-off. The evidence shows that predictability is stronger during business cycle contractions and that the timing strategy is profitable because it avoids losses during bad times. The evidence also shows that parameter breaks occur very frequently over short periods of time, and not only when the economy switches from one phase of the business cycle to another. The authors’ results suggest that idiosyncratic risk matters in asset pricing and that its effect is time varying. TOPICS:Financial crises and financial market history, factor-based models, exchanges/markets/clearinghouses
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".