Market Volatility Risk and Stock Returns around the World: Implication for Multinational Corporations*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We investigate the pricing of market volatility risk as a risk factor—the innovation risk and as a characteristic risk—the level risk. We find that the pricing of the country‐level (local) market volatility risk factor is not robust across 21 developed markets and that the global market volatility risk factor prices 21 developed market portfolios after controlling for global market, value, and size factors. Capturing various market information, idiosyncratic market volatility as a country‐specific characteristic risk dominates global market, value, size, and market volatility risk factors in predicting returns of market portfolios. Countries with higher investor protection and accounting standards have higher country‐specific market volatility. Market volatility is higher in these countries because corporate managers take higher risks on innovative projects that benefit economic growth.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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