Size-Portfolio Idiosyncratic Volatility with Aggregate Return, Cross-Sectional Return, and GDP Growth: U.S. and International Evidence
Bibliographic record
Abstract
Firm size is an essential factor in examining the relation between returns and idiosyncratic volatilities. This paper documents that, when the idiosyncratic volatility is specified by firm size, the size-portfolio idiosyncratic volatility is statistically significant in explaining the future aggregate return. This property prevails for the equally- or value-weighted scheme, the different time periods in the U.S. market, or the international markets. This paper also examines the relation between size-portfolio idiosyncratic volatilities and future cross-sectional returns. The size-portfolio idiosyncratic volatilities are also significantly related to future cross-sectional returns for both the U.S. and international markets. Finally, this paper examines the predictive ability of the size-portfolio idiosyncratic volatility for GDP growth. It concludes that size-portfolio idiosyncratic volatility contain significant information for forecasting future GDP growth for both the U.S. and the international markets.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".