Does style investing uniformly affect correlations in small and large markets?
Bibliographic record
Abstract
Empirical and theoretical research concurs to show that style investing increases return correlations within assets that are classified into the same style. The theoretical model presented in this study addresses the question of how the correlation increases due to style investing depend on market size, and how they respond to economic downturns and to the incidence and awareness of style investing. The results show that correlation distortions caused by style investing are more robust for smaller markets. Further, the effect of style investing on correlations strengthens risk aversion, and hence during downturns. Market awareness, and incidence, of style investing also increase correlation distortions. The model yields closed-form analytical expressions for the correlation distortions caused by style investing, as well as for the effects of changes in risk aversion and in the incidence and awareness of style investing. Given the surge ETF-based style investing over the last two decades, the results have implications for portfolio risk diversification. This study predicts that the ability of risk mitigation through portfolio diversification diminishes particularly for small-market domestic investors as a result of the growing relevance of country-based ETF-based.
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".