The Attenuation of Idiosyncratic Risk under Alternative Portfolio Weighting Strategies: Recent Evidence from the UK Equity Market
Bibliographic record
Abstract
In this study, we investigate the attenuation of idiosyncratic risk and corresponding benefits of diversification for equally weighted and market capitalisation weighted portfolios in the UK Equity Market over 2002 - 2012. We analyse the absolute benefits of risk reduction by testing the homogeneity of variances of portfolios of different sizes using Levene's Test. Next, we perform a cost-benefit analysis to determine the return benefit of diversification from a practical perspective. We find that the absolute benefits of diversification for an equally weighted portfolio are greater in the 'crisis' than 'pre-crisis' period, but when we analyse the results from a practical perspective the benefits fall dramatically and the results are reversed. When comparing the benefits of market capitalisation weighted and equally weighted portfolios, we note that the benefits of diversification tend to be greater for an equally weighted portfolio for small portfolios but that a crossover occurs as the size of the portfolio increases. The relative benefits of diversification under these different weighting strategies are thus highly dependent upon the state of the market and further study is needed to determine why the diversification benefits for the alternative weighting strategies decay at varying rates.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".