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Record W2089311098 · doi:10.5539/ijef.v4n11p1

The Attenuation of Idiosyncratic Risk under Alternative Portfolio Weighting Strategies: Recent Evidence from the UK Equity Market

2012· article· en· W2089311098 on OpenAlexvenueno aff
Chia Rui Ming Daryl, Lim Kai Jie Shawn

Bibliographic record

VenueInternational Journal of Economics and Finance · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsDiversification (marketing strategy)PortfolioWeightingSystematic riskMarket capitalizationEconometricsEconomicsEquity (law)Financial economicsMarket portfolioBusinessActuarial scienceStock market

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.275
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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