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Record W3005558111 · doi:10.5539/ijef.v12n3p1

The Effect of Board Structure on Egyptian Mutual Fund Performance: A Structural Equation Model Analysis

2020· article· en· W3005558111 on OpenAlexvenueno aff
Nancy Youssef, Peng Zhou

Bibliographic record

VenueInternational Journal of Economics and Finance · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersCardiff Metropolitan UniversityCardiff UniversityAmerican Association for the Surgery of Trauma
KeywordsEndogeneityStructural equation modelingCorporate governanceMutual fundEquity (law)BusinessAccountingTarget date fundMisconductEconomicsOpen-end fundEconometricsFinancePolitical scienceInstitutional investorLawMathematicsStatistics

Abstract

fetched live from OpenAlex

Purpose – This paper empirically explores the causality between board structure and the fund performance in the mutual fund industry of an emerging market. Design – Using a panel of 82 Egyptian funds spanning 10 years before and after the global financial crisis, we develop a Structural Equation Model to deal with the endogeneity between measures of governance and performance in a systematic and identified way. Findings – Experimental results show a significant negative relationship between the equity ownership by the directors and the fund performance. Evidence shows little support for a significant effect of board structure on the performance after controlling for the endogeneity. It implies the misconduct of governance rules in Egypt, especially the weakness in board composition. Originality– Given the important role of mutual fund industry in Egypt, this is the first study of its kind explores the causality between board structure and the fund performance in the mutual fund.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.015
GPT teacher head0.220
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

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