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Record W2593367550 · doi:10.3138/cpp.2016-071

Qualified Foreign Institutional Investor Shareholdings and Corporate Operating Performance

2017· article· en· W2593367550 on OpenAlexvenueno aff
Shuili Yang, Xiaoyan Ren

Bibliographic record

VenueCanadian Public Policy · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceBusinessInstitutional investorEquity (law)Profitability indexAccountingChinaCorporate debtPanel dataMonetary economicsDebtFinancial systemFinanceEconomicsEconometrics

Abstract

fetched live from OpenAlex

In this article, profitability, asset quality, debt risk, and sales growth are used as measurement indices of corporate operating performance. On the basis of panel data of 630 companies for 2008–2014, which is after the completion of China's split-share structure reform, an entity fixed-effects model is used to study the effect of qualified foreign institutional investor (QFII) shareholdings on the operating performance of Chinese non-financial transnational corporations. The results show that the QFII shareholdings ratio and shareholdings checks and balances degree are significantly positively correlated with return on equity and total assets turnover, but they have no significant correlation with asset liability ratio and sales growth rate. The evidence therefore suggests that the higher the QFII shareholdings ratio is, the higher the enthusiasm is for QFII shares participating in corporate governance, and the better the general corporate operating performance. The findings provide an important reference for Chinese transnational corporations to introduce QFII shares to improve governance structure and corporate operating performance.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.083
GPT teacher head0.235
Teacher spread0.153 · 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 designObservational
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

Citations6
Published2017
Admission routes1
Has abstractyes

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