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Record W2002405078 · doi:10.1108/cfri-06-2013-0067

The correlation between corporate governance and market value: regime or signal?

2015· article· en· W2002405078 on OpenAlexaff
Chang Li, Wei Zheng, Philip Chang, Shanmin Li

Bibliographic record

VenueChina Finance Review International · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCorporate governanceBusinessValue (mathematics)AccountingCorrelationPositive correlationIndustrial organizationEconometricsEconomicsFinanceStatisticsMathematics

Abstract

fetched live from OpenAlex

Purpose – As literatures argue that managers’ personalities will affect both corporate governance structures and corporate performance, the correlation between them is a mixed result. The purpose of this paper is to separate different routes leading to the mixed correlation, and name the separated routes as regime effect and signal effect. Design/methodology/approach – By theoretical analysis, the authors list three routes leading to the correlation between corporate governance and corporate performance. Routes 1 and 2 show that governance can directly and indirectly change the performance; while route 3 shows that both the governance and performance are results of managers’ personalities, and the governance has no influence onto the performance, which means the correlation led by route 3 is fake. By design a new econometric methodology, this paper separates the mixed correlation between corporate governance and performance, and names the correlation led by routes 1 and 2 as the regime effect and the correlation led by route 3 as signal effect. Findings – By an empirical research on Chinese listed corporates, the authors find that the correlations between Chinese listed corporates’ market value and main corporate governance factors can be separated into regime effects and signal effects; and the authors also find that some factors (Share of Institutional Investors, Share of Real Controller and the Squared, Dummy of Identical CEO and Chairman, Ownership Concentration) only show regime effects, some factors (Separating Extent of Ownership and Controlling Right, Dummy of Provincial State-Owned Firms) only show signal effects, and some factors (Dummy of Republic State-Owned Firms, Scale of Board) show both. What’s more, the authors find out an interesting result that the state-owning has no negative regime effect on China SOEs’ performance but very significantly negative signal effect; in this paper, the authors suggest that this means the key negative factors of Chinese SOEs is not state-owning ownership structure but the managers’ corruption. Practical implications – As only the factors with regime effects can directly and indirectly affect corporates’ performance and the factors with signal effects show that there’re some managers’ personalities affecting both the governance and performance, the separation method in this paper can help shareholders knowing which governance factors will be helpful to improve the performance and which others will show managers’ hard-working or corruption intention. Originality/value – Separate the regime effect and the signal effect from the correlation between corporate governance and 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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.641
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.038
GPT teacher head0.248
Teacher spread0.210 · 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 designNot applicable
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

Citations5
Published2015
Admission routes1
Has abstractyes

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