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Record W2254055608

The Relationship between Managers' Compensation and Market Value Added (MVA) in Iranian Listed Firms: Panel Data Technique

2012· article· en· W2254055608 on OpenAlexvenueno aff
Meysam Doaei, Mohammad Hossein Vadiei, Hasan Bari

Bibliographic record

VenueReview of Economics and Finance · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexCompensation (psychology)Panel dataStock exchangeBusinessValue (mathematics)Market valueRegression analysisEnterprise valueVariablesStock marketAccountingEconometricsIndustrial organizationEconomicsFinanceStatisticsMathematics
DOInot available

Abstract

fetched live from OpenAlex

Nowadays, manager compensation is considered as one of the motivation factors in many communities. Other researchers have been done in scientific and professional associations about what variable should be the subject of compensation. Many firms and economic organizations determine the manager compensation based on profitability rates; however, it has been criticized due to formulating some failures on modification and computation of it. So, this research has intended to investigate the relationship between manager¡¯s compensation and market value added (MVA). In this paper, it is applied panel data technique for 46 listed firms in five years. Finally, it is concluded that there is a relationship between manager¡¯s compensation and market value added in Tehran Stock Exchange (TSE) and the 47% of fluctuations is explained by the defined regression model.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.171
GPT teacher head0.333
Teacher spread0.162 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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