MétaCan
Menu
Back to cohort
Record W2462446916 · doi:10.3968/7995

Large Shareholder’s Identity With Linguistic World and Stock Price Synchronicity: Evidence From a MENA Market and the Way Languages Affect Them

2016· article· en· W2462446916 on OpenAlexvenueno aff
Tamadur Sulayman Al-Shamileh

Bibliographic record

VenueStudies in literature and language · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsSynchronicityShareholderMonetary economicsStock (firearms)Stock marketBusinessEmpirical evidenceEconomicsAffect (linguistics)Corporate governanceFinancial economicsAccountingFinanceLinguistics

Abstract

fetched live from OpenAlex

I investigate the association between large shareholder’s identity and stock price synchronicity in a country where investor applying for languages is really lows protection is weak. My results show that stock prices in Jordan have synchronous behavior especially when the firm is large, consistent with previous empirical evidence on stock price behavior in low per capita GDP countries. Most of the public corporations are owned and controlled by families thus language exchanges such as speaking, reading and listening. In most of the family-controlled firms, the controlling family is also involved in firm’s management leading to loose separation between ownership and management. Furthermore, stock prices of family-controlled firms are significantly less synchronous while those of government controlled firms are more synchronous than stock prices of widely held corporations. The pyramid structure is the most widely used indirect control mechanism in languages world and results in little deviations between ownership and control.

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)0.007
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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.019
GPT teacher head0.267
Teacher spread0.248 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueStudies in literature and languageSame topicCorporate Finance and GovernanceFrench-language works237,207