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Record W2147574179 · doi:10.5539/ijef.v4n6p141

The Impact of Institutional Investors on Firms Accounting Flexibility: Evidence from Jordan

2012· article· en· W2147574179 on OpenAlexvenueno aff
Imad Zeyad Ramadan

Bibliographic record

VenueInternational Journal of Economics and Finance · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAccrualStock exchangeProxy (statistics)Institutional investorLeverage (statistics)BusinessAccountingRegression analysisStock (firearms)Earnings managementEarningsEconometricsRetained earningsMonetary economicsEconomicsFinanceDividendCorporate governanceStatistics

Abstract

fetched live from OpenAlex

In this paper the existence of the impact of the institutional investor on the firm’s accounting flexibility in generating discretionary accruals was verified. For this purpose balanced data cross-sectional regression model for all 70th Jordanian manufacturing companies listed at Amman Stock Exchange (ASE) over eleven years from 2000 to 2010 was utilized. In the regression model discretionary working capital accruals (DWCA), proxy for earnings manipulation, was set as the dependant variable. Independent variables were; the percentage of the institutional investors ownership of common stock in firm as a proxy of the institutional investors (IIP), the managerial ownership (MAO), firm’s size (SIZE), leverage ratio (LEV), and return on sales ratio (ROS). The econometric model was estimated. The results of various analysis and tests carried out in this study confirm the monitoring role of the institutional investors and the role played by the institutional investor in alleviating the practices of earnings management.

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 score0.006
Threshold uncertainty score0.011

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.028
GPT teacher head0.258
Teacher spread0.230 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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Same venueInternational Journal of Economics and FinanceSame topicAuditing, Earnings Management, GovernanceFrench-language works237,207