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Record W2170804261 · doi:10.5267/j.msl.2013.03.017

A study on relationship between institutional investors and earnings management: Evidence from the Tehran Stock Exchange

2013· article· en· W2170804261 on OpenAlexvenueno aff
Milad Emamgholipour, Seyedeh Maryam Babanejad Bagheri, Elham Mansourinia, Ali Mohammadpour Arabi

Bibliographic record

VenueManagement Science Letters · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsEarnings managementAccrualStock exchangeBusinessLeverage (statistics)Corporate governanceShareholderAccountingInstitutional investorSample (material)EarningsMonetary economicsFinanceEconomics

Abstract

fetched live from OpenAlex

Institutional investors play important role on formation of different changes on corporate governance systems. They can significantly influence on companies by monitoring the performance of management and limiting their opportunistic behaviors and manipulating their financial statements. Therefore, the main objective of the present study is to investigate the relationship between institutional investors and earnings management on some listed companies on Tehran Stock Exchange by examining a sample of 700 firm-years data over the period 2006-2010. In this study, the discretionary accruals are used as an indicator for earnings management. The results indicate that there is a positive and significant relationship between institutional investors and earnings management and suggest that increasing the ownership percentage of institutional shareholders increases earnings management. In addition, the results of the control variables have shown that firm size had no impact on earnings management, but financial leverage and return on sales, respectively had negative and positive effect on the earnings management of companies.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.048
GPT teacher head0.249
Teacher spread0.201 · 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.

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

Citations24
Published2013
Admission routes1
Has abstractyes

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