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Record W4385973836 · doi:10.5267/j.uscm.2023.7.015

Level of voluntary disclosure and market value: An applied study on companies listed in Amman Stock Exchange

2023· article· en· W4385973836 on OpenAlexvenueno aff
Mo’taz Al Zobi, Othman Hel Al-Dhaimesh, Almothanna Abu-Allan

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsVoluntary disclosureBusinessStock exchangeAccountingFinanceStock marketTurnoverDescriptive statisticsValue (mathematics)Economics

Abstract

fetched live from OpenAlex

Voluntary disclosure is viewed as an essential communication tool through which the company's ideas can be promoted to potential stakeholders, which contributes to achieving the company's growth and sustainability. This study came to reveal the reality of voluntary disclosure in the Jordanian industrial companies and its impact on its market value. The descriptive-analytical approach was adopted, where content analysis of the financial reports published by 72 industrial companies listed on the Amman Stock Exchange during the period (2016-2022). The study found that the level of disclosure of non-financial items included the aspects of the board of directors, social responsibility, and community policy were high, which reached 56%, compared to the level of disclosure of financial items, which reached 43%. This indicates that companies pay more attention to disclosing non-financial items than financial items. In addition, the study indicated that companies with a high level of voluntary disclosure have a better market value compared to those companies that have not disclosed or have a lower level of disclosure. Moreover, increasing voluntary disclosure of strategic information, financial information, and non-financial information increases the market-to-book value, because voluntary disclosure enhances the information available to investors about shares, which in turn improves the market value.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.194
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.051
GPT teacher head0.272
Teacher spread0.221 · 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 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
Published2023
Admission routes1
Has abstractyes

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