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Record W2996570483 · doi:10.5296/ijafr.v9i4.15319

Fourth Quarter Earnings Volatility: Case of Firms Listed in DFM

2019· article· en· W2996570483 on OpenAlexaboutno aff
Nadia Sbei Trabelsi

Bibliographic record

VenueInternational Journal of Accounting and Financial Reporting · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)BusinessEarningsAccountingRevenueInterimVolatility (finance)FinanceAuditOrder (exchange)

Abstract

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Mandatory disclosure of quarterly financial reports for publicly traded companies, in the majority of jurisdictions around the world, is the direct consequence of applying “timeliness” as presented in the Conceptual Framework for Financial Reporting (the conceptual framework) developed jointly in 2010 by the International Accounting Standards Board (IASB) and the Financial Accounting Standards Board (FASB). Having relevant information available sooner would improve its capacity to influence decisions. However, the interim reports are not required to be audited. In UAE, companies whose securities are listed on a securities and commodities market licensed by the Securities and Commodities Authority (SCA) are required to notify and provide interim financial reports, which are reviewed by the external auditor of the company. The objective of this paper is to analyze, in UAE, the volatility of the fourth-quarter earnings compared with the previous three. This study includes four years (2012-2015) of quarterly financial statements of firms listed in Dubai Financial Market (DFM). In order to determine if interim results are suspect, the paper analyzes the magnitude of differences in fourth quarter earnings and revenues relative to the first three quarters by using the Kiger’s 1974 methodology. Overall, results indicate that the volatility of earnings and revenue in the fourth quarter is significantly higher than those of the first three quarters. This main finding would be explained by the necessary adjustments to the fourth quarter earnings and revenues in order to correct the estimation. In fact, the quarterly financial statements require the use of more estimates than those prepared at the end of the fiscal year. This research would contribute to better understanding the quality of interim reports in an emerging market context.

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)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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