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Record W4224995329 · doi:10.5430/afr.v11n2p35

The Impact Of Asset Management Efficiency Ratios on Earnings per Share Case Study of Industrial Companies Listed on the Amman Stock Exchange from 2005 to 2019)

2022· article· en· W4224995329 on OpenAlexvenueno aff
Ateyah Mohammad Alawneh

Bibliographic record

VenueAccounting and Finance Research · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWorking Capital and Financial Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAsset turnoverStock exchangeBusinessEarningsEconometricsStock (firearms)Asset (computer security)Profitability indexTurnoverVariablesMonetary economicsEconomicsReturn on assetsFinanceStatisticsMathematics

Abstract

fetched live from OpenAlex

This study aims to analyze the effect of the represented asset management efficiency (total asset turnover (TAT), fixed asset turnover (FAT), and working capital turnover (WCT)) on the earnings per share (EPS) of industrial companies listed on the Amman Stock Exchange (IASE) as the data were obtained from the Amman Stock Exchange (ASE) from 2005 to 2019, where the unit root test was analyzed for the time series of the study variables. Results revealed that all the variables stabilize at the first differences 1 (1), several diagnostic tests, such as variance instability, Ramsay stability, and serial correlation tests were also performed, all of which confirmed the fit and validity of the model used. Results showed the positive and strong impact of the asset turnover rate on EPS, the positive and strong impact of the fixed asset turnover rate on the return on profitability, and the positive impact of the (WCT) on EPS. Therefore, asset management efficiency positively affects the EPS. Moreover, this result indicates the efficiency of industrial companies in managing assets during the study period.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.084
GPT teacher head0.326
Teacher spread0.242 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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