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Record W2588538250 · doi:10.5539/ijef.v9n3p133

Empirical Investigation of Symmetric and Asymmetric Target Adjustment Models: Capital Structure of Non-Financial Firms in Jordan

2017· article· en· W2588538250 on OpenAlexvenueno aff
Bashar Khaled Abu Khalaf

Bibliographic record

VenueInternational Journal of Economics and Finance · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersUniversity of Jordan
KeywordsCapital structureProfitability indexLeverage (statistics)Stock exchangeDebt-to-capital ratioBusinessMonetary economicsProfit (economics)Financial structureFinanceEconometricsEconomicsIndustrial organizationMicroeconomicsEquity ratioReturn on equity

Abstract

fetched live from OpenAlex

The different capital structure theories propose the possible asymmetric behavior of capital structure. Thus, this paper empirically investigates whether non-financial Jordanian firms follow symmetrical or asymmetrical adjustment model. Then, an interaction model with the size and profitability (firm characteristics) investigated the impact of low/high profit and small/large size on the adjustment of leverage towards the target leverage ratio. This paper covered the period of 14 years (2002-2015) for a total of 110 companies listed on Amman Stock Exchange (75 industrial and 35 services). Results indicate that although Jordanian firms seek a target leverage ratio, their adjustment towards that target is Asymmetrical and high profitable and large companies tend to adjust faster than low profitable and small size 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 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)0.005
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.017
GPT teacher head0.234
Teacher spread0.218 · 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
Published2017
Admission routes1
Has abstractyes

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