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Record W2056829337 · doi:10.5539/ijef.v4n11p217

Debt Policy and Corporate Performance: Empirical Evidence from Tehran Stock Exchange Companies

2012· article· en· W2056829337 on OpenAlexvenueno aff
Nima Sepehr Sadeghian, Mohammad Mehdi Latifi, Saeed Soroush, Zeinab Talebipour Aghabagher

Bibliographic record

VenueInternational Journal of Economics and Finance · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsDebt ratioDebt levels and flowsDebtInternal debtDebt-to-GDP ratioMonetary economicsExternal debtDebt service ratioEquity valueStock exchangeBusinessEconomicsWeighted average cost of capitalRecourse debtCapital structureFinanceFinancial systemProfit (economics)Financial capitalMicroeconomicsCapital formation

Abstract

fetched live from OpenAlex

The ability of companies in determining suitable financial policies to make investment opportunities is one of the most principal factors for the companies’ growth and progression. Adopting a debt policy or a capital structure is considered as a momentous decision that influences the companies’ value. This paper is aimed to investigate the probable relationship between debt policies (including Current Debt, Non-Current Debt, and Total Debt) and performance of Tehran Stock Exchange Companies. The regression model is applied to investigate the relationship between the performance indicators and debt ratios. In this research, financial performance indicators are considered as Gross Margin Profit, Return on Assets (ROA), Tobin's Q Ratio, and Debt Ratios (Current Debt, Non-Current Debt, and Total Debt). “size” and “growth rate” are considered as control variables. Results show that an increase in current debts, non-current debts, and total debts has a negative influence on the corporate performance. It was also found that companies that merely attempt to create assets through debts, without any attention to the company size and other important factors, are not able to have an excellent performance.

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)0.000
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 score0.093
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.087
GPT teacher head0.269
Teacher spread0.182 · 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

Citations50
Published2012
Admission routes1
Has abstractyes

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