MétaCan
Menu
Back to cohort
Record W3175183890 · doi:10.5539/ibr.v14n7p103

The Impact of the Financial Performance on Capital Structure of Insurance Industry in Egypt

2021· article· en· W3175183890 on OpenAlexvenueno aff
Salah Mohamed Eladly

Bibliographic record

VenueInternational Business Research · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsCapital structureReturn on capital employedReturn on capitalMarket liquidityReturn on equityDebt-to-capital ratioEconomicsBusinessMonetary economicsEconometricsFinancial economicsEquity ratioFinanceProfitability indexDebtFinancial capitalCapital formation

Abstract

fetched live from OpenAlex

This paper attempts to investigate the impact of the profitability and liquidity on capital structure of insurance industry in Egypt as applied on a sample of (19) insurance firms represented in the Egyptian insurance industry over the period from 1999-2019. The capital structure is measured by debt ratio, and the financial performance is measured by (liquidity, return on equity, and retune on investment).The study results show that there are significant negative linear relationships between the independent variable in terms of return on equity (X1), return on investment (X3), and dependent variable for the capital structure (Y) at the level of significant less than (0.001); based on panel data analysis, the results show that Tau-statistic, and z-statistic, are at a significant level less than (0.05).The statistical conclusion is the null significant relationship between the capital structure and liquidity, while there is a significant relationship between the capital structure, return on equity, and return on investment. The results  also show that the R2 for the independent variables are accepted in the model (capital structure Y, lag Y1, return on equity X1, liquidity X2, and return on investment) by (79.3%) from total variation of capital structure (Y).

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)0.001
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 score0.049
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.041
GPT teacher head0.311
Teacher spread0.270 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Business ResearchSame topicInsurance and Financial Risk ManagementFrench-language works237,207