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Record W2625348056

Adoption of International Financial Reporting Standard, Capital Structure and Profitability of Listed Firms in Nigeria

2017· article· en· W2625348056 on OpenAlexvenueno aff
Otekunrin Adegbola Olubukola

Bibliographic record

VenueThe Journal of Internet Banking and Commerce · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexFinancial statementAccountingBusinessCapital structureInternational Financial Reporting StandardsExtant taxonCapital (architecture)FinanceCapital marketAudit
DOInot available

Abstract

fetched live from OpenAlex

The extant literature on the relationship between capital structure and profitability of listed firms when their financial statement is prepared as per Nigerian Accounting Standard revealed that a relationship exists between capital structure and profitability of listed firms. Listed firms in Nigeria were mandated to adopt International Financial Reporting Standard (hereafter referred to as IFRS) since the year 2012. Since the year 2012, it is not fully known if the adoption of IFRS affects the relationship between capital structure and profitability of listed firms or it is still the same before the adoption of IFRS. This is a gap which this research study intended to address. This study used multiple regression analysis to find out whether the same relationship exists between capital structure and profitability of listed firms in Nigeria or not when listed firms prepare their financial statement as per IFRS from the adoption of IFRS in 2012 to 2015. This research limits its analysis to the use of data taken from the selected firms’ financial statement for the period under study. This finding shows that the relationship between capital structure and profitability of the firm is the same both before and after the adoption of IFRS. It means adoption of IFRS does not influence the relationship between capital structure and profitability of the firm significantly

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)0.003
metaresearch head score (Gemma)0.004
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 score0.027
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.037
GPT teacher head0.315
Teacher spread0.278 · 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
Published2017
Admission routes1
Has abstractyes

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