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Record W2161956314 · doi:10.5430/afr.v2n3p45

A Longitudinal and Cross-Industry Study on the Stability of Financial Ratios of Malaysian Companies

2013· article· en· W2161956314 on OpenAlexvenueno aff
Ben Chin-Fook Yap, Zulkifflee Mohamad, K-Rine Chong

Bibliographic record

VenueAccounting and Finance Research · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Distress and Bankruptcy Prediction
Canadian institutionsnot available
Fundersnot available
KeywordsCash flowFinancial ratioBusinessCurrent ratioSample (material)FinanceCurrent assetFinancial crisisEconomicsEconometricsMonetary economicsWorking capitalMarket liquidity

Abstract

fetched live from OpenAlex

The purpose of this study is to test whether a set of six financial ratios that have been used extensively by practitioners and researchers and found to be useful for various purposes including company financial performance evaluations are stable across three different industry sectors and whether they are stable over time. The sample comprises a total of 180 listed companies covering a period of five years from 2006 to 2010. Analysis of variance and post hoc multiple comparisons were carried out for each ratio to see whether it exhibits a stable profile across industries and over time. The findings showed that four out of the six ratios displayed no significant differences across industries, one (Current Assets Turnover) showed significant differences among all three sectors while the remaining ratio (Cash Flow to Total Assets) showed significant differences between two of the sectors. The test results also showed that all the financial ratios except for Cash Flow to Total Assets for all three industry sectors are stable over time. This finding is surprising in that the years 2008 and 2009 are periods where the financial crisis is at its height and companies’ financial data are expected to be adversely affected and where the means of the ratio values in these two years are expected to be volatile and unstable compared to the period before and after the financial crisis. The analysis also showed that there are no interaction effects between sector and time. Thus, some ratios are industry specific and some ratios cannot be extrapolated over time when evaluating financial performance or forecasting future trends.

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)0.001
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 score0.014
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.074
GPT teacher head0.322
Teacher spread0.248 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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