MétaCan
Menu
Back to cohort
Record W1914476462

Examining the Relationship Between Performance Measures and Share Price: An Empirical Study on Mobile Telecommunications Companies Listed on Bursa Malaysia

2014· article· en· W1914476462 on OpenAlexaboutno aff
Gilbert O'Neil Mushure

Bibliographic record

VenueInternational Journal of Sciences: Basic and Applied Research · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsnot available
Fundersnot available
KeywordsShare priceEarnings per shareReturn on equityBusinessStock exchangeMarket shareQuarter (Canadian coin)EarningsPrice–earnings ratioAccountingEquity (law)Investment (military)MarketingFinance
DOInot available

Abstract

fetched live from OpenAlex

This paper is in Market Based Accounting Research (MBAR) which is a sub-discipline of Accounting & Finance under the broad field of Business Management. This research paper is a quantitative study of the relationship between financial and non-financial performance measures, and future share price performance of mobile telecommunications companies listed on Malaysia’s stock exchange known as Bursa Malaysia. Knowledge of the nature and strength of these relationships is used for forecasting future share price performance for investment decision making purposes. Four variables’ relationships to future share price performance were studied; these variables were Earnings Per Share (EPS), Price Earnings (P/E), Return on Equity (ROE), and Subscriber Growth (SG). The study found evidence that from one quarter to the next, EPS and P/E had the strongest relationship with the subsequent quarter’s share price, whilst ROE and SG were poor predictors of share price performance. Furthermore the study found that these relationships, whilst true for one company, did not necessarily exist for all companies even though the companies were all in the mobile telecommunications industry and their shares were traded on the same Bursa Malaysia market.

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)0.010
metaresearch head score (Gemma)0.002
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 score0.038
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.390
GPT teacher head0.456
Teacher spread0.066 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Sciences: Basic and Applied ResearchSame topicFinancial Reporting and Valuation ResearchFrench-language works237,207