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Record W2932258705 · doi:10.5430/ijfr.v10n2p68

The Determinants of Profitability of the Pharmaceutical Industry of Bangladesh: A Random Effect Analysis

2019· article· en· W2932258705 on OpenAlexvenueno aff
Md. Shahidul Islam, Muhammad Saifuddin Khan

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexRegression analysisPanel dataEconometricsBusinessInflation (cosmology)Return on assetsRandom effects modelVariablesEarnings before interest and taxesPharmaceutical industryEquity (law)EconomicsReturn on equityMonetary economicsFinanceStatisticsMathematics

Abstract

fetched live from OpenAlex

The present study investigates the factors affecting the profitability of the pharmaceutical industry of Bangladesh. Using the data of 20 listed pharmaceutical companies fully functional in Bangladesh, we found both the firm specific and the macroeconomic specific variables affect the profitability of the companies. A panel dataset of 10 years starting from 2007 to 2016, we ran the random effect regression. The regression output depicts that among the firm specific variables, sales, operating income, operating cost, return on equity and total liabilities have significant effect on the profitability of the companies. We also found that the GDP growth rate and the rate of inflation among the macroeconomic variables have significant deterministic role on the profitability. The regression results followed by recommendations will be a great help to the policy makers both from inside and outside of the corporations.

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)0.006
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 score0.010
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.053
GPT teacher head0.380
Teacher spread0.327 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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