The Effect of Firm’s Specific variables on firms' financial Performance: A Global Sectorial Analysis
Bibliographic record
Abstract
This study investigated the impact of Corporate Diversification, investment, Capital structure, and dividend policies on a firm’s financial performance. The dependent variables taken for measuring the financial performance of the firms included ROE, ROA, and Tobin’s q. The independent variables were taken as investment, dividend as well as capital structure policies. Moreover, corporate diversification variables are represented by product diversification and geographic diversification. Other variables like the size of assets and the age of firms were taken as control. The hypothesis stated that divided policy, investment policy, and corporate diversification have a positive impact on a firm’s financial performances and capital structure has a negative impact on a firm’s financial performance. The data is collected from 10 multinational firms of different sectors. These firms are Bosch Pvt Ltd, Toyota Motors Ltd, Sanofi Aventis Pharmaceuticals Ltd, Pfizer Pharmaceuticals Ltd, Coca-Cola beverages Ltd, Pepsi Ltd, McDonald\'s Ltd, Nestle Ltd, Reckitt Benckiser Ltd, and Unilever Ltd. The firms’ data are collected from 25 countries. The countries include Argentina, Australia, Austria, Brazil, Canada, China, Ecuador, France, Germany, India, Indonesia, Italy, Japan, Malaysia, Mexico, New Zealand, Peru, Romania, Spain, Switzerland, Thailand, Turkey, UAE, UK, and the USA. The data is examined annually from 2015 to 2019 in panel form. The regression analysis, descriptive statistics, correlation matrix, and ANOVA methods are used for the estimation, interdependency, and correlation between the variables. The results are based on sectorial analysis as the firms belong to the consumer, pharmaceutical, automobile, food, and FMCG sectors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".