Impact of Weighted Average Cost of Capital and Profitability on Economic Value Added of Firms in the Industrial Sector
Bibliographic record
Abstract
In a context where the measurement of economic value is key for financial decision-making, Economic Value Added (EVA) stands out as a relevant indicator for assessing companies’ financial performance efficiency. This research aimed to determine the impact of the Weighted Average Cost of Capital (WACC) and profitability on the EVA of industrial sector companies in Peru. A quantitative approach was used, with a correlational-causal and non-experimental design. The sample included four industrial sector companies listed on the Lima Stock Exchange (BVL). The authors applied the document review technique, and the correlational analysis was carried out using linear regression. Results show that Return on Equity (ROE) is a statistically significant predictor of EVA across all companies analyzed, indicating a direct relationship. In contrast, WACC showed a weak relationship with the variables studied. It is concluded that profitability has a greater influence on EVA than WACC. However, the relationship between WACC, ROE, and EVA differs among companies. The model explains a moderate variability in EVA, suggesting that other factors not considered in the model also affect the generation of economic value.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".