The impact of economic value added (EVA) adoption on stock performance
Bibliographic record
Abstract
The adoption of EVA as a compensation and management plan, generally, impacts positively the performance of companies adopting this method.However, this paper examines whether the adoption of the EVA framework enhances the firm's performance and gauge the long-term effects of such an adoption on the firm's value.It also assesses whether the market reacts to the announcement of the adoption of EVA as a compensation system.Moreover, the paper fills this gap in research literature by showing whether or not EVA adoption leads to a significant increase in firm value as reflected by its market prices on the long run.Growing evidence in research indicates that the stock market does not incorporate all firm information into the stock price quickly and completely.Therefore, the critique that contemporaneous association between price and EVA does not reflect reality is likely to be correct.However, this paper takes a different action.The basic contention is that although prices adjust slowly to information, long horizons are sufficiently long for markets to incorporate almost all relevant information into prices.The study sample consists of 89 US firms adopted EVA as a compensation system.It compares the performance of adopting firms to that of selected matching firms and to the market indexes, particularly, the S&P500 portfolio.Then it uses two common aggregating methods to test the event of adopting EVA by different US firms namely the CAR and BHAR methods.The results obtained, however, showed a slight improvement in the performance of companies adopting EVA within five years from the date of adoption.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".