Further thoughts on the relationship between economic value added and stock market performance
Bibliographic record
Abstract
Abstract As authors of a previous study questioning the strength of the relationship between EVA and shareholder value, and in light of the arguments posed by Keefe and Roush, we revisit the relationship between EVA and shareholder return and reexamine the evidence and issues surrounding the use of EVA as a tool for valuing investments. Using the Stern Stewart Fortune 1000 data, we examine two potential relationships for 33 food companies listed in the database. The first is between the absolute level of EVA in 2000 and 3‐, 5‐, and 10‐year shareholder returns. The second is between 3‐, 5‐, and 10‐year mean percentage changes EVA and 3‐, 5‐, and 10‐year shareholder returns. The correlations found were extremely weak in all instances tested. [EconLit citations: 9120, 9320, M410, Q130]. © 2003 Wiley Periodicals, Inc. Agribusiness 19: 255–267, 2003.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.002 |
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".