The relationship between economic value added and stock market performance: A theoretical analysis
Bibliographic record
Abstract
Abstract Recently in this journal, Turvey et al. (2000, p. 415) report that “… there is absolutely no relationship between EVA and stock market performance” with their sample of Canadian agribusiness firms. We examine the implied relation by invoking a set of perfect market assumptions that are not radically out of kilter with a semistrong form efficient market. We use a simple project as the focus of the analysis. We initially explore the way that the market value of the project evolves over time. We then effect a similar analysis on the project's economic profit. The latter is the theoretical construct underlying EVA. A comparison of these two time series argues that Turvey et al.'s (2000) result is not unexpected from a theoretical perspective. Notwithstanding, we offer brief suggestions as to why executives adopt EVA as a measure of financial performance. [EconLit citations: D81, G30, M41.] © 2003 Wiley Periodicals, Inc. Agribusiness 19: 245–253, 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".