PERHAPS EVA DOES BEAT EARNINGS—REVISITING PREVIOUS EVIDENCE
Bibliographic record
Abstract
In two articles, the first published in 1997 in the Journal of Accounting and Economics and the second in 1999 in this journal, Gary Biddle, Robert Bowen, and James Wallace presented evidence that reported earnings are more closely related than EVA to marketadjusted stock returns– in other words, that earnings are more “value relevant” than EVA. These papers, which are among the most widely cited in finance and accounting, fundamentally affected perceptions about the importance of EVA as a measure of corporate performance. The current article addresses a simple question: Do the Biddle, Bowen, and Wallace results continue to hold for a different set of companies, a different time period, and a different market? The authors first examined updated EVA information for different companies in the same time period examined in the Biddle, Bowen, and Wallace study. They then looked at a more recent time period (1995–1999) and a different market (the Canadian stock market), and found in all cases that “EVA has greater power than earnings in explaining marketadjusted stock returns.” Their findings validate the widespread corporate acceptance of EVA as a management tool.
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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 | 0.017 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".