The Quest for the Superior Financial Performance Measures
Bibliographic record
Abstract
This study tries to answer whether value based measures are superior to traditional accounting measures in explaining stock returns. A pooling panel data method on 115 Iranian listed companies in the Tehran Stock Exchange (TSE) from 2001 to 2008 is used to investigate the explanatory power of four value-based measures (including Economic Value Added, Refined Economic Value Added, Market Value Added and Shareholder Value Added) compare to five accounting-based measures (including Earning Per Share, Return On Equity, Return On Assets, Cash Flow from Operations and Return On Sales) in explaining stock returns. Our findings do not provide evidence which support assertions that value-based measures are superior compared with other traditional accounting measures. Relative information content tests revealed that stock returns are more closely associated with ROA and ROE than other performance measures. Furthermore, incremental information content tests suggest that value-based measures add only marginally information content beyond accounting measures. However, the results indicate that accounting measures generally dominate value-based measures.
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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 | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".