Fundamental Analysis: Combining the Search for Quality with the Search for Value
Bibliographic record
Abstract
ABSTRACT Using cross‐sectional forecasts, we combine fundamental analysis strategies based on quality, such as the FSCORE from Piotroski (2000) and the GSCORE from Mohanram (2005), with strategies based on value, such as the V/P ratio from Frankel and Lee (1998) and the PEG ratio. While all four strategies generate significant hedge returns, combining quality‐driven and value‐driven approaches substantially improves the efficacy of fundamental analysis. Our parsimonious two‐dimensional approach can be applied to a wide cross section of stocks and outperforms common practitioner approaches that require a lengthy time series of data. The improvements in hedge returns hold for a variety of partitions and are robust to controls for risk factors and other determinants of stock returns. While the efficacy of fundamental analysis has declined in recent years, this can partially be attributed to investors arbitraging away excess returns by investing in fundamental strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".