Bibliographic record
Abstract
We hear a lot about value investing, an investing approach introduced by Benjamin Graham in the 1930s and championed by Warren Buffett, but we know very little about why it works so consistently. Academia has considered the consistent performance of value investors as a statistical anomaly, but given that it has persisted for more than eighty years, it warrants further investigation. In this paper, we tried to explain why value investing works. This paper presents a basic exposition of the core tenets of value investing. We make a clear distinction between value investing according to the Graham-Buffett paradigm and what is typically referred to in the academic literature as value investing. We then provide some reasons behind the recent underperformance of value portfolios and why value investing is likely to bounce back. Finally, we provide some behavioral explanations as to why investors find it so difficult to practice value investing consistently for long periods of time.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".