Bibliographic record
Abstract
Based on accumulated empirical evidence, the academic community has generally come to agree that value investment strategies, on average, outperform growth investment strategies (Chan and Lakonishok, 2004:71). An influential article by Fama and French (1992) tested the notion that United States stock prices might be related to the ratio of a firms book value of common equity (BV) to its market value of common equity (MV). It found that companies with high book value relative to market value of equity (BV/MV) outperform the market. This finding led to extensive testing for the value premium in developed countries around the world. Fama and French (1998a) tested it with data from twelve major European countries, as well as from Australia and the Far East. They found that between 1975 and 1995 in almost every country, value stocks delivered a higher return than growth stocks. The value premium has not been tested with the same vigor in third world or developing countries, which raises the question whether the value premium is only a first world phenomena and, if not, how third world value premiums compare to those found in developed countries. This paper compares the size of the value premium in the USA, UK, and some continental European countries with South African data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".