The Performance, Pervasiveness, and Determinants of Value Premium in Different US Exchanges: 1985-2006
Bibliographic record
Abstract
Using AMEX, NASDAQ and NYSE stock market data for the period 1985–2006, this paper sheds further light into the value premium and the discussion of whether the value premium is driven by risk or behavioral factors. The paper utilizes a more comprehensive set of data and tests than previous studies and a research methodology that minimizes potential data snooping problems and confounding inferences. We document a consistently strong value premium in all markets examined, which persists in both bull and bear markets, as well as in recessions and recoveries. We show that the value premium is not driven by a few outliers, but it is pervasive as the overwhelming majority of stocks in the value portfolio have positive returns, and the majority of the industries in our sample have positive value premiums. The value premium, in general, remains positive and statistically significant over time. Our results are consistent with, but, in general, stronger than, those of other US studies. Previous studies’ results seem to be driven primarily by AMEX and NYSE stocks, as NASDAQ stocks experience much stronger value premium than other markets. In terms of explaining the drivers of the value premium, having looked at this question from many angles, we conclude that the evidence is mixed. It seems that both risk and mispricing may play a role in explaining the value premium, although the scale of the evidence seems to tilt more to the side of mispricing. The paper’s conclusions both with regards to the value premium and its drivers hold up well to various robustness tests.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".