Separating Winners from Losers Among Value and Growth Stocks in Different US Exchanges: 1969-2011
Bibliographic record
Abstract
The purpose of this paper is twofold: (a) to determine whether there is value premium in our sample of US stocks for the period May 1, 1969-April 30, 2011; and (b) to examine whether an additional screening to the first step of the value investing process can be employed to separate the outperforming value and growth stocks from the underperforming ones. In this paper, we document the following: We find a consistently strong and pervasive value premium over the sample period. We show that there are distinct differences between US exchanges which means that papers that aggregate all US exchanges under one umbrella may dilute findings and bias conclusions. The stocks of AMEX firms, high business risk firms and firms that report extraordinary items experience worse returns than the rest of the US stocks in our sample. We find that P/E based sortings produce better overall results than sortings based on P/B. We are able to construct a composite score indicator (SCORE), combining various fundamental and market metrics, which enable us not only to separate the winners from the losers among value and growth stocks, but also to predict future returns of value and growth stocks. SCORE portfolios give better results for sortings based on P/E and when we employed a cross-section-time series medians approach. Results remain robust for a time period out of sample, for negative P/E or P/B ratio firms and for the firms that were excluded from SCORE-based performance, namely, AMEX stocks, stocks with high business risk and firms that reported extraordinary items the year before. Finally, we provide evidence that the return of a portfolio strategy that buys (sells) stocks that rank low (high) in the composite score indicator has significant explanatory power in an asset pricing model framework and that such a strategy earns statistically significant positive returns.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".