Searching for the Sustainably Profitable Stocks: Evidence on S&P 500 Companies
Bibliographic record
Abstract
Using a balanced panel dataset of 214 firms from S&P 500 during 2001-2012, the main purpose of this study is to search for the possibility of the sustainably profitable stocks by utilizing a nonlinear panel smooth transition regression (PSTR) model. We document three important empirical findings on this study. First, we show that the concurrent total asset growth rate and the change in EPS positively correlate with the market adjusted stock returns, while one-year lagged market-to-book asset ratio (MBA) had negative impact on market adjusted stock returns. Second, we find that most value stocks remained in the same regime over the sample period and the annual average market-adjusted return of these value stocks is 7.80%, approximately 10.56% higher than growth stocks. Third, we further report that 116 valued firms in our sample are most likely to be sustainably profitable stocks over the entire sample period and the annual average market-adjusted return of these stocks is 6.53%. Especially now, with an investment environment that has been somewhat ungenerous in offering returns, we expect these interesting findings provide rich implications for institutional investors to design profitable and effective investment strategies.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".