Bibliographic record
Abstract
Purpose The value‐premium is the empirical observation that “value” stocks (low market/book) have higher returns than “growth” stocks (high market/book). The purpose of this paper is to propose a new explanation for the value‐premium that the authors call the limits to growth hypothesis. Design/methodology/approach To guide the testing, a dynamic equity valuation model was used that has the property that profitability increases risk for value firms in anticipation of future growth‐leverage, whereas, profitability “covers” the capital expenditure costs of growth, which decreases risk for growth firms. Because the authors interpret dividends as a corporate response to growth‐limits, they test for this predicted differential relation between profitability and risk for value versus growth stocks with the returns of profitable dividend‐paying firms. Findings It is found that profitability increases returns to a greater extent for dividend‐paying value firms compared to dividend‐paying growth firms, which is consistent with a differential relation between profitability and risk. At the same time, it is also found that growth firms have lower returns than value firms. Originality/value The authors use the limits‐to‐growth hypothesis to explain why profitability can either increase or decrease risk. High‐profitability dividend‐paying growth firms have lower returns than low‐profitability dividend‐paying value firms. This value‐premium is consistent with the argument that high profitability “covers” the capital expenditure costs of growth, which decreases risk and, thus, returns. At the same time, profitability increases returns to a greater extent for value stocks compared to growth stocks, which is consistent with the hypothesis that profitability increases risk for value firms in anticipation of future growth‐leverage.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".