Value Premium and Equity Term Structures of Value and Growth Firms
Bibliographic record
Abstract
This paper studies the impact of information processing and learning about expected future cashflows on the level and timing of risk premiums in the cross section of firms. Learning with information sources of different qualities endogenously generates value firms, growth firms, and a value premium in equilibrium. Furthermore, the learning model predicts an upward-sloping equity term structure for value firms and a flat equity term structure for growth firms. Using earnings, return, and news data on value and growth firms, we show that the predictions of the learning model are consistent with the data. This paper was accepted by Kay Giesecke, finance. Funding: Research support from Long-Term Investors@UniTo, the University of Neuchatel, and the University of Toronto is gratefully acknowledged. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2024.05922 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".