Market risk premium used in 2010 by analysts and companies: A survey with 2.400 answers
Bibliographic record
Abstract
The average MRP used by analysts in the United States and Canada (5.1%) was similar to the one used by their colleagues in Europe (5.0%), and United Kingdom (5.2%). But the average MRP used by companies in the United States and Canada (5.3%) was smaller than the one used by companies in Europe (5.7%), and United Kingdom (5.6%). The dispersion of the MRP used was high, but lower than that of the MRP used by professors: the average range of MRP used by analysts (companies) for the same country was 5.7% (4.1%) and the average standard deviation was 1.7% (1.2%). These statistics were 7.4% and 2.4% for the professors. Most previous surveys have been interested in the Expected MRP, but this survey asks about the Required MRP. The paper also contains the references that analysts and companies use to justify their MRP, and comments from 89 respondents that illustrate the various interpretations of what is the required MRP.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".