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Record W2157351803

MARKET RISK PREMIUM USED IN 2008: A SURVEY OF MORE THAN A 1,000 PROFESSORS

2009· preprint· en· W2157351803 on OpenAlexaboutno aff
Pablo Fernández

Bibliographic record

VenueRePEc: Research Papers in Economics · 2009
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsConfusionRisk premiumVariety (cybernetics)EconomicsActuarial scienceBusinessMathematicsEconometricsStatisticsPsychology
DOInot available

Abstract

fetched live from OpenAlex

The average Market Risk Premium (MRP) used in 2008 by professors in the United States (6.5%) was higher than the one used by their colleagues in Europe (5.3%), Canada (5.4%), the United Kingdom (5.6%) and Australia (5.9%). The dispersion of the MRP was high. 15% ofthe professors decreased their MRP in 2008 (1.5% on average) and 24% increased it (2% on average). 66% of the professors used a lower MRP in 2007 than in 2000 (22% used a higher one). The average MRP used in 2007 was 1.5% lower than the one used in 2000. Most previous surveys were interested in the Expected MRP, but this survey asks about the Required MRP. The paper also contains the references that professors use to justify their MRP and comments from 180 professors that illustrate the variety of interpretations of what the required MRP is and explain the confusion of students and practitioners about its concept and magnitude.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.068
GPT teacher head0.307
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainEvaluation
GenreEmpirical

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".

Quick stats

Citations4
Published2009
Admission routes1
Has abstractyes

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