Risk Premium and Fear of Investors in Crisis’ Periods: An Empirical Approach Based on Fama–French and Carhart Factor Models
Bibliographic record
Abstract
This study aims to answer the question about the interactions between “investors’ fear”, two factors proposed by Fama & French, the Carhart momentum factor, andthe risk premium, and how these interactions were affected by two financial crises, the Dot-Com and Sub-Prime crises. This paper is the first empirical study that considers the effects of these financial crises. It is of critical importance as it changes the specificity of the empirical models for different periods, significantly affecting the results compared to previous research work. The main findings include a general negative change in fear over all of the sub-periods. Secondly, no consistent positive trend was observed in any of the risk premiums over time. After each crisis, the relationships between the endogenous variables had significant changes. More specifically, investors’ fear, on the first day of the week, appears to be systematically higher across all sub-periods except during the Sub-Prime crisis. Finally, after the Sub-Prime financial crisis, there is an almost complete loss of the explanatory power of the VAR models. Although fear does not seem to affect risk premiums or momentum, it was nevertheless found that the results are sensitive to the specification of the models.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".