The performances of Financial Models During COVID-19: Evidence from CAPM and Fama-French Five Factors Model
Bibliographic record
Abstract
The ravages of the COVID-19 pandemic impact the operation of society, from home isolation to the shutdown of factories, bringing great uncertainty to the financial market.This article uses the information retrieval method to review the performance of the CAPM model and the Fama-French five factors model under the COVID-19 pandemic.As for the issue that CAPM has a weak ability to interpret abnormal data, many researchers have improved the CAPM model according to the real situation of different industries, but the improved model still has shortcomings.Moreover, there are differences in the performance of different industries in the Fama-French model before and after the pandemic.Specifically, interpretation strength has increased, and Mkt, SMB, HMI have changed significantly, but there are still unexplainable risk factors.Thus, future research should focus on how to further improve the interpretation of the model in response to emergencies to better explain and obtain excess returns.These results shed light on the better application of financial models in different situations.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".