Research on Mathematical Methods of Improving Fama and French Three-factor Model Based on ETF Factors
Bibliographic record
Abstract
Abstract Will ETF factors help to improve Fama and French 3 factor Model? Research has examined the Fama French model in different countries and its risk premium in different mathematical ways. This paper reconstructs the Fama-French model by replacing market capitalization size index (SMB) and book-to-market ratio index (HML) to other Exchange Traded Fund (ETF) factors to investigate whether ETF factors will improve the Fama French model. After comparing 15 ETFs, Factors EMD (GDP growth index) and SMG (factor IYC minus factor IYK) are chosen for our adapted risk model because of a relatively low rate of correlation and covariance. 20 sample stocks are selected form various industries and market capitalization size, and used to test to the performance of FFM and the new FFM. The result has been analyzed from 3 different ways: correlation and covariance, R squared, and p value. Although the new model fails to perform better than a traditional FFM, it may be improved by adding more factors or choosing factors which are more representative of the market.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".