Comparative Analysis on Fama-French Five-factor Model and Three-factor Model adopted in various Industries in A-share Market of China
Bibliographic record
Abstract
Asset pricing has always been a hot issue in the financial industry. The cutting-edge research achievements of Capital Asset Pricing Models are the Fama-French three-factor model and the Fama-French five-factor model. Although many scholars have studied the performance of Fama-French three-factor model and five-factor model in China's A-share market, there is still controversy about the explanatory power of these two model in the A-share market. This thesis discusses the applicability of Fama-French three-factor and five-factor models adopted in various industries. This thesis chooses A shares in terms of performances, with 14 years commencing from August 2007 to August 2021 as the samples, and utilizes the data of monthly transactions of listed companies in the market for calculation. The thesis has divided the samples into 18 industries. The Fama-French three-factor and five-factor models are used for regression to verify the model's applicability in China's stock market. Through the empirical test, this thesis found that the Fama-French three-factor and five-factor models have strong explanatory powers regarding the excess returns of 15 industries. The research obtained in the thesis has enriched and broadened the study of the Asset Pricing Theory in China, providing theoretical guidance for various investment entities in acts conducted in 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".