Bibliographic record
Abstract
The Fama and French five-factor model (FF5 model) has been widely used in finance to explain the returns of stocks. This model posits that stock returns can be explained by five factors: market beta, size, value, profitability and investment pattern. Despite its wide use, there has been limited empirical research on whether FF5 model is the superior in the context of the NASDAQ composite index (IXIC). This paper aims to fill this gap by conducting empirical research on the validity of the FF5 model compared with other asset pricing models. The study uses seventy-five stocks listed on the NASDAQ composite from January 2002 to December 2021 to test the validity of the FF5 model. The data was analyzed using regression analysis, with stock returns as the dependent variable and the five factors as the independent variables. The results of the study provide evidence for the validity of the FF5 model compared with other asset pricing models. It suggests that the FF5 model can explain stocks on the NASDAQ composite and it is superior compared with other asset pricing models. Result also indicated that the market beta, size, value, profitability and investment factors can have significant impact to stock returns. Thus, results suggests that the FF5 model can be used to effectively predict rate of return of stocks on the NASDAQ composite.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".