A Comparative Analysis of Four-Factor Model and Three-Factor Model in the Nigerian Stock Market
Bibliographic record
Abstract
The study investigates if the three-factor model explains variation in expected returns of stocks on the Nigerian Stock Exchange (NSE); and also ascertains if the four-factor model explains the variation in expected returns of stocks on the NSE better than the three-factor model. The study use a sample size of 139 stocks with continuous trading on the NSE for the period January 2007 to December 2014 to construct 10 portfolios on the bases of size, value and returns. By means of multiple OLS regression analysis method with the aid of StataC13 software the analysis was done. The empirical analysis reveals that the three-factor model explains cross sectional variation in expected returns in the NSE. Also, the study shows that the size effect, value effect as well as momentum effect is present in the market. Comparing the four-factor model with three-factor model, shows that the four-factor model have better explanatory power than the three-factor model in explaining returns in the Market. It is recommended that equity investors, fund/portfolio managers and investment advisers should embed in their operational strategies the explanatory power of market beta, size and value as well as momentum on stock/portfolio returns to enable them build up trading strategies that minimize loss and maximize returns. Market regulators and policy makers should ensure appropriate measures are in place to improve market viability and liquidity in order to enhance the depth and breathe 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 | 0.004 | 0.009 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".