Comparison of Multifactor Asset Pricing Models in the South African Stock Market [2000–2016]
Bibliographic record
Abstract
The quest for parsimonious models has been a key objective in asset pricing. However, there appears to be no consensus on the most successful asset pricing strategy in the literature, especially for the South African Market. Using financial statements from January 2000 to December 2015, this article explores how market anomalies affect the performance of securities in the Johannesburg Stock Exchange’s (JSE’s) resources, industrial, and finance sectors. We investigated the efficacy of several asset pricing models and their capacity to account for market anomalies in the JSE’s resources, industrial, and financial sectors, as well as the applicability of the Fama and French five-factor model. The study used multiple regression techniques and applied stationarity and cointegration methods to ensure robust results. Results also suggest that when the FF5FM is implemented, there is statistical significance at the 10% level for the CMA in the resources sector as the value factor disappears. The FF5FM results in the industrial sector show a significance level of 5% in the SMB. The financial sector seems to have the majority of the style-based risk factors as the SMB is positively significant at a 5% level, the HML is significant at a 1% level, and the CMA is negatively significant at a 10% level of significance. The results suggest that the Carhart Four Factor model is the best to use in all market conditions. Results also show that value becomes redundant in a bullish market, but the opposite holds in a bearish market for a model with operating profitably and investing factors. These findings highlight the necessity for investors to determine which investment risk elements produce abnormal returns in both bearish and bullish market circumstances before investing.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".