How Efficiently Can Infant Stock Markets Exhibit the Random Walk? Evidence From Malawi
Bibliographic record
Abstract
It is no secret that the Malawi Stock Exchange (MSE) is still in its infancy. In 2011, the Malawi government in conjunction with the World Bank launched the Financial Sector Technical Assistance Project (FSTAP). The project targeted an improvement in financial literacy and also the automation of trading on the MSE to an advanced stage so as to improve market efficiency. This paper investigated the weak form and semi-strong efficient market hypotheses on the Malawi Stock Market in the wake of such a project with aid of parametric and non-parametric tests. The weak form efficiency of the market is tested by the application of Lo and MacKinlay’s Variance ratio test, the Cumby-Huizinga autocorrelation test and the Phillips-Perron unit root test. An adjustment to the methodology suggested by Borges (2009) is employed to ascertain the presence of market anomalies and by extension test out semi-strong form of efficiency. The paper employed more recent and comprehensive data stretching back to January 2010 through to June, 2022, amounting to 12years and 6months. Results are in support of weak form efficiency. However, the paper found significant evidence against semi-strong efficiency of the MSE. Calendar effects like day-of-the-week effect and turn-of-the-year effect were deemed to be absent from the market but turn-of-the-month effect was existent. Results of applying the Fama and French three-factor model to a time series regression reveal the presence of size and value effects. As such, the paper concludes that the Malawi Stock Market is weak-form efficient but semi-strong inefficient.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".