The Contribution of African Capital Markets in the Diversification of European Investment Portfolios
Bibliographic record
Abstract
This study aims to evaluate the contribution of the African capital markets in the diversification of European investment portfolios. The study used the methodology based on the application of optimization models like Mean Variance (MV), Resample Michaud (RM), SemiVariance (SV), Mean Absolute Deviation (MAD) and Filtered Historical Simulation (FHS). In-Sample and Out-of-Sample approaches were used to analyze the data. The study results suggested the existence of a strong correlation between some African capital markets and European capital markets, that is, they tend to move in the same direction. The most important being the diversification of global portfolio with assets of African capital markets generate benefits for both types of investors; that is, it provides benefits in the return and reduce investment risk. Still, the study result suggested that the foreign investors should look for an African capital markets with a chance to maximize their wealth and diversify the investment risk in their portfolios. In the same order, the study result went further to elaborate contribute to on the advantage of the international diversification and furthermore contribute to the literature through application of the Filtered Historical Simulation (FHS) method in the optimization portfolio. This methodology, In addition to producing good results, is more restrained in the composition of investment portfolios than the other methods.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".