Dynamic Selection of Optimal Sub-portfolio - Application in the Regional Stock Exchange of Securities in West Africa (BRVM)
Bibliographic record
Abstract
In this paper, we consider a generalization of the mean-variance model of Markowitz, including a new limiting constraint on marginal returns through the PER(price earnings ratio), that we apply to an optimal portfolio selection in the Regional Stock Exchange of Securities in West africa (BRVM). We study the PER restriction impact through a dynamic selection of optimal sub-portfolios from an initial portfolio. We trace out dynamically the efficient frontiers of the sub-portfolios for different integer values of the PER. The main objective is to provide, for every level of performance (in term of returns) and for a fixed PER, the selection of optimal sub-portfolios that should lead, for a minimal risk, to the same performance as the original portfolio. We show that based on the PER, we can know for each level of performance, the securities to turn down to be able to define the best selection that converge to the optimal portfolio. Moreover, this work is a contribution to the development efforts of our financial systems and our West African stock market environment. The plots of the efficient frontiers of the sub-portfolios were carried out throughout data collected from the BRVM (Regional Stock Exchange Securities) in the period from October 2014 to February 2015.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".