Synchronous Set-Based Particle Swarm Optimization: Heuristics for Portfolio Optimization
Bibliographic record
Abstract
Portfolio optimization (PO) difficulties entail deciding which assets to invest and allocating the weights in those assets in order to maximize overall return while minimizing overall risk at the same time. With an increase in the vast number of assets available to invest, the stock selection and optimal asset weight allocation becomes more complex. In recent studies, researchers have achieved better performance in asset selection and weight allocation to an extent using nature-inspired algorithms than traditional methods often at the cost of heavy computing power used in blending multiple methods, or considering a small pool of assets. In this study, we propose a novel heuristics, which we call synchronous set-based particle swarm optimization (SSBPSO), that performs a large scale stock selection and weight optimization to generate resilient portfolios from a large pool of assets. The portfolios are generated from the pool of stocks that are constituents of stock indexes and their performance is compared with the indexes itself. We used three stock indexes from around the world and generated portfolios using SSBPSO, the returns of portfolio generated outperform the stock indexes in terms of portfolio return.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".