Multi-Attribute Portfolio Selection with Genetic Optimization Algorithms
Bibliographic record
Abstract
The traditional portfolio theory first proposed by Markowitz only provides a solution to capital allocation to a pre-determined set of assets, regardless of asset quality. To remedy this gap, a multi-attribute asset quality analysis, before asset allocation, is proposed. Thus a two-stage multi-attribute portfolio selection framework that considers asset quality, as well as asset allocation, is formulated. For solving the proposed portfolio selection problem, this study applies genetic algorithms for multi-attribute portfolio selection and analysis. In the first stage, i.e. asset quality evaluation, a genetic algorithm is used to identify good quality assets in terms of asset ranking. In the asset allocation stage, allocation of capital to individual high-quality assets is optimized using another genetic algorithm based on Markowitz's mean-variance theory. Through the two-stage asset evaluation and allocation process, an optimal portfolio can be determined in the context of considering both multiple asset return attributes and risk exposures. Experimental results reveal that the proposed multi-attribute portfolio selection framework provides a very feasible and useful tool to assist investors in planning their investment strategy and constructing their portfolios.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".