Enhancing an existing algorithm for small-cardinality constrained portfolio optimisation
Bibliographic record
Abstract
The efficient frontier (EF) allows an investor to (in theory) maximise their return for a given level of risk. Portfolios on the EF may contain many assets, making their management difficult and possibly expensive. An investor may wish to impose an upper bound on the number of assets in their portfolio, leading to so-called cardinality constrained efficient frontiers (CCEFs). Recently, a new algorithm was developed to find CCEFs for small cardinalities. Relative to other algorithms for this problem, this algorithm is very intuitive, and its authors demonstrated that it performs at nearly the state-of-the-art. However, we have found that the algorithm seems to struggle in certain situations, particularly when faced with both bonds and equities. While preserving its intuitiveness, we modified the algorithm to improve its CCEFs. This improvement comes with longer runtimes, but we think many practitioners will prefer the algorithm with modifications. Some practitioners may prefer other algorithms, due to the runtimes or because some points on our CCEFs still fall short of optimality. However, in addition to its intuitiveness, our modified algorithms (and the original version) find low-risk points on the CCEF that a state-of-the-art algorithm does not.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".