Portfolio diversification using ant brood sorting clustering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The process of uncovering underlying intelligence in financial time series is non-intuitive; therefore, data analysis techniques such as clustering (i.e. grouping a collection of objects such that objects in the same group are more similar to each other than those in the other groups) are often used to extract intelligence from financial time series. In this paper, we investigate using the ant brood sorting clustering technique to extract a new form of intelligence from financial time series that can be used in diversifying portfolio composition. Brood sorting is a nature-inspired computing technique modeled after the natural phenomenon of cemetery organization and sorting of broods amongst ants. The technique reveals promising results that can be used in making informed decision on the collection of assets that can be owned together in order to minimize possible losses (in the case of a down-turn of the economy) or maximize gain (in the case of a growing economy).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it