Informative index for investment based on Kelly criterion
Why this work is in the frame
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Bibliographic record
Abstract
When it comes to asset allocation and portfolio management, Kelly criterion is a mathematical formula used to optimise expected log-returns over the long term. Nonetheless, not all stocks are well suited for analysis using Kelly criterion, due to their transient nature and noisy data. This paper presents an innovative index by which to assess the suitability of stocks for analysis using the Kelly criterion. When applied to real-world stock data, the correlation coefficient between the proposed KSI and log-returns based on the Kelly criterion was −57.045% with a p-value of 1.215×10−1. In a robustness test based on the Mid-Cap 100 dataset, the correlation coefficient was −44.064% with a p-value of 2.438×10−5. The results demonstrate the efficacy of the KSI for portfolio management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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