Effective Online Portfolio Selection for the Long-Short Market Using Mirror Gradient Descent
Why this work is in the frame
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Bibliographic record
Abstract
Online portfolio selection has been actively studied to maximise overall returns by selecting the optimal portfolio weights using online algorithms. However, most work has focused on long-only portfolios, and developing efficient algorithms with loose portfolio constraints remains a challenge. In this letter, the classical online portfolio selection problem is reformulated to allow long/short and margin. For this problem, conventional gradient-based online algorithms face the challenges of high regret and computational complexity due to non-optimal gradients and high-dimensional projections. To tackle this, we propose a novel online algorithm that introduces mirror descent to achieve dimension-free regret in a non-Euclidean space. Specifically, a Bregman divergence is introduced to replace the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2}$</tex-math></inline-formula> norm as a valid proximal setup for the problem to achieve uniform gradients and reduce projection computations. Furthermore, a smoothing technique is developed to reduce the variance of the gradients. The evaluation shows that our algorithm achieves low regret bound and computational complexity, which guarantees a 30% advantage over other strategies in Chinese futures market.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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