Risk-return adaptive receding Horizon Index Tracking Strategy
Bibliographic record
Abstract
Index tracking is a well-established financial strategy for passive investing. Typical index tracking models are single period in nature, deriving an optimal tracking portfolio based on future price/return estimates, using most if not all index constituent assets. In this article, we propose a framework for index tracking that can accommodate multi-periods and asset selection. First, we propose a risk-return-based index tracking strategy within a multi-period adaptive receding horizon framework. The framework demonstrates strong tracking fidelity with the benchmark whilst accounting for future tracking states. We then adapt a Penalized Alternating Direction Method (PADM) to the multi-period framework to efficiently enforce a limit on tracking portfolio size (cardinality). The PADM produces high-quality solutions to the cardinality-constrained models and can be used effectively in both low and higher re-balancing frequency environments. Finally, we generalize our base multi-period formulation to an enhanced index tracking strategy, which can easily accommodate possible portfolio manager (PM) preferences. We present computational results that indicate that our cardinality-constrained and non-cardinality-constrained adaptive receding horizon framework for index tracking yields high tracking accuracy when compared to equivalent single-period or return-based models used in a rolling horizon framework.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".