Dynamic Weight Optimization for Double Linear Policy: A Stochastic Model Predictive Control Approach
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912993736916992 |
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| author | Hong, Tan Chin Hsieh, Chung-Han |
| author_facet | Hong, Tan Chin Hsieh, Chung-Han |
| contents | The Double Linear Policy (DLP) framework guarantees a Robust Positive Expectation (RPE) under optimized constant-weight designs or admissible prespecified time-varying policies. However, the sequential optimization of these time-varying weights remains an open challenge. To address this gap, we propose a Stochastic Model Predictive Control (SMPC) framework. We formulate weight selection as a receding-horizon optimal control problem that explicitly maximizes risk-adjusted returns while enforcing survivability and predicted positive expectation constraints. Notably, an analytical gradient is derived for the non-convex objective function, enabling efficient optimization via the L-BFGS-B algorithm. Empirical results demonstrate that this dynamic, closed-loop approach improves risk-adjusted performance and drawdown control relative to constant-weight and prescribed time-varying DLP baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_00415 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dynamic Weight Optimization for Double Linear Policy: A Stochastic Model Predictive Control Approach Hong, Tan Chin Hsieh, Chung-Han Systems and Control Optimization and Control Computational Finance 93E20, 93E03, 93B45, 91-08 The Double Linear Policy (DLP) framework guarantees a Robust Positive Expectation (RPE) under optimized constant-weight designs or admissible prespecified time-varying policies. However, the sequential optimization of these time-varying weights remains an open challenge. To address this gap, we propose a Stochastic Model Predictive Control (SMPC) framework. We formulate weight selection as a receding-horizon optimal control problem that explicitly maximizes risk-adjusted returns while enforcing survivability and predicted positive expectation constraints. Notably, an analytical gradient is derived for the non-convex objective function, enabling efficient optimization via the L-BFGS-B algorithm. Empirical results demonstrate that this dynamic, closed-loop approach improves risk-adjusted performance and drawdown control relative to constant-weight and prescribed time-varying DLP baselines. |
| title | Dynamic Weight Optimization for Double Linear Policy: A Stochastic Model Predictive Control Approach |
| topic | Systems and Control Optimization and Control Computational Finance 93E20, 93E03, 93B45, 91-08 |
| url | https://arxiv.org/abs/2604.00415 |