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Dynamic Weight Optimization for Double Linear Policy: A Stochastic Model Predictive Control Approach

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  • Tan Chin Hong
  • Chung-Han Hsieh

Abstract

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.

Suggested Citation

  • Tan Chin Hong & Chung-Han Hsieh, 2026. "Dynamic Weight Optimization for Double Linear Policy: A Stochastic Model Predictive Control Approach," Papers 2604.00415, arXiv.org.
    • Handle: RePEc:arx:papers:2604.00415
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    File URL: http://arxiv.org/pdf/2604.00415
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