To address the challenges of rapid changes in stock market conditions, significant sequence noise, and the difficulty of timely risk exposure adjustments using traditional static allocation, this paper constructs a dynamic portfolio rebalancing model, denoted as VMD-RC-PPO, based on point-to-point data processing, rolling variational mode decomposition, and risk-constrained near-end strategy optimization. At each rebalancing point, the model constructs the state using only publicly available market data, company behavior, historical constituent stocks, and newly disclosed financial information disclosed before market close. The model uses point-to-point adjustment sequences to construct price features and completes order execution, cash deduction, and position valuation using the original unadjusted prices. To address potential instability issues at window boundaries, the model performs mirror extension and periodic recalculation within a 60-day historical window, retaining only the frequency structure features within the inner interval. The strategy uses weekly account logarithmic net return minus actual turnover penalty as a reward, preserving volatility and drawdown in the state, and controlling risk through a cash floor constraint. All benchmark strategies operate under the same stock pool, transaction costs, position boundaries, and trading rules. This paper establishes a dynamic rebalancing framework that connects information availability, state construction, actual transactions, and account feedback, which can provide a methodological reference for the research of long-term bullish portfolio strategies in A-shares.

Jiayi Sun, Portfolio Dynamic Rebalancing Strategy Generation Model Based on Deep Reinforcement Learning. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 2: 1-13. https://doi.doi.org/10.3807/IJSSEM.2026.070201.

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