用精简神经网络降低杠杆投资的波动损耗，提升资金效率。
Neural Network-Driven Volatility Drag Mitigation under Aggressive Leverage
- 用五参数加权移动平均和门控单元替代复杂层，模型参数从39586减至2175。
- 在模拟测试中实现最低实际波动率，相同收益下可承受更高杠杆。
- 适合追求高杠杆稳健性的量化交易研究者和金融机构使用。
本文提出一种紧凑的模块化端到端神经网络架构，用于全球最小方差投资组合优化，将模型复杂度与回看窗口长度及资产规模解耦。采用五参数双曲加权移动平均与饱和指数函数，取代原有的2400参数滞后变换层；结合双向门控循环单元特征清洗模块与简化边缘波动率网络，使总可学习参数从39,586降至2,175。在离样本测试中，该紧凑网络在不牺牲预期收益的前提下，实现了低于先进非线性收缩与风险平价基准的最低实际投资组合方差。在仅允许做多约束下，方差降低支持显著更高的杠杆水平，同时保持相近回撤控制。在包含真实追加保证金动态的高保真交易模拟器中验证，其具备更强的过度杠杆抗性。结果表明，端到端方差最小化架构可在不损失风险调整收益的情况下，实现显著的参数效率与资本效率提升。
原文摘要 · Abstract (English)
This paper introduces a compact reformulation of a modular end-to-end neural network for global minimum-variance portfolio optimization that decouples model complexity from both look-back window length and universe size. A five-parameter hyperbolic weighted moving average combined with a saturating exponential replaces the original 2,400-parameter lag-transformation layer, and a bidirectional gated-recurrent-unit eigencleaning module together with a streamlined marginal-volatility network reduce total learnable parameters from 39,586 to just 2,175. In out-of-sample tests against state-of-the-art nonlinear-shrinkage and risk-parity benchmarks, the compact network attains the lowest realized portfolio variance without compromising expected return. Under long-only constraints, the variance reduction supports substantially higher leverage while maintaining comparable drawdown control. Validation in a high-fidelity trading simulator that incorporates realistic margin-call dynamics confirms enhanced over-leverage resilience. These findings demonstrate that end-to-end variance-minimization architectures can achieve substantial parameter efficiency and robust capital-efficiency gains without sacrificing risk-adjusted performance.
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