不依赖预期收益，用动态准入和因子约束构建更稳健的组合。
Dynamic Inclusion and Bounded Multi-Factor Tilts for Robust Portfolio Construction
- 根据流动性等市场条件动态调整可投资资产，自动调节因子暴露。
- 通过硬性结构约束控制集中度与换手率，避免模型脆弱性。
- 适合长期配置，对参数敏感度低，操作透明且易实施。
本文提出一种投资组合构建框架，旨在应对估计误差、非平稳性和实际交易约束下的鲁棒性问题。方法结合动态资产准入、确定性再平衡及对等权重基准的有界多因子倾斜。资产准入被形式化为状态相关的组合约束，使因子暴露能内生响应可观测市场条件（如流动性、波动率、横截面广度）。该框架不依赖预期收益或协方差估计，而是基于横截面排序与硬性结构约束，控制集中度、换手率和脆弱性。结果方法完全算法化、透明且可直接执行，为参数化优化与无约束多因子模型提供鲁棒替代方案，特别适用于长期配置中以稳定性和可操作性为核心目标的场景。
原文摘要 · Abstract (English)
This paper proposes a portfolio construction framework designed to remain robust under estimation error, non-stationarity, and realistic trading constraints. The methodology combines dynamic asset eligibility, deterministic rebalancing, and bounded multi-factor tilts applied to an equal-weight baseline. Asset eligibility is formalized as a state-dependent constraint on portfolio construction, allowing factor exposure to adjust endogenously in response to observable market conditions such as liquidity, volatility, and cross-sectional breadth. Rather than estimating expected returns or covariances, the framework relies on cross-sectional rankings and hard structural bounds to control concentration, turnover, and fragility. The resulting approach is fully algorithmic, transparent, and directly implementable. It provides a robustness-oriented alternative to parametric optimization and unconstrained multi-factor models, particularly suited for long-horizon allocations where stability and operational feasibility are primary objectives.
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