Risk & Portfolio (风险与组合)
7 分类索引 — Risk & Portfolio。完整概念清单在 concepts/index, 本页只列本分类。
业绩归因 / Alpha Beta 切分
风险测度 / 优化框架
- Volatility Targeting (波动率目标
- Volatility-Managed Portfolio
- Risk Parity
- Coherent Risk Measures
- Robust Optimization
- Stochastic Optimization
- Markowitz Curse (马科维茨诅咒
ML-driven 优化
- Decision-Focused Learning for MVO
- Decision-Focused Sparse Tangent Portfolio (决策聚焦稀疏切点组合 (Jeon et al. 2026 ICML: DPP-QCQP + 软 top-k 端到端; KOSPI200 ρ=20% Sharpe 2.098; MDD 更深是代价)
- Prediction-Loss Guided DFL (预测损失引导的决策聚焦学习 (Jeon et al. 2025: 梯度空间角平分融合替代固定 loss 权重; Pareto 平稳收敛 O(1/\sqrt T); 噪声越大优势越大)
- Predict-then-Optimize
- Mean-Variance Optimization with Transaction Cost
- Linearity-Generating Strategies (LGS (CD-Daniel-Sağlam-Moallemi 2014 多期动态组合 + TC 闭式解)
- Label Horizon Paradox (标签视界悖论 (Song-Liu-Chen 2026; 训练标签视界 ≠ 推理视界; BLO 自适应选择; 10 模型 × 3 数据集一致提升 IC)
- 市值加权的两侧分裂 (Market-Cap Weighting Splits Risk and Opportunity (华安金工消融: 风险侧 IC 持平, 机会侧 0.1568→−0.0034 — A 股机会来自中小盘结构扩散)
- Bures–Wasserstein 协方差预测 (BW Covariance Forecasting (RFM on BW manifold: 协方差预测误差均值 0.94 vs EWMA 1.45; 因子 1 ≈ VIX)
- 协方差估计与预测的方法选择 (Estimation vs Forecasting for Covariance (收缩=estimation vs RFM=forecasting+解释; 数据形态→方法对照表; pipeline 收缩在前)
- Signal-Noise Decomposition in Label Design — 理论 (Theorem 3.2)
- Adaptive Horizon Learning via Bi-level Optimization — BLO 算法 (warm-up + 熵正则)
风险模型增强
联合因子-图学习 (MINGLE)
Cross-ref
完整页清单
本文件夹全部 27 个概念页。返回: Concepts 总索引 · Quant Wiki 总索引
- Adaptive Asset Allocation
- Adaptive CutV 谱分割
- Adaptive Horizon Learning via Bi-level Optimization (双层优化的自适应标签视界学习
- Alternative Alpha vs Alternative Beta (另类 alpha 与另类 beta
- Coherent Risk Measures
- Contagion Cut 分配策略
- Decision-Focused Learning for MVO
- Decision-Focused Sparse Tangent Portfolio (决策聚焦稀疏切点组合
- Prediction-Loss Guided DFL (预测损失引导的决策聚焦学习
- Label Horizon Paradox (标签视界悖论
- Linearity-Generating Strategies (LGS
- MINGLE 框架
- Markowitz Curse (马科维茨诅咒
- Mean-Variance Optimization with Transaction Cost
- Predict-then-Optimize
- Risk Model Enhancement via Transient Statistical Factors
- Risk Parity
- Robust Optimization
- Signal-Noise Decomposition in Label Design (监督信号的信噪分解
- Statistical Factors vs Fundamental Factors
- Stochastic Optimization
- Volatility Targeting
- Volatility-Managed Portfolio (波动率管理组合
- 联合因子-图学习 (Joint Factor-Graph Learning
- 市值加权的两侧分裂 (Market-Cap Weighting Splits Risk and Opportunity
- Bures–Wasserstein 协方差预测 (BW Covariance Forecasting
- 协方差估计与预测的方法选择 (Estimation vs Forecasting for Covariance