发表机构
Quantitative Analysis and Artificial Intelligence Department, Miralta Finance Bank S.A.; Department of Computer Science, University of Reading（Miralta Finance Bank S.A. 量化分析与人工智能部; 雷丁大学计算机系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出将条件信息作为受硬性可容许约束的决策变量，建立可容许投资组合优化框架，证明其存在性与价值定理，并量化因果识别的预言机代价，在127个驱动变量的市场数据上验证了条件在个股上可实现、在预分散组合上不可实现。
AI 中文摘要
均值-方差投资组合选择将条件信息视为给定，并在权重上进行优化，因此关于该信息的两种错误会未被察觉地传入投资组合：使用了决策时不可用的变量，以及将共同变动视为特质性变动。我们将条件信息设为决策变量，并受制于事先声明的硬性可容许约束：可用性、对决策滤子的无套利保持扩张、统计分离，以及对于干预性主张而言，跨声明制度的不变性。一个词典序的可容许顺序（其中不涉及任何风险-收益量）选择最优信息类别，经典问题在该类别内部求解。我们证明了存在性、在重新编码下的不变性，以及一个可容许信息价值定理，其失效条件表明，按决策损失进行选择会在存在时纳入前瞻信息；两阶段解与联合包络是同一集合上两个序的最小元素。在精确分离下，风险的可分散部分是容许类别的属性，且仅在干预可容许性下才具有干预稳定性；要求因果识别会带来一个显式的预言机代价，与搜索复杂度相权衡。估计量具有一致性和二阶遗憾。在包含127个候选驱动变量的市场数据上，该条件在个股上可实现，其中扩大搜索会以可测量的速率减少缺陷；而在预分散组合上则几乎对搜索不变，因为残差依赖本身就是共同因子。在条件失效之处，协方差仍能产生与收缩法匹配的最低方差组合，但换手率显著更低，同时丢弃了其风险中可测量的部分，精确分解将其归因于残差份额、广度和平均残差相关性。
英文摘要
Mean--variance portfolio choice takes the conditioning information as given and optimizes over weights, so two errors about that information pass into the portfolio unseen: using variables unavailable at the decision time and treating common variation as idiosyncratic. We make the conditioning information a decision variable subject to hard admissibility constraints declared in advance: availability, a no-arbitrage-preserving enlargement of the decision filtration, statistical separation and, for interventional claims, invariance across declared regimes. A lexicographic admissibility order in which no risk--return quantity enters selects an optimal information class, and the classical problem is solved inside it. We prove existence, invariance under recodings, and a value-of-admissible-information theorem whose failure conditions show that selection by decision loss admits look-ahead information whenever present; the two-stage solution and the joint envelope are the minimal elements of two orders on one set. Under exact separation the diversifiable part of risk is a property of the admissible class and is interventionally stable only under interventional admissibility; requiring causal identification carries an explicit oracle price traded against search complexity. The estimator is consistent with second-order regret. On market data with 127 candidate drivers the condition is attainable on individual equities, where enlarging the search reduces the defect at a measurable rate, and not on pre-diversified portfolios, where it is nearly invariant to the search because the residual dependence is the common factor itself. Where it fails, the covariance still yields minimum-variance portfolios that match shrinkage at markedly lower turnover while discarding a measurable part of their risk, which an exact decomposition attributes to the residual share, breadth and average residual correlation.
Comments40 pages, 13 figures, 6 tables
