Large scale portfolio choice is highly sensitive to estimation error, making the preliminary asset selection essential in empirical implementation. Existing selection rules typically rely on scalar returns or low dimensional high frequency summaries, and thus discard intraday risk dynamics that may be relevant for risk adjusted allocation. We propose Metric Dependence Screening (MDS), an asset selection procedure that incorporates high frequency information as object valued data. Each asset day observation is represented as a point-curve object combining daily return with an intraday risk state curve, equipped with a weighted product metric that preserves both reward information and within day risk dynamics. MDS ranks assets by a Fréchet variation based dependence score, measuring how much a risk adjusted target explains the metric dispersion of the asset representations. This yields a simple two stage portfolio procedure: MDS first reduces the investable universe, and standard mean-variance or minimum variance allocation is then applied. We develop a target slicing estimator and establish concentration, sure selection, and rank consistency guarantees under $α$-mixing time series dependence and ultrahigh dimensionality. Simulations show that MDS performs well across both Euclidean and non-Euclidean settings. Using 5 minute data for 2,938 Chinese A-share stocks from July 2023 to December 2025, we demonstrate that MDS improves out of sample portfolio performance over return based and scalar dependence based benchmarks, highlighting the value of preserving intraday risk dynamics.

翻译：大规模投资组合选择对估计误差高度敏感，因此在实际应用中，初步的资产筛选至关重要。现有筛选规则通常依赖于标量收益率或低维度的高频汇总数据，从而忽略了可能与风险调整配置相关的日内风险动态。我们提出度量依赖筛选方法（Metric Dependence Screening, MDS），这是一种将高频信息作为对象值数据纳入的资产筛选程序。每个资产日观测值被表示为一个点-曲线对象，结合了日收益率与日内风险状态曲线，并配备一个加权乘积度量，以同时保留收益信息和日内风险动态。MDS通过基于Fréchet变差的依赖得分对资产进行排序，该得分衡量风险调整目标对资产表示度量离散程度的解释能力。这形成了一种简单的两阶段投资组合流程：MDS首先缩减可投资范围，随后应用标准均值-方差或最小方差分配。我们开发了一种目标切片估计器，并在$α$-混合时间序列依赖和超高维条件下建立了集中性、必然筛选与秩一致性保证。模拟实验表明，MDS在欧几里得和非欧几里得设定下均表现良好。使用2023年7月至2025年12月期间2,938只中国A股股票的5分钟数据，我们证明MDS相比于基于收益率和基于标量依赖的基准方法，能够提升样本外投资组合表现，凸显了保留日内风险动态的价值。