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 high frequency data for $2938$ 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，使得初步资产筛选在实际应用中至关重要。现有筛选规则通常依赖标量收益率或低维高频统计量，从而忽略了可能影响风险调整配置的日内风险动态。我们提出度量相关性筛选法（MDS），这是一种将高频信息作为对象值数据纳入资产筛选流程的方法。每个资产日度观测值被表示为结合日收益率与日内风险状态曲线的点-曲线组合对象，并配备加权乘积度量以同时保留收益信息与日内风险动态。MDS通过基于Fréchet变异的依赖性得分对资产进行排序，该得分衡量风险调整目标对资产表征度量离散程度的解释能力。这构成了简洁的两阶段投资组合流程：MDS首先缩减可投资资产池，随后应用标准均值-方差或最小方差配置。我们构建了目标切片估计量，并在α-混合时间序列依赖性和超高维条件下建立了浓度、确信筛选和秩一致性保证。仿真表明MDS在欧氏与非欧氏场景下均表现优异。基于2023年7月至2025年12月期间2938只中国A股的高频数据，我们证明MDS相较于基于收益率和标量依赖性的基准方法显著提升了样本外投资组合绩效，凸显了保留日内风险动态的实践价值。