We propose post-screening portfolio selection (PS$^2$), a two-step framework for high-dimensional mean--variance investing. First, assets are screened by Lasso-type regression of a constant on excess returns without an intercept. Second, portfolio weights are estimated on the selected set using standard low-dimensional methods. Because strong factors can destroy sparsity in real data, we further introduce PS$^2$ with factors (FPS$^2$), which defactors returns before screening and allows factor investing in the final step. We establish theoretical guarantees, and simulations and an empirical application show competitive performance, especially when sparse screening is appropriate or strong factors are explicitly accommodated.

翻译：我们提出筛选后投资组合选择（PS$^2$），这是一个用于高维均值-方差投资的两步框架。首先，通过Lasso型回归对超额收益相对于常数项进行无截距筛选，以选择资产。其次，在所选资产集上使用标准低维方法估计投资组合权重。由于强因子可能破坏真实数据中的稀疏性，我们进一步引入带因子的PS$^2$（FPS$^2$），该方法在筛选前对收益进行去因子化，并允许在最终步骤中进行因子投资。我们建立了理论保证，模拟和实证应用显示出竞争性的表现，尤其在稀疏筛选适用或明确处理强因子的情况下。