A basket half full: sparse portfolios
针对现有稀疏投资组合方法局限于低维、缺乏理论分析且因ℓ1惩罚导致次优的问题，提出了一种高维稀疏投资组合构建方法，从理论上建立了稀疏权重估计量的Oracle界并指导其分布，实证发现该策略在经济衰退时稳健且可作为对冲工具。
The existing approaches to sparse wealth allocations (1) are limited to low-dimensional setup when the number of assets is less than the sample size; (2) lack theoretical analysis of sparse wealth allocations and their impact on portfolio exposure; (3) are suboptimal due to the bias induced by an ℓ1-penalty. We address these shortcomings and develop an approach to construct sparse portfolios in high dimensions. Our contribution is twofold: from the theoretical perspective, we establish the oracle bounds of sparse weight estimators and provide guidance regarding their distribution. From the empirical perspective, we examine the merit of sparse portfolios during different market scenarios. We find that in contrast to non-sparse counterparts, our strategy is robust to recessions and can be used as a hedging vehicle during such times.