Allocation of Large Portfolio by Sparse Group Lasso

发布时间：2026-04-01 作者：浏览次数：

Speaker:	黄磊	DateTime:	2026年4月11日（周六）上午10:30-11:30
Brief Introduction to Speaker: 黄磊，西南交通大学		Place:	国交2号楼315会议室
Abstract:We propose URM-SPAGL, a high-dimensional portfolio selection method that combines the unconstrained regression formulation of mean variance optimization with Sparse Group Lasso regularization and factor based covariance structure.The procedure incorporates industry grouping, allows simultaneous group and within group sparsity, and selects the tuning parameter by risk-constrained cross-validation to target a specified portfolio risk level. Under standard regularity conditions, we establish convergence of the resulting portfolio return to its theoretical target at a rate determined by both element-wise and group level sparsity.Simulation results show that URM-SPAGL achieves near optimal Sharpe ratios,accurate risk control, and negligible white noise selection. Out-of-sample analyses using S&P 500 constituents and Chinese A-Share Market further show that the proposed method delivers competitive risk adjusted performance with substantially lower turnover than benchmark procedures...