学术报告：From High-dimensional Linear Discriminant Analysis to Markowitz Portfolio Optimization
报告题目：From High-dimensional Linear Discriminant Analysis to Markowitz Portfolio Optimization
报告摘要：High-dimensional linear discriminant analysis (HLDA) suffers from the difficulty of consistent estimation of covariance matrix. Recently, Cai and Liu proposed a linear programming discriminant (LPD) rule which was shown to be Bayes consistent in high-dimensional settings. We further show that the LPD rule is sign consistent under the sparsity assumption. We then bridge HLDA to high dimensional Markowitz portfolio optimization, and propose a linear portfolio optimizer (LPO). Moreover, the LPO estimator is shown to asymptotically yield the maximum expected return while conserving the risk constraint. Simulations on both synthetic and empirical data validate the performance of the proposed method.