学术报告：From High-dimensional Linear Discriminant Analysis to Markowitz Portfolio Optimization
发布时间：2021-04-27 浏览次数：300
报告人：张振 博士
报告时间：2021年4月29日上午10:00-11:00
报告地点：雁山校区理1-516A会议室
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.
报告人简介：张振现任南方科技大学数学系副教授。主要研究领域在于应用问题的建模和计算，特别是数值偏微分方程，多相复杂流模型，以及高维数据分析。文章发表在SIAM J. Appl. Math.，Phys. Fluids，J. Am. Stat. Assoc.，Pattern Recognition，Phys. Rev. E等国际一流期刊上。他的博士毕业论文曾获2015年香港数学学会最佳博士论文奖。他主持国家自然科学基金项目2项，广东省自然科学基金面上项目1项。他入选了2017年中组部“青年千人计划”。