报告时间：2024年10月29日 周二下午 15：00
报告地点：纽约国际967线路测试四牌楼校区中心楼二楼教育部重点会议室
组织单位：纽约国际967线路测试 纽约国际967线路测试
邀请人：忻欣教授，纽约国际967线路测试，纽约国际967线路测试复杂工程系统测量与控制教育部重点实验室，纽约国际967线路测试智能无人系统研究院
报告主题：Investment Portfolio Management: Brief Introduction and New Results
报告人简介：Qing-Guo WANG is a member of Academy of Science of South Africa. He is a Distinguished Professor with Institute for Intelligent Systems, University of Johannesburg, South Africa. He received Ph.D. in 1987 with highest honor from Zhejiang University, China. He was AvH Research Fellowship of Germany from 1990 to 1992. From 1992 to 2015, he was with Department of Electrical and Computer Engineering of the National University of Singapore, where he became a Full Professor in 2004. His research lies in the field of Automation/AI with focuses on modelling, estimation, prediction, control and optimization. He has published 420 technical papers in international journals and seven research monographs. He received over 20000 citations with h-index of 82. He was presented with the award of the most cited article of the journal “Automatica” in 2006-2010 and was in the Thomson Reuters list of the highly cited researchers 2013 in Engineering. He received the prize of the most influential paper of the 30 years of the journal “Control Theory and Applications” in 2014. He was on Stanford University list of World's Top 2% Scientists 2020 (both career and year). He was listed on ScholarGPS highly Ranked Scholars-Lifetime in two fields of PID Controller and Control Theory, where he is #6 in PID Controller. He was ranked by Research.com in 2023 within the top 500 scientists in the world and No 1 in South Africa in Electronics and Electrical Engineering. He is currently the deputy Editor-in-Chief of the ISA Transactions (USA).
报告摘要：
The talk starts with the brief introduction to portfolio management problem, including importance of finance, financial bubbles, key factors of investment, theory and problems. It investigates a multi period portfolio management problem over a finite horizon. The objective is to seek the optimal investment policy series which maximizes the weighted sum of a linear combination of the expected return and the variance of portfolio over all the investment periods. This formulation enables the investor to adjust weights for any period and have full freedom and control over their best trade-off between return and risk over each period. We show that such a problem is a convex quadratic programming problem in terms of the decision variables, regardless of price dynamic nature (either linear or nonlinear cases). By solving the convex quadratic programming problem directly, an optimal solution is developed for its original problem without using dynamic programming. The solution is simplified for a general linear price model with high-order and coupled asset dynamics and shown to be implementable with historical price data. Simulation is carried out on USA and China stock markets with real data, which demonstrates feasibility and better performance of the proposed solution than the special case considered in the literature. In particular, the proposed solution with suitable non-zero weights on intermediate time periods offers higher return at the same risk level, compared with one involving the terminal wealth only in the objective function.
