| 主讲人 |
杨松山 |
简介 |
<p>With the global financial market experiencing continuous expansion and escalating volatility, the development of efficient strategies for high-dimensional portfolio allocation has become critically important. Previous approaches to high-dimensional portfolio selection have mainly focused on large-cap companies, presenting challenges when confronted with datasets such as the Russell 2000 index. This paper aims to address portfolio optimization challenges within this context, using the 2020-2021 U.S. stock market as a case study. We propose a Dantzig-type portfolio optimization (DPO) model, and present efficient parallel computing algorithms based on asset-splitting. Through empirical analysis on the S&P 500 and Russell 2000 indices, we demonstrate the consistent outperformance of the DPO portfolios over Markowitz mean-variance and Lasso-type mean-variance models, as well as corresponding ETFs, in terms of Sharpe and Sortino ratios. This outperformance is particularly pronounced for the Russell 2000 index. We provide a new effective approach for investors seeking to optimize their portfolios in complex market environments.</p> |
