A0756
Title: A new regularization method for high-dimensional portfolio selection
Authors: Songshan Yang - Renmin University of China (China) [presenting]
Abstract: With the global financial market experiencing continuous expansion and escalating volatility, the development of efficient strategies for high-dimensional portfolio selection 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. The aim is to address portfolio optimization challenges within this context, using the 2020-2021 U.S. stock market as a case study. A Dantzig-type portfolio optimization (DPO) model is proposed, and an efficient parallel computing algorithm is presented based on asset-splitting. Through empirical analysis of the S\&P 500 and Russell 2000 indices, the consistent outperformance of the DPO portfolios is demonstrated over corresponding ETFs in terms of Sharpe and Sortino ratios, especially for the Russell 2000 index. A new, effective approach is provided for investors seeking to optimize their portfolios in complex market environments.
