High-dimensional sparse portfolio selection with nonnegative constraint

Portfolio selection is a fundamental problem in finance with challenges of dimensionality and market complexities. This paper focuses on the prevalent strategy of passive portfolio management, called index tracking, considering the no-short sales, volatility, transaction costs, and the limited set of effective sam ples. An effective method is proposed for the high-dimensional sparse portfolio selection by using the nonconcave penalty SCAD and the nonnegative constraint. Oracle statistical properties are studied, and the Multiplicative Updates algorithm is applied for the method. The detailed comparisons of the proposed method with other existing nonnegative methods are shown in simulations and empirical analysis, which demonstrate that the proposed method has better performance.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

Portfolio selection, a fundamental problem in finance, is addressed in this paper with considerations of dimensionality and market complexities. The focus is on passive portfolio management strategy, specifically index tracking, taking into account factors such as no-short sales, volatility, transaction costs, and limited effective samples. An effective method utilizing the nonconcave penalty SCAD and nonnegative constraint is proposed for high-dimensional sparse portfolio selection. Statistical properties and the Multiplicative Updates algorithm are studied, and comprehensive comparisons with existing nonnegative methods are provided through simulations and empirical analysis, revealing the superior performance of the proposed method.