An asset subset-constrained minimax optimization framework for online portfolio selection

Jianfei Yin, Anyang Zhong, Xiaomian Xiao, Ruili Wang, Joshua Zhexue Huang

Research output: Journal PublicationArticlepeer-review

3 Citations (Scopus)

Abstract

Effective online portfolio selection necessitates seamless integration of three key properties: diversity, sparsity, and risk control. However, existing algorithms often prioritize one property at the expense of the others due to inherent conflicts. To address this issue, we propose an asset subset-constrained minimax (ASCM) optimization framework, which generates optimal portfolios from diverse investment strategies represented as asset subsets. ASCM consists of: (i) a minimax optimization model that focuses on risk control by considering a set of loss functions constrained by different asset subsets; (ii) the construction of asset subsets via price-feature clipping, which effectively reduces redundant assets in the portfolio; (iii) a state-based estimation of price trends that guides all ASCM loss functions, facilitating the generation of sparse solutions. We solve the ASCM minimax model using an efficient iterative updating formula derived from the projected subgradient method. Furthermore, we achieve near O(1) time complexity through a novel initialization scheme. Experimental results demonstrate that ASCM outperforms eight representative algorithms, including the best constant rebalanced portfolio in hindsight (BCRP) on five out of the six real-world financial datasets. Notably, ASCM achieves a 67-fold improvement over BCRP in cumulative wealth on the TSE dataset.

Original languageEnglish
Article number124299
JournalExpert Systems with Applications
Volume254
DOIs
Publication statusPublished - 15 Nov 2024
Externally publishedYes

Keywords

  • Asset subset-constrained online portfolio selection
  • Minimax optimization
  • Projected subgradient method
  • Robust online portfolio selection

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence

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