2024/05/13 by Yizun Lin, Lin, Yizun, Yangyu Zhang +5 · 1 citation
Engineering · #FOS: Computer and information sciences #FOS: Economics and business #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Portfolio Management (q-fin.PM) #Reservoir Engineering and Simulation Methods
paper · pdf · doi:10.48550/arxiv.2405.08047
openalex publication_date 2024/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The ℓ0-constrained mean-CVaR model poses a significant challenge due to its NP-hard nature, typically tackled through combinatorial methods characterized by high computational demands. From a markedly different perspective, we propose an innovative autonomous sparse mean-CVaR portfolio model, capable of approximating the original ℓ0-constrained mean-CVaR model with arbitrary accuracy. The core idea is to convert the ℓ0 constraint into an indicator function and subsequently handle it through a tailed approximation. We then propose a proximal alternating linearized minimization algorithm, coupled with a nested fixed-point proximity algorithm (both convergent), to iteratively solve the model. Autonomy in sparsity refers to retaining a significant portion of assets within the selected asset pool during adjustments in pool size. Consequently, our framework offers a theoretically guaranteed approximation of the ℓ0-constrained mean-CVaR model, improving computational efficiency while providing a robust asset selection scheme.