Autonomous Sparse Mean-CVaR Portfolio Optimization
<|reference_start|>Autonomous Sparse Mean-CVaR Portfolio Optimization: The $\ell_0$-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 $\ell_0$-constrained mean-CVaR model with arbitrary accuracy. The core idea is to convert the $\ell_0$ 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 $\ell_0$-constrained mean-CVaR model, improving computational efficiency while providing a robust asset selection scheme.<|reference_end|>
@article{lin2024autonomous,
    title={Autonomous Sparse Mean-CVaR Portfolio Optimization},
    author={Yizun Lin, Yangyu Zhang, Zhao-Rong Lai and Cheng Li},
    journal={arXiv preprint arXiv:2405.08047},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08047},
    primaryClass={math.OC cs.LG q-fin.PM}
}