Large-scale portfolio optimization with variational neural annealing
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913934875820032 |
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| author | Ranabhat, Nishan Javanparast, Behnam Goerz, David Inack, Estelle |
| author_facet | Ranabhat, Nishan Javanparast, Behnam Goerz, David Inack, Estelle |
| contents | Portfolio optimization is a routine asset management operation conducted in financial institutions around the world. However, under real-world constraints such as turnover limits and transaction costs, its formulation becomes a mixed-integer nonlinear program that current mixed-integer optimizers often struggle to solve. We propose mapping this problem onto a classical Ising-like Hamiltonian and solving it with Variational Neural Annealing (VNA), via its classical formulation implemented using autoregressive neural networks. We demonstrate that VNA can identify near-optimal solutions for portfolios comprising more than 2,000 assets and yields performance comparable to that of state-of-the-art optimizers, such as Mosek, while exhibiting faster convergence on hard instances. Finally, we present a dynamical finite-size scaling analysis applied to the S&P 500, Russell 1000, and Russell 3000 indices, revealing universal behavior and polynomial annealing time scaling of the VNA algorithm on portfolio optimization problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07159 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large-scale portfolio optimization with variational neural annealing Ranabhat, Nishan Javanparast, Behnam Goerz, David Inack, Estelle Disordered Systems and Neural Networks Statistical Mechanics Machine Learning Portfolio Management Portfolio optimization is a routine asset management operation conducted in financial institutions around the world. However, under real-world constraints such as turnover limits and transaction costs, its formulation becomes a mixed-integer nonlinear program that current mixed-integer optimizers often struggle to solve. We propose mapping this problem onto a classical Ising-like Hamiltonian and solving it with Variational Neural Annealing (VNA), via its classical formulation implemented using autoregressive neural networks. We demonstrate that VNA can identify near-optimal solutions for portfolios comprising more than 2,000 assets and yields performance comparable to that of state-of-the-art optimizers, such as Mosek, while exhibiting faster convergence on hard instances. Finally, we present a dynamical finite-size scaling analysis applied to the S&P 500, Russell 1000, and Russell 3000 indices, revealing universal behavior and polynomial annealing time scaling of the VNA algorithm on portfolio optimization problems. |
| title | Large-scale portfolio optimization with variational neural annealing |
| topic | Disordered Systems and Neural Networks Statistical Mechanics Machine Learning Portfolio Management |
| url | https://arxiv.org/abs/2507.07159 |