Large-scale portfolio optimization with variational neural annealing

• URL: http://arxiv.org/abs/2507.07159v1
• Date: Wed, 09 Jul 2025 17:46:59 GMT
• Title: Large-scale portfolio optimization with variational neural annealing
• Authors: Nishan Ranabhat, Behnam Javanparast, David Goerz, Estelle Inack,
• Abstract summary: Under real-world constraints such as turnover limits and transaction costs, portfolio optimization becomes a mixed-integer nonlinear program.<n>We propose mapping this problem onto a classical Ising-like Hamiltonian and solving it with Variational Annealing (VNA)<n>We demonstrate that VNA can identify near-optimal solutions for more than 2,000 assets and yields performance comparable to that of state-of-the-art formulation as Mosek.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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