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arXiv · 2411.06566

A Fully Analog Pipeline for Portfolio Optimization

James S. Cummins·Natalia G. Berloff
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Abstract

Portfolio optimization is a ubiquitous problem in financial mathematics that relies on accurate estimates of covariance matrices for asset returns. However, estimates of pairwise covariance could be better and calculating time-sensitive optimal portfolios is energy-intensive for digital computers. We present an energy-efficient, fast, and fully analog pipeline for solving portfolio optimization problems that overcomes these limitations. The analog paradigm leverages the fundamental principles of physics to recover accurate optimal portfolios in a two-step process. Firstly, we utilize equilibrium propagation, an analog alternative to backpropagation, to train linear autoencoder neural networks to calculate low-rank covariance matrices. Then, analog continuous Hopfield networks output the minimum variance portfolio for a given desired expected return. The entire efficient frontier may then be recovered, and an optimal portfolio selected based on risk appetite.

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q-fin.PMcond-mat.dis-nncs.CEphysics.opticsquant-ph

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James S. Cummins, Natalia G. Berloff. 2024-11-10. A Fully Analog Pipeline for Portfolio Optimization. https://arxiv.org/abs/2411.06566

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