  We present the first application of modern Hopfield networks to the problem
of portfolio optimization. We performed an extensive study based on
combinatorial purged cross-validation over several datasets and compared our
results to both traditional and deep-learning-based methods for portfolio
selection. Compared to state-of-the-art deep-learning methods such as
Long-Short Term Memory networks and Transformers, we find that the proposed
approach performs on par or better, while providing faster training times and
better stability. Our results show that Modern Hopfield Networks represent a
promising approach to portfolio optimization, allowing for an efficient,
scalable, and robust solution for asset allocation, risk management, and
dynamic rebalancing.
