Hopfield Networks for Asset Allocation

TL;DR

This paper introduces the application of modern Hopfield networks to portfolio optimization, demonstrating competitive performance, faster training, and enhanced stability compared to existing deep-learning methods.

Contribution

It is the first to apply modern Hopfield networks to asset allocation, showing their effectiveness and efficiency in portfolio optimization tasks.

Findings

Hopfield networks perform on par or better than LSTMs and Transformers.

The approach offers faster training and improved stability.

Results are validated across multiple datasets.

Abstract

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.