Hopfield Networks for Asset Allocation

ArXiv ID: 2407.17645 “View on arXiv”

Authors: Unknown

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.

Keywords: Modern Hopfield Networks, Portfolio Optimization, Combinatorial Purged Cross-Validation, Asset Allocation, Deep Learning, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper introduces advanced deep learning architectures like Modern Hopfield Networks, which involve sophisticated energy-based models and attention mechanisms, indicating high math complexity. It also demonstrates strong empirical rigor through extensive combinatorial purged cross-validation, comparison against state-of-the-art baselines, and reporting of multiple financial metrics like Sharpe and Sortino ratios.

  flowchart TD
    A["Research Goal: Apply Modern Hopfield Networks<br>to Portfolio Optimization"] --> B["Data Input: Multiple Datasets<br>Equities Asset Allocation"]
    B --> C["Methodology: Combinatorial<br>Purged Cross-Validation"]
    C --> D["Computational Process:<br>Modern Hopfield Networks"]
    D --> E["Comparison: vs. LSTM & Transformers<br>Traditional Methods"]
    E --> F["Key Findings:<br>On Par or Better Performance"]
    E --> G["Key Findings:<br>Faster Training & Better Stability"]
    F --> H["Outcomes: Efficient, Scalable<br>Robust Asset Allocation Solution"]
    G --> H