DeepUnifiedMom: Unified Time-series Momentum Portfolio Construction via Multi-Task Learning with Multi-Gate Mixture of Experts

ArXiv ID: 2406.08742 “View on arXiv”

Authors: Unknown

Abstract

This paper introduces DeepUnifiedMom, a deep learning framework that enhances portfolio management through a multi-task learning approach and a multi-gate mixture of experts. The essence of DeepUnifiedMom lies in its ability to create unified momentum portfolios that incorporate the dynamics of time series momentum across a spectrum of time frames, a feature often missing in traditional momentum strategies. Our comprehensive backtesting, encompassing diverse asset classes such as equity indexes, fixed income, foreign exchange, and commodities, demonstrates that DeepUnifiedMom consistently outperforms benchmark models, even after factoring in transaction costs. This superior performance underscores DeepUnifiedMom’s capability to capture the full spectrum of momentum opportunities within financial markets. The findings highlight DeepUnifiedMom as an effective tool for practitioners looking to exploit the entire range of momentum opportunities. It offers a compelling solution for improving risk-adjusted returns and is a valuable strategy for navigating the complexities of portfolio management.

Keywords: Momentum Strategy, Multi-task Learning, Mixture of Experts, Deep Learning, Portfolio Management

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning architectures (multi-task learning with multi-gate mixture of experts) involving complex formulations, while demonstrating rigorous empirical validation with comprehensive backtests across multiple asset classes including transaction costs.

  flowchart TD
    A["Research Goal:<br>Create unified momentum portfolio<br>via Multi-task Learning & MoE"] --> B["Data & Inputs<br>Asset Classes: Equity, FX, Bonds, Commodities"]
    B --> C["Core Methodology<br>DeepUnifiedMom Framework"]
    C --> D["Multi-Task Learning<br>Long/Short signals across time horizons"]
    C --> E["Multi-Gate Mixture of Experts<br>Specialized expert networks"]
    D & E --> F["Computational Process<br>Unified Portfolio Construction"]
    F --> G["Backtesting & Evaluation<br>Transaction costs applied"]
    G --> H["Key Outcomes<br>Consistent outperformance<br>Superior risk-adjusted returns"]