Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning

ArXiv ID: 2306.13661 “View on arXiv”

Authors: Unknown

Abstract

A diversified risk-adjusted time-series momentum (TSMOM) portfolio can deliver substantial abnormal returns and offer some degree of tail risk protection during extreme market events. The performance of existing TSMOM strategies, however, relies not only on the quality of the momentum signal but also on the efficacy of the volatility estimator. Yet many of the existing studies have always considered these two factors to be independent. Inspired by recent progress in Multi-Task Learning (MTL), we present a new approach using MTL in a deep neural network architecture that jointly learns portfolio construction and various auxiliary tasks related to volatility, such as forecasting realized volatility as measured by different volatility estimators. Through backtesting from January 2000 to December 2020 on a diversified portfolio of continuous futures contracts, we demonstrate that even after accounting for transaction costs of up to 3 basis points, our approach outperforms existing TSMOM strategies. Moreover, experiments confirm that adding auxiliary tasks indeed boosts the portfolio’s performance. These findings demonstrate that MTL can be a powerful tool in finance.

Keywords: Time-Series Momentum (TSMOM), Multi-Task Learning (MTL), Deep Neural Networks, Volatility Forecasting, Futures, Futures

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts such as deep neural networks with LSTM modules, multi-task learning frameworks, and volatility scaling equations, indicating high mathematical complexity. It also demonstrates strong empirical rigor by conducting extensive backtests from 2000 to 2020 on futures contracts, accounting for transaction costs, and benchmarking against existing strategies.

  flowchart TD
    A["Research Goal<br>Construct robust TSMOM<br>using Deep Multi-Task Learning"] --> B["Methodology<br>Deep Neural Network with<br>Joint Multi-Task Learning"]
    
    B --> C["Input Data<br>Continuous Futures Contracts<br>Jan 2000 - Dec 2020"]
    
    C --> D["Computational Process<br>Simultaneously learn:<br>1. Portfolio Construction<br>2. Auxiliary Volatility Tasks"]
    
    D --> E{"Performance Evaluation<br>Transaction Costs up to 3 bps"}
    
    E -- Comparison --> F["Key Findings/Outcomes"]
    
    F --> G["Superior Performance<br>Outperforms existing TSMOM strategies"]
    F --> H["Robustness<br>Effective even with transaction costs"]
    F --> I["MTL Efficacy<br>Auxiliary tasks boost portfolio performance"]
    F --> J["Risk Protection<br>Offers tail risk protection<br>during extreme events"]