mlsanityData to 14 Oct 2026

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

arXiv 2306.13661 ↗First posted 8 Jun 2023q-fin.CP cs.LG q-fin.PMcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 10 of its 61 references that are in the map. First posted on arXiv 8 Jun 2023.

Builds on

10 of 61 references.

References · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  3. 2017An Overview of Multi-Task Learning in Deep Neural Networkscited by 803
  4. 2018Auxiliary Tasks in Multi-task Learningcited by 108
  5. 2017Identifying beneficial task relations for multi-task learning in deep neural networkscited by 59
  6. 2018Tied Multitask Learning for Neural Speech Translationcited by 54
  7. 2019Enhancing Time Series Momentum Strategies Using Deep Neural Networkscited by 31
  8. 2021Trading with the Momentum Transformer: An Intelligent and Interpretable Architecturecited by 16
  9. 2022PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoincited by 7
  10. 2020Deep Learning for Portfolio Optimizationcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 3; 2026: 1.

5 papers.

  1. 2025Towards Unified Music Emotion Recognition across Dimensional and Categorical Modelscited by 7
  2. 2025Gradient Similarity Surgery in Multi-Task Deep Learningcited by 5
  3. 2023Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indicescited by 1
  4. 2025Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimizationcited by 0
  5. 2026Self-Supervised Auxiliary Task Discovery for Stable Reinforcement Learning in Stock Tradingcited by 0