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

arXiv: https://arxiv.org/abs/2306.13661 · HTML: https://mlsanity.com/paper/2306.13661/

## 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 (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Auxiliary Tasks in Multi-task Learning](https://mlsanity.com/paper/1805.06334/) (2018)
- [Identifying beneficial task relations for multi-task learning in deep neural networks](https://mlsanity.com/paper/1702.08303/) (2017)
- [Tied Multitask Learning for Neural Speech Translation](https://mlsanity.com/paper/1802.06655/) (2018)
- [Enhancing Time Series Momentum Strategies Using Deep Neural Networks](https://mlsanity.com/paper/1904.04912/) (2019)
- [Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture](https://mlsanity.com/paper/2112.08534/) (2021)
- [PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin](https://mlsanity.com/paper/2206.00648/) (2022)
- [Deep Learning for Portfolio Optimization](https://mlsanity.com/paper/2005.13665/) (2020)

## Built on by (most cited first, 5 of 5)

- [Towards Unified Music Emotion Recognition across Dimensional and Categorical Models](https://mlsanity.com/paper/2502.03979/) (2025)
- [Gradient Similarity Surgery in Multi-Task Deep Learning](https://mlsanity.com/paper/2506.06130/) (2025)
- [Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices](https://mlsanity.com/paper/2309.15640/) (2023)
- [Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimization](https://mlsanity.com/paper/2509.10461/) (2025)
- [Self-Supervised Auxiliary Task Discovery for Stable Reinforcement Learning in Stock Trading](https://mlsanity.com/paper/2608.15841/) (2026)

## 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.