# Enhancing Time Series Momentum Strategies Using Deep Neural Networks

arXiv: https://arxiv.org/abs/1904.04912 · HTML: https://mlsanity.com/paper/1904.04912/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2019.

## Built on by (most cited first, 30 of 31)

- [Reinforcement Learning for Quantitative Trading](https://mlsanity.com/paper/2109.13851/) (2021)
- [Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review](https://mlsanity.com/paper/2106.00123/) (2021)
- [Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture](https://mlsanity.com/paper/2112.08534/) (2021)
- [Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection](https://mlsanity.com/paper/2105.13727/) (2021)
- [A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks](https://mlsanity.com/paper/2212.02721/) (2022)
- [Deep Learning for Market by Order Data](https://mlsanity.com/paper/2102.08811/) (2021)
- [Realised Volatility Forecasting: Machine Learning via Financial Word Embedding](https://mlsanity.com/paper/2108.00480/) (2021)
- [Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling](https://mlsanity.com/paper/2107.11972/) (2021)
- [Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies](https://mlsanity.com/paper/2302.10175/) (2023)
- [Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning](https://mlsanity.com/paper/2306.13661/) (2023)
- [Applying Informer for Option Pricing: A Transformer-Based Approach](https://mlsanity.com/paper/2506.05565/) (2025)
- [Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies](https://mlsanity.com/paper/2310.10500/) (2023)
- [Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization](https://mlsanity.com/paper/2403.07916/) (2024)
- [Feature Selection for Multivariate Time Series via Network Pruning](https://mlsanity.com/paper/2102.06024/) (2021)
- [Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction](https://mlsanity.com/paper/2106.04114/) (2021)
- [Deep Learning for Options Trading: An End-To-End Approach](https://mlsanity.com/paper/2407.21791/) (2024)
- [Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity](https://mlsanity.com/paper/2208.09968/) (2022)
- [The cross-sectional stock return predictions via quantum neural network and tensor network](https://mlsanity.com/paper/2304.12501/) (2023)
- [Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models](https://mlsanity.com/paper/2305.06704/) (2023)
- [Network Momentum across Asset Classes](https://mlsanity.com/paper/2308.11294/) (2023)
- [Conservative Predictions on Noisy Financial Data](https://mlsanity.com/paper/2310.11815/) (2023)
- [Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management](https://mlsanity.com/paper/2407.13751/) (2024)
- [A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks](https://mlsanity.com/paper/2107.14695/) (2021)
- [Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training](https://mlsanity.com/paper/2304.11043/) (2023)
- [Learning to Learn Financial Networks for Optimising Momentum Strategies](https://mlsanity.com/paper/2308.12212/) (2023)
- [DeepAries: Adaptive Rebalancing Interval Selection for Enhanced Portfolio Selection](https://mlsanity.com/paper/2510.14985/) (2025)
- [Hierarchical AI Multi-Agent Fundamental Investing: Evidence from China's A-Share Market](https://mlsanity.com/paper/2510.21147/) (2025)
- [DeePM: Regime-Robust Deep Learning for Systematic Macro Portfolio Management](https://mlsanity.com/paper/2601.05975/) (2026)
- [Enhancing Portfolio Optimization with Deep Learning Insights](https://mlsanity.com/paper/2601.07942/) (2026)
- [Deep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performance](https://mlsanity.com/paper/2603.01820/) (2026)

## Abstract (arXiv)

While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule. In this paper, we introduce Deep Momentum Networks -- a hybrid approach which injects deep learning based trading rules into the volatility scaling framework of time series momentum. The model also simultaneously learns both trend estimation and position sizing in a data-driven manner, with networks directly trained by optimising the Sharpe ratio of the signal. Backtesting on a portfolio of 88 continuous futures contracts, we demonstrate that the Sharpe-optimised LSTM improved traditional methods by more than two times in the absence of transactions costs, and continue outperforming when considering transaction costs up to 2-3 basis points. To account for more illiquid assets, we also propose a turnover regularisation term which trains the network to factor in costs at run-time.