# Deep Learning Statistical Arbitrage

arXiv: https://arxiv.org/abs/2106.04028 · HTML: https://mlsanity.com/paper/2106.04028/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 64 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [Multi-Period Trading via Convex Optimization](https://mlsanity.com/paper/1705.00109/) (2017)
- [Interpretable Sparse Proximate Factors for Large Dimensions](https://mlsanity.com/paper/1805.03373/) (2018)

## Built on by (most cited first, 13 of 13)

- [Data-Driven Merton's Strategies via Policy Randomization](https://mlsanity.com/paper/2312.11797/) (2023)
- [Time-Series Foundation AI Model for Value-at-Risk Forecasting](https://mlsanity.com/paper/2410.11773/) (2024)
- [Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves](https://mlsanity.com/paper/2301.00092/) (2022)
- [A Set-Sequence Model for Time Series](https://mlsanity.com/paper/2505.11243/) (2025)
- [High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods](https://mlsanity.com/paper/2308.16192/) (2023)
- [Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimization](https://mlsanity.com/paper/2402.08108/) (2024)
- [End-to-End Policy Learning of a Statistical Arbitrage Autoencoder Architecture](https://mlsanity.com/paper/2402.08233/) (2024)
- [Statistical Arbitrage in Rank Space](https://mlsanity.com/paper/2410.06568/) (2024)
- [Ordinary Least Squares as an Attention Mechanism](https://mlsanity.com/paper/2504.09663/) (2025)
- [Attention Factors for Statistical Arbitrage](https://mlsanity.com/paper/2510.11616/) (2025)
- [Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning](https://mlsanity.com/paper/2606.04574/) (2026)
- [Hierarchical Graph Learning for Calendar Spread Strategies in Commodity Futures Markets](https://mlsanity.com/paper/2606.25811/) (2026)
- [STOCK-JEPA: Prior-Anchored Latent Revision Representation Learning in Equity Markets](https://mlsanity.com/paper/2610.07006/) (2026)

## Abstract (arXiv)

Statistical arbitrage exploits temporal price differences between similar assets. We develop a unifying conceptual framework for statistical arbitrage and a novel data driven solution. First, we construct arbitrage portfolios of similar assets as residual portfolios from conditional latent asset pricing factors. Second, we extract their time series signals with a powerful machine-learning time-series solution, a convolutional transformer. Lastly, we use these signals to form an optimal trading policy, that maximizes risk-adjusted returns under constraints. Our comprehensive empirical study on daily US equities shows a high compensation for arbitrageurs to enforce the law of one price. Our arbitrage strategies obtain consistently high out-of-sample mean returns and Sharpe ratios, and substantially outperform all benchmark approaches.