# Statistical Arbitrage in Rank Space

arXiv: https://arxiv.org/abs/2410.06568 · HTML: https://mlsanity.com/paper/2410.06568/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Deep Learning Statistical Arbitrage](https://mlsanity.com/paper/2106.04028/) (2021)
- [Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization](https://mlsanity.com/paper/2403.16667/) (2024)

## Abstract (arXiv)

Equity market dynamics are conventionally investigated in name space, where stocks are indexed by company names. However, this perspective often suffers from high volatility and a low signal-to-noise ratio, which poses challenges for effective learning by deep neural networks (DNNs). In contrast, by indexing stocks by their ranks in capitalization, we gain a distinct and more structured view of market behavior in rank space. In this work, we demonstrate that DNNs achieve superior performance in statistical arbitrage when operating in rank space compared to name space. This performance gain is driven by more robust market representations and enhanced mean-reverting properties of residual returns in rank space, which facilitate more efficient learning. Our findings highlight the critical role of domain-informed data transformation in improving deep learning performance in noisy financial environments.