# Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market

arXiv: https://arxiv.org/abs/2406.10695 · HTML: https://mlsanity.com/paper/2406.10695/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 15 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [Classification-based Financial Markets Prediction using Deep Neural Networks](https://mlsanity.com/paper/1603.08604/) (2016)
- [SPONGE: A generalized eigenproblem for clustering signed networks](https://mlsanity.com/paper/1904.08575/) (2019)
- [Supervised Autoencoder MLP for Financial Time Series Forecasting](https://mlsanity.com/paper/2404.01866/) (2024)

## Built on by (most cited first, 2 of 2)

- [Toward Quantum Utility in Finance: A Robust Data-Driven Algorithm for Asset Clustering](https://mlsanity.com/paper/2509.07766/) (2025)
- [Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning](https://mlsanity.com/paper/2606.04574/) (2026)

## Abstract (arXiv)

The study seeks to develop an effective strategy based on the novel framework of statistical arbitrage based on graph clustering algorithms. Amalgamation of quantitative and machine learning methods, including the Kelly criterion, and an ensemble of machine learning classifiers have been used to improve risk-adjusted returns and increase immunity to transaction costs over existing approaches. The study seeks to provide an integrated approach to optimal signal detection and risk management. As a part of this approach, innovative ways of optimizing take profit and stop loss functions for daily frequency trading strategies have been proposed and tested. All of the tested approaches outperformed appropriate benchmarks. The best combinations of the techniques and parameters demonstrated significantly better performance metrics than the relevant benchmarks. The results have been obtained under the assumption of realistic transaction costs, but are sensitive to changes in some key parameters.