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

TL;DR

This paper introduces a novel multi-pair trading strategy in US equities using graph clustering algorithms combined with machine learning to enhance risk-adjusted returns and transaction cost immunity.

Contribution

It presents an integrated framework for signal detection and risk management, optimizing take profit and stop loss functions for daily trading strategies.

Findings

All tested approaches outperformed benchmarks.

Best techniques showed significantly better performance metrics.

Results are sensitive to key parameter changes.

Abstract

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.