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

ArXiv ID: 2406.10695 “View on arXiv”

Authors: Unknown

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.

Keywords: Statistical Arbitrage, Graph Clustering, Kelly Criterion, Portfolio Optimization, Machine Learning Classifiers

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts like graph clustering algorithms, correlation matrix manipulation, and the Kelly criterion, while providing extensive backtesting on 23 years of S&P500 data with rigorous parameter optimization, cross-validation, and sensitivity analysis.

  flowchart TD
    A["Research Goal:<br>Develop Effective Statistical<br>Arbitrage Strategy"] --> B["Data & Inputs:<br>US Equities Market Data,<br>Realistic Transaction Costs"]
    B --> C["Key Methodology:<br>Graph Clustering<br>for Pair Selection"]
    C --> D["Computational Process:<br>Ensemble ML Classifiers<br>+ Kelly Criterion"]
    D --> E["Optimization:<br>Dynamic Take Profit<br>& Stop Loss Functions"]
    E --> F["Key Outcomes:<br>Outperformed Benchmarks<br>Risk-Adjusted Returns<br>Realistic Cost Sensitivity"]