Advanced Statistical Arbitrage with Reinforcement Learning

ArXiv ID: 2403.12180 “View on arXiv”

Authors: Unknown

Abstract

Statistical arbitrage is a prevalent trading strategy which takes advantage of mean reverse property of spread of paired stocks. Studies on this strategy often rely heavily on model assumption. In this study, we introduce an innovative model-free and reinforcement learning based framework for statistical arbitrage. For the construction of mean reversion spreads, we establish an empirical reversion time metric and optimize asset coefficients by minimizing this empirical mean reversion time. In the trading phase, we employ a reinforcement learning framework to identify the optimal mean reversion strategy. Diverging from traditional mean reversion strategies that primarily focus on price deviations from a long-term mean, our methodology creatively constructs the state space to encapsulate the recent trends in price movements. Additionally, the reward function is carefully tailored to reflect the unique characteristics of mean reversion trading.

Keywords: Statistical Arbitrage, Reinforcement Learning, Mean Reversion, Model-Free Framework, Pairs Trading, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 4.5/10
• Quadrant: Lab Rats
• Why: The paper employs advanced stochastic calculus (Ornstein-Uhlenbeck processes) and reinforcement learning algorithms, indicating high mathematical density. While it tests on real-world US stock data, the reliance on simulated data for algorithm validation and the focus on theoretical framework over detailed implementation specifics (like transaction costs) places it more in the research lab than ready for the trading desk.

  flowchart TD
    A["Research Goal: Model-Free<br>Statistical Arbitrage"] --> B["Inputs: Historical<br>Stock Price Data"]
    B --> C["Spread Construction:<br>Minimize Empirical Mean Reversion Time"]
    C --> D["Trading Framework:<br>Reinforcement Learning RL"]
    D --> E["State Space:<br>Recent Price Trends"]
    D --> F["Reward Function:<br>Tailored for Mean Reversion"]
    E & F --> G["Key Findings:<br>Optimal Trading Strategy &<br>Model-Free ARBITRAGE"]