Advanced Statistical Arbitrage with Reinforcement Learning (2403.12180v1)

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.

• The paper introduces a model-free RL framework that minimizes empirical mean reversion time to construct robust arbitrage portfolios.
• It employs dynamic state space designs and tailored reward functions to overcome the limitations of static, heuristic trading rules.
• Empirical tests on simulated and US stock market data reveal superior cumulative returns and improved risk-adjusted performance compared to traditional methods.

Advanced Statistical Arbitrage with Reinforcement Learning

The paper "Advanced Statistical Arbitrage with Reinforcement Learning" by Boming Ning and Kiseop Lee proposes a novel framework for statistical arbitrage that diverges from traditional model-dependent approaches. By leveraging reinforcement learning (RL) in conjunction with a model-free strategy, the authors advance the current methodologies in mean reversion trading. This work is structured to address the conventional constraints of statistical arbitrage, specifically the reliance on rigid model assumptions and pre-defined heuristic trading rules.

At the core of this research is a distinctive approach to constructing mean-reverting spreads using an empirical mean reversion time metric. This method optimizes asset coefficients by minimizing the empirical mean reversion time, which implies higher adaptability to real-world market conditions where traditional assumptions may not hold. The empirical mean reversion time serves as a practical measure of reversion speed, enhancing the robustness of arbitrage portfolio construction across various asset classes.

The paper introduces a comprehensive RL framework tailored for the trading phase. The RL model is designed to dynamically learn optimal trading strategies by incorporating recent price trends into its state space, thereby circumventing the need for static rules based on past historical averages. The reward function, vital to the RL process, is specifically crafted to reflect the characteristics of mean reversion trading, optimizing for maximum cumulative returns over the trading period. This adaptable RL approach to trading addresses the limitations associated with hyper-parameter selection in traditional strategies, such as thresholds for price deviations.

Empirical evaluations conducted on both simulated data and real-world US stock market trades reveal noteworthy outcomes. By establishing control experiments and contrasting with classical distance method and Ornstein-Uhlenbeck (OU) strategies, the proposed framework demonstrates superior results in terms of higher average profits and risk-adjusted returns. The RL-based strategy not only achieves higher cumulative returns but also exhibits robustness against market fluctuations observed during the experimental timeframe.

Several key performance metrics are highlighted, including average daily returns, daily Sharpe ratios, and maximum drawdowns, underscoring the efficacy of the RL approach. This comprehensive evaluation provides significant evidence supporting the feasibility of employing RL for sophisticated trading strategies in statistical arbitrage, promising a future trajectory for the development of more nuanced AI-driven financial models.

The implications of this research are multifaceted. Theoretically, it opens a new avenue for integrating advanced machine learning techniques into financial trading strategies, extending beyond mere predictive analytics into proactive, real-time decision making. Practically, it equips traders and financial institutions with a flexible, adaptive tool that optimizes trading performance in diverse, rapidly-changing market conditions. The potential for RL algorithms to consistently evolve through continuous learning and adaptation presents a transformative prospect for the future of statistical arbitrage.

As part of future research, the authors intend to explore more sophisticated RL algorithms, such as those leveraging deep reinforcement learning, to enhance the trading strategy's adaptability and optimization capabilities further. The exploration of alternate reward structures and additional market scenarios could further increase the robustness and generalizability of the RL strategies across a broader spectrum of financial instruments and market environments.