A Bayesian approach to generating distribution-based signals in pairs trading
提出一种贝叶斯方法，通过推导对冲比率的完整条件分布并利用其分位数作为交易信号确认阈值，在美巴市场41个资产对中提升了交易绩效和风险管理，同时将交易频率降低约24%。
This paper introduces a novel approach to improving the precision and adaptability of trading signals in pairs trading. Our method derives the full conditional distribution of the hedge ratio and utilizes its quantiles as confirmation thresholds for trading signals generated within the standard cointegration framework. We apply this approach to 41 selected asset pairs across the U.S. and Brazilian markets, evaluating its effectiveness through empirical analysis. Our findings indicate that the proposed Bayesian hierarchical model improves trading performance and risk management in the majority of analyzed pairs, with particularly strong results in dual-class share configurations. The method achieves these improvements while reducing trading frequency by approximately 24%, which implies less exposure to transaction costs in practical implementations. By adopting a distribution-based framework, our approach not only enables more timely and adaptive trading signals but also enhances pair selection by effectively filtering out false positives in cointegration tests, as demonstrated through simulations.