Paired-Portfolio Trading: A Statistical Approach

Thomas M. Menino Convention & Exhibition Center 

Traditional pairs trading focuses on selecting a pair of individual stocks. However, in practice, it is difficult to identify pairs that may exhibit similar behavior over an extended period. In this work, we take a more drastic approach: paired-portfolio trading, where, using statistical techniques, we carefully construct paired portfolios. We do so by using multivariate techniques such as canonical correlations and cointegration. We apply and illustrate our approach to daily data on ten large-cap U.S. equities from January 2021 to December 2023. We construct a host of paired portfolios from these ten equities, which have a high possibility of cointegrating and thus offer profitable paired-portfolio trading. Our strategy is almost market-neutral, as the net investment in the market is minimal. We develop several entry and exit rules and refine our approach by using a variety of modelling techniques, such as orthogonal regression. We also evaluate the strategy for the future out-of-sample data from January 2024 through November 2025. Our analysis indicates that most of the selected paired portfolios remain profitable in future data, yielding positive returns, often substantial.

Canonical Correlation Analysis

Cointegration

Orthogonal Regression

Paired-Portfolio Trading 

Business and Economic Statistics Section