Selecting Cointegrated Pairs with Maximum Weighted Matching
Summary
The document presents a portfolio construction method for pairs trading that limits overlap between selected pairs. It represents assets as graph nodes and cointegrated candidate pairs as weighted edges, with edge weights reflecting the strength of cointegration. The portfolio is chosen as a maximum weighted matching, which selects strong pairs while ensuring that each asset appears in only one pair. This addresses a drawback of selecting pairs solely by cointegration, where several pairs may reuse the same assets and concentrate risk.
The authors report theoretical analysis and an empirical study using S&P 500 data from 2017 to 2023. The matching portfolio had lower variance than the cointegration-only baseline and a gross Sharpe ratio of 1.23, compared with 0.48 for the baseline and 0.59 for the market. It also showed lower turnover-related trading costs and less single-asset risk, according to the document. The summary does not provide net performance, transaction cost assumptions, or details needed to judge sensitivity to the sample and pair selection choices.
Key ideas
- The method models assets as graph nodes and cointegrated pairs as weighted edges.
- Maximum weighted matching selects strong pairs while preventing any asset from appearing in multiple pairs.
- The authors report lower portfolio variance than a baseline that selects pairs only by cointegration.
- In the stated S&P 500 study, the matching strategy had a gross Sharpe ratio of 1.23 and lower turnover-related costs.
Tags
Full text
# Pairs Trading Using a Novel Graphical Matching Approach # Pairs Trading Using a Novel Graphical Matching Approach Pairs trading, a strategy that capitalizes on price movements of asset pairs driven by similar factors, has gained significant popularity among traders. Common practice involves selecting highly cointegrated pairs to form a portfolio, which often leads to the inclusion of multiple pairs sharing common assets. This approach, while intuitive, inadvertently elevates portfolio variance and diminishes risk-adjusted returns by concentrating on a small number of highly cointegrated assets. Our study introduces an innovative pair selection method employing graphical matchings designed to tackle this challenge. We model all assets and their cointegration levels with a weighted graph, where edges signify pairs and their weights indicate the extent of cointegration. A portfolio of pairs is a subgraph of this graph. We construct a portfolio which is a maximum weighted matching of this graph to select pairs which have strong cointegration while simultaneously ensuring that there are no shared assets within any pair of pairs. This approach ensures each asset is included in just one pair, leading to a significantly lower variance in the matching-based portfolio compared to a baseline approach that selects pairs purely based on cointegration. Theoretical analysis and empirical testing using data from the S\&P 500 between 2017 and 2023, affirm the efficacy of our method. Notably, our matching-based strategy showcases a marked improvement in risk-adjusted performance, evidenced by a gross Sharpe ratio of 1.23, a significant enhancement over the baseline value of 0.48 and market value of 0.59. Additionally, our approach demonstrates reduced trading costs attributable to lower turnover, alongside minimized single asset risk due to a more diversified asset base.
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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.