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Selection of a Portfolio of Pairs Based on Cointegration: A Statistical Arbitrage Strategy

Last Updated on 10 February, 2024 by Rejaul Karim

João Caldeira and Guilherme V. Moura present a comprehensive exploration titled “Selection of a Portfolio of Pairs Based on Cointegration: A Statistical Arbitrage Strategy.” Published in 2013, their study navigates the realm of statistical arbitrage, with a specific focus on pairs trading and its extensions.

Employing cointegration tests as the linchpin, the authors aim to discern stocks suitable for constructing mean-reverting spreads with predictability. Going beyond mere identification, the paper delves into estimating long-term equilibrium and modeling resulting residuals. The essence lies in strategically selecting stock pairs for a trading portfolio, leveraging an in-sample profitability indicator.

The empirical analysis, conducted using São Paulo stock exchange data spanning 2005 to 2012, unfolds a compelling narrative of the strategy’s prowess—exhibiting an impressive 16.38% annual excess return, a Sharpe Ratio of 1.34, and commendable market correlation resilience.

Abstract Of Paper

Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean- reverting spreads with a certain degree of predictability. This paper applies cointegration tests to identify stocks to be used in pairs trading strategies. In addition to estimating long-term equilibrium and to model the resulting residuals, we select stock pairs to compose a pairs trading portfolio based on an indicator of profitability evaluated in-sample. The profitability of the strategy is assessed with data from the São Paulo stock exchange ranging from January 2005 to October 2012. Empirical analysis shows that the proposed strategy exhibit excess returns of 16.38% per year, Sharpe Ratio of 1.34 and low correlation with the market.

Original paper – Download PDF

Here you can download the PDF and original paper of Selection of a Portfolio of Pairs Based on Cointegration: A Statistical Arbitrage Strategy.

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Author

João Caldeira
Universidade Federal de Santa Catarina & CNPq

Guilherme V. Moura
Universidade Federal de Santa Catarina (UFSC) – Department of Economics

Conclusion

In conclusion, our exploration of statistical arbitrage strategies, with a focus on pairs trading, leverages cointegration tests to identify stocks, emphasizing mean-reverting spreads with predictive qualities. By employing these tests, we not only estimate long-term equilibrium but also model residuals, facilitating the selection of stock pairs for an optimal pairs trading portfolio.

Through rigorous in-sample evaluations, our strategy demonstrates robust profitability. Applied to São Paulo stock exchange data from January 2005 to October 2012, our approach yields compelling results—excess returns of 16.38% annually, a Sharpe Ratio of 1.34, and notably low correlation with the broader market.

This study contributes to refining statistical arbitrage methodologies, offering a systematic framework for pairs selection that proves lucrative and resilient in real-world market conditions.

Related Reading:

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The Asset Growth Effect in Stock Returns

FAQ

Q1: What is the central focus of the paper “Selection of a Portfolio of Pairs Based on Cointegration: A Statistical Arbitrage Strategy”?
A1: The paper primarily focuses on statistical arbitrage strategies, specifically pairs trading and its generalizations. The authors employ cointegration tests to identify stocks suitable for constructing mean-reverting spreads with predictability. The study goes beyond mere identification, delving into estimating long-term equilibrium and modeling resulting residuals to strategically select stock pairs for a trading portfolio.

Q2: What empirical results does the study reveal regarding the proposed pairs trading strategy?
A2: The empirical analysis, conducted using São Paulo stock exchange data spanning 2005 to 2012, showcases the prowess of the strategy. It exhibits an impressive 16.38% annual excess return, a Sharpe Ratio of 1.34, and commendable market correlation resilience. The in-sample evaluations highlight the robust profitability of the pairs trading portfolio constructed based on cointegration tests.

Q3: How does the paper contribute to refining statistical arbitrage methodologies?
A3: The study contributes to refining statistical arbitrage methodologies by providing a systematic framework for pairs selection. Leveraging cointegration tests, the authors offer insights into constructing mean-reverting spreads with predictive qualities. The proposed strategy, evaluated in real-world market conditions, demonstrates both lucrative returns and resilience, enhancing the understanding of statistical arbitrage in the realm of pairs trading.

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