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Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Market

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  • Jaydip Sen

Abstract

A pair-trading strategy is an approach that utilizes the fluctuations between prices of a pair of stocks in a short-term time frame, while in the long-term the pair may exhibit a strong association and co-movement pattern. When the prices of the stocks exhibit significant divergence, the shares of the stock that gains in price are sold (a short strategy) while the shares of the other stock whose price falls are bought (a long strategy). This paper presents a cointegration-based approach that identifies stocks listed in the five sectors of the National Stock Exchange (NSE) of India for designing efficient pair-trading portfolios. Based on the stock prices from Jan 1, 2018, to Dec 31, 2020, the cointegrated stocks are identified and the pairs are formed. The pair-trading portfolios are evaluated on their annual returns for the year 2021. The results show that the pairs of stocks from the auto and the realty sectors, in general, yielded the highest returns among the five sectors studied in the work. However, two among the five pairs from the information technology (IT) sector are found to have yielded negative returns.

Suggested Citation

  • Jaydip Sen, 2022. "Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Market," Papers 2211.07080, arXiv.org.
    • Handle: RePEc:arx:papers:2211.07080
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    References listed on IDEAS

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    1. Ananda Chatterjee & Hrisav Bhowmick & Jaydip Sen, 2021. "Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models," Papers 2111.01137, arXiv.org.
    2. Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021. "Stock Portfolio Optimization Using a Deep Learning LSTM Model," Papers 2111.04709, arXiv.org.
    3. Jacobs, Heiko, 2015. "What explains the dynamics of 100 anomalies?," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 65-85.
    4. Sidra Mehtab & Jaydip Sen, 2020. "Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models," Papers 2010.13891, arXiv.org.
    5. Sidra Mehtab & Jaydip Sen, 2019. "A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing," Papers 1912.07700, arXiv.org.
    6. Sidra Mehtab & Jaydip Sen, 2020. "A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models," Papers 2004.11697, arXiv.org, revised May 2021.
    7. Dong-Mei Zhu & Jia-Wen Gu & Feng-Hui Yu & Tak-Kuen Siu & Wai-Ki Ching, 2021. "Optimal pairs trading with dynamic mean-variance objective," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 94(1), pages 145-168, August.
    8. Jaydip Sen & Abhishek Dutta, 2022. "A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks," Papers 2210.00984, arXiv.org.
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    Cited by:

    1. Jaydip Sen & Subhasis Dasgupta, 2023. "Portfolio Optimization: A Comparative Study," Papers 2307.05048, arXiv.org.
    2. Jaydip Sen & Aditya Jaiswal & Anshuman Pathak & Atish Kumar Majee & Kushagra Kumar & Manas Kumar Sarkar & Soubhik Maji, 2023. "A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market," Papers 2305.17523, arXiv.org.

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