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The Journal of Financial Data Science

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Pairs Trading Strategy with Geolocation Data—The Battle between Under Armour and Nike

Jim Kyung-Soo Liew, Tamas Budavari, Zixiao Kang, Fengxu Li, Xuzhi Wang, Shihao Ma and Brandon Fremin
The Journal of Financial Data Science Winter 2020, 2 (1) 126-143; DOI: https://doi.org/10.3905/jfds.2019.1.024
Jim Kyung-Soo Liew
is the co-founder of SoKat.co and an associate professor in finance at the Johns Hopkins Carey Business School in Baltimore, MD;
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Tamas Budavari
is an associate professor at the Johns Hopkins University in Baltimore, MD
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Zixiao Kang
is an MS graduate of the Johns Hopkins Carey Business School in Baltimore, MD, and a data scientist at NetEase Game
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Fengxu Li
is an MS in finance at the Johns Hopkins Carey Business School in Baltimore, MD, and a data engineer at SoKat.co
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Xuzhi Wang
is an MS at the Johns Hopkins Whiting School of Engineering in Baltimore, MD, and a PhD candidate in biostatistics at Boston University
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Shihao Ma
is an MS in finance at the Johns Hopkins Carey Business School in Baltimore, MD
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Brandon Fremin
is an undergraduate student at Johns Hopkins University in Baltimore, MD
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Abstract

In this article, the authors examine fundamental linkages between geolocation (human movement) data and financial market equity price behavior. The geolocation positions were recorded intraday and cover the period from January 1, 2018 to July 17, 2018. The authors concentrate their study on a popular hedge fund trading strategy known as pairs trading. First, the authors collect data regarding Under Armour (UA) and Nike’s stock market price and volume. Second, they investigate the volume of people visiting each physical store of UA and Nike, as proxied from anonymous cell phone geolocation traffic. Third, they monitor the relative activities for tweets related to UA and Nike. Fourth, after combining all the data, the authors glean the following fascinating results: (1) Geolocation information is proven to be an important factor in a pairs trading strategy between UA and Nike, according to the results of feature selection and prediction accuracy for price ratio change; (2) both ensemble methods of machine learning and rolling analysis could significantly raise the prediction accuracy; and (3) a pairs trading strategy incorporating geolocation information can have a cumulative return of 13.72% from January 2018 to June 2018, with an annualized Sharpe ratio of 3.88.

TOPICS: Statistical methods, simulations, big data/machine learning

Key Findings

  • • Geolocation data can encapsulate the relative influx and outflux of consumers from department stores. This is an extremely useful feature in training machine learning models that can be used in lucrative pairs-trading strategies.

  • • In the era of big data, it is tempting to throw every feature into a model and assume that the model will always figure out which features are most important. However, the model will usually perform better when trained on a subset of distinct features.

  • • Ensemble methods are an effective means of eliminating bias in machine learning models because they use the output from a series of independently trained submodels to generate one balanced result.

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The Journal of Financial Data Science: 2 (1)
The Journal of Financial Data Science
Vol. 2, Issue 1
Winter 2020
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Pairs Trading Strategy with Geolocation Data—The Battle between Under Armour and Nike
Jim Kyung-Soo Liew, Tamas Budavari, Zixiao Kang, Fengxu Li, Xuzhi Wang, Shihao Ma, Brandon Fremin
The Journal of Financial Data Science Jan 2020, 2 (1) 126-143; DOI: 10.3905/jfds.2019.1.024

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Pairs Trading Strategy with Geolocation Data—The Battle between Under Armour and Nike
Jim Kyung-Soo Liew, Tamas Budavari, Zixiao Kang, Fengxu Li, Xuzhi Wang, Shihao Ma, Brandon Fremin
The Journal of Financial Data Science Jan 2020, 2 (1) 126-143; DOI: 10.3905/jfds.2019.1.024
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  • Article
    • Abstract
    • ARTIFICIAL INTELLIGENCE
    • PAIRS TRADING
    • OUR RESEARCH RESULTS
    • LITERATURE REVIEW
    • DATA AND PRELIMINARY ANALYSIS
    • METHODOLOGY
    • PAIRS TRADING BASED ON GEOLOCATION
    • CONCLUSION
    • ACKNOWLEDGMENTS
    • ADDITIONAL READING
    • REFERENCES
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