Pairs Trading Strategy with Geolocation Data: The Battle Between Under Armour and Nike

47 Pages Posted: 19 Aug 2019

See all articles by Jim Kyung-Soo Liew

Jim Kyung-Soo Liew

Johns Hopkins University - Carey Business School

Tamás Budavári

Johns Hopkins University - Department of Applied Mathematics and Statistics

Zixiao Kang

Johns Hopkins University

Fengxu Li

Johns Hopkins University - Carey Business School

Xuzhi Wang

Johns Hopkins University

Brandon Fremin

Johns Hopkins University

Date Written: July 22, 2019

Abstract

We investigate the fundamental linkages between “geolocation” (human movement) data and financial market’s equity price behavior. The geolocation positions were recorded intraday and cover the period from January 1, 2018 to July 17, 2018. Our initial data set spans over 54 billion observations and was provided by Fysical. We focus our study on a popular hedge fund trading strategy known as “pairs trading.” First, we collect Under Armour (UA) and Nike’s stock price and volume data. Second, we investigate the relative activity of people visiting a particular physical store of UA and Nike, as proxied from anonymous cell phone foot traffic. Third, we collect the relative sentiment for tweets to UA and Nike. After combining all the data, we glean the following fascinating results: (1) geolocation information is an important variable in pairs trading strategy between UA and Nike, as evidenced by the results from our feature selection popularity methodology; (2) surprisingly, pairs trading only using geolocation data yields positive returns, and employing machine learning methods and rolling analysis enhances the returns; and (3) pairs trading strategy incorporating geolocation information yields a cumulative return of 13.7% from January, 2018 to June, 2018, with an Annualized Sharpe Ratio of 3.9. Some caveats are in order – our results are very sensitive to transaction costs assumptions and our data set has serious limitations.

Keywords: pairs trading, geolocation data, machine learning, tweet sentiments, alternative data, artificial intelligence, intraday trading

JEL Classification: G1, G12, G14

Suggested Citation

Liew, Jim Kyung-Soo and Budavari, Tamas and Kang, Zixiao and Li, Fengxu and Wang, Xuzhi and Fremin, Brandon, Pairs Trading Strategy with Geolocation Data: The Battle Between Under Armour and Nike (July 22, 2019). Available at SSRN: https://ssrn.com/abstract=3436840 or http://dx.doi.org/10.2139/ssrn.3436840

Jim Kyung-Soo Liew (Contact Author)

Johns Hopkins University - Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States

Tamas Budavari

Johns Hopkins University - Department of Applied Mathematics and Statistics ( email )

3400 N Charles Street
Whitehead 100
Baltimore, MD 21218
United States

Zixiao Kang

Johns Hopkins University

Baltimore, MD 20036-1984
United States

Fengxu Li

Johns Hopkins University - Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States

Xuzhi Wang

Johns Hopkins University

Baltimore, MD 20036-1984
United States

Brandon Fremin

Johns Hopkins University ( email )

Baltimore, MD 20036-1984
United States

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