Intraday pairs trading strategies on high frequency data: the case of oil companies |
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| Authors: | Bo Liu Hélyette Geman |
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| Affiliation: | 1. Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 North Charles St, Baltimore, MD, 21218USA.;2. Department of Economics, Mathematics and Statistics, Birkbeck College, University of London, Malet St, London, WC1E 7HXUK. |
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| Abstract: | This paper introduces novel ‘doubly mean-reverting’ processes based on conditional modelling of model spreads between pairs of stocks. Intraday trading strategies using high frequency data are proposed based on the model. This model framework and the strategies are designed to capture ‘local’ market inefficiencies that are elusive for traditional pairs trading strategies with daily data. Results from real data back-testing for two periods show remarkable returns, even accounting for transaction costs, with annualized Sharpe ratios of 3.9 and 7.2 over the periods June 2013–April 2015 and 2008, respectively. By choosing the particular sector of oil companies, we also confirm the observation that the commodity price is the main driver of the share prices of commodity-producing companies at times of spikes in the related commodity market. |
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| Keywords: | Pairs trading Quantitative trading strategies Conditional modelling Doubly mean-reverting model High frequency data Transaction costs |
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