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On the Profitability of Optimal Mean Reversion Trading Strategies

18 Pages Posted: 22 Jan 2016 Last revised: 19 Feb 2016

Peng Huang

Columbia University, Fu Foundation School of Engineering and Applied Science, Department of Industrial Engineering and Operations Research (IEOR), Students

Tianxiang Wang

Columbia University, Fu Foundation School of Engineering and Applied Science, Department of Industrial Engineering and Operations Research (IEOR), Students

Date Written: January 20, 2016

Abstract

We study the profitability of optimal mean reversion trading strategies in the US equity market. Different from regular pair trading practice, we apply maximum likelihood method to construct the optimal static pairs trading portfolio that best fits the Ornstein-Uhlenbeck process, and rigorously estimate the parameters. Therefore, we ensure that our portfolios match the mean-reverting process before trading. We then generate contrarian trading signals using the model parameters. We also optimize the thresholds and the length of in-sample period by multiple tests. In nine good pair examples, we can see that our pairs exhibit high Sharpe ratio (above 1.9) over in-sample period and out-of-sample period. In particular, Crown Castle International Corp. (CCI) and HCP, Inc. (HCP) achieve a Sharpe ratio of 2.326 during in-sample test and a Sharpe ration of 2.425 in out-of-sample test. Crown Castle International Corp. CCI and (Realty Income Corporation) O achieve a Sharpe ratio of 2.405 and 2.903 separately during in-sample period and out-of-sample period.

Keywords: maximum likelihood estimation, Ornstein-Uhlenbeck process, mean-reversion trading

JEL Classification: C41, G11, G12

Suggested Citation

Huang, Peng and Wang, Tianxiang, On the Profitability of Optimal Mean Reversion Trading Strategies (January 20, 2016). Available at SSRN: https://ssrn.com/abstract=2719182 or http://dx.doi.org/10.2139/ssrn.2719182

Peng Huang (Contact Author)

Columbia University, Fu Foundation School of Engineering and Applied Science, Department of Industrial Engineering and Operations Research (IEOR), Students ( email )

New York, NY
United States

Tianxiang Wang

Columbia University, Fu Foundation School of Engineering and Applied Science, Department of Industrial Engineering and Operations Research (IEOR), Students ( email )

New York, NY
United States

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