Pairs Trading: Performance of a Relative-Value Arbitrage Rule

Gatev, Evan; Goetzmann, William N.; Rouwenhorst, K. Geert

doi:10.1093/rfs/hhj020

Cited by 596 publications

(311 citation statements)

References 25 publications

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“…If two stock prices were truly random, pairs trading would not work well. The Law of One Price (LOP) states that two stocks with the same payoff in every state of nature must have the same current value (Caldeira & Moura, 2013;Gatev, Goetzmann, & Rouwenhorst, 2006). Thus, two stock prices spread between close substitute assets should have a stable, long-term equilibrium price over time.…”

Section: Introductionmentioning

confidence: 99%

High Frequency and Dynamic Pairs Trading Based on Statistical Arbitrage Using a Two-Stage Correlation and Cointegration Approach

Miao¹

2014

IJEF

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In this paper, a high frequency and dynamic pairs trading system is proposed, based on a market-neutral statistical arbitrage strategy using a two-stage correlation and cointegration approach. The proposed pairs trading system was applied to equity trading in U.S. equity markets in any type of market cycle condition to capture statistical mispricing between the prices of each stock pair based on its residuals and to model the stock pairs naturally as a mean-reversion process. The proposed pairs trading system was tested for out-of-sample testing periods with high frequency stock data from 2012 and 2013. Our trading strategy yields cumulative returns up to 56.58% for portfolios of stock pairs, well exceeding the S&P 500 index performance by 34.35% over a 12-month trading period. The proposed trading strategy achieved a monthly 2.67 Sharpe ratio and an annual 9.25 Sharpe ratio. Furthermore, the proposed pairs trading system performed well during the two months in which the S&P 500 index had negative returns. Thus, the trading system might be especially more profitable at times when the U.S. stock market performed poorly. Therefore, the performance returns of the proposed pairs trading system were relatively market-neutral and were positive regardless of the performance of the S&P 500 index.

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Section: Introductionmentioning

confidence: 99%

High Frequency and Dynamic Pairs Trading Based on Statistical Arbitrage Using a Two-Stage Correlation and Cointegration Approach

Miao¹

2014

IJEF

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“…We formulate this problem as a combination of finding an optimal threshold for each pair and deciding an optimal holding weight. This formulation extends the standard pairs trading strategy of Gatev et al [4] and is easily understood by investors.…”

Section: Introductionmentioning

confidence: 76%

“…Practitioners usually evaluate their strategy by return, risk, and cost. The empirical studies of Gatev et al [4], Adachi [1], and Hakamada [5] are also based on these components. On path s, the expected return, expected cost, and portfolio risk from t 1 to t K , respectively, are defined by…”

Section: Objective Functionmentioning

confidence: 99%

“…Moreover, a zero closing threshold was assumed in early empirical studies such as Gatev et al [4]. Therefore, we set …”

Section: Fix the Closing Thresholds To Zeromentioning

confidence: 99%

“…In empirical evaluation, Gatev et al [4] tested a traditional pairs trading strategy on real stock market data in the United States. They calculated the opening threshold as two times standard deviations from the mean using daily historical price data, and constructed a position when the spread diverges by more than the opening threshold.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Multiple Pairs Trading Strategyusing Derivative Free Optimizationunder Actual Investment Management Conditions

Yamamoto

Hibiki

2017

JORSJ

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Pairs trading strategy has a history of at least 30 years in the stock market and is one of the most common trading strategies used today due to its understandability. Recently, Yamamoto and Hibiki [13] studied optimal pairs trading strategy using a new approach under actual fund management conditions, such as transaction costs, discrete rebalance intervals, finite investment horizons and so on. However, this approach cannot solve the problem of multiple pairs because this problem is formulated as a large scale simulation based non-continuous optimization problem. In this research, we formulate a model to solve an optimal pairs trading strategy problem using multiple pairs under actual fund management conditions. Furthermore, we propose a heuristic algorithm based on a derivative free optimization (DFO) method for solving this problem efficiently.

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