Does Simple Pairs Trading Still Work?

By B. Do, R. Faff. 2010

252 citations recorded by the source corpus.

Subfields: Accounting, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5710c989268df3d7ae646c9a270647e8e4508a49

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Rong Qi Liew
2. Christopher Krauss
3. Bo Liu
4. Helyette Geman
5. Lo-Bin Chang
6. Nicolas Huck
7. Yuan Wu

In Finance

1. Christopher Krauss
2. Xuan Anh Do
3. Rong Qi Liew
4. Bo Liu
5. Helyette Geman
6. Lo-Bin Chang
7. Nicolas Huck
8. Yuan Wu

In Management Science and Operations Research

1. Christopher Krauss
2. Xuan Anh Do
3. Rong Qi Liew
4. Szu-Hao Huang
5. Bo Liu
6. Helyette Geman
7. Lo-Bin Chang
8. Nicolas Huck

Citations in the corpus

• Intraday pairs trading strategies on high frequency data: the case of oil companies (2017, background): “As a contrarian strategy, pairs trading tends to perform better during markets downturns (Do and Faff, 2010).”
• Pairs trading in the UK equity market: risk and return (2016, unknown): “Do and Faff (2010) extend the work of Gatev et al. (2006) for the financial crisis period and find that the strategy performs particularly well during the crisis.”
• Pairs trading: A copula approach (2013, methodology): “(Vidyamurthy, 2004) Recent researchers (Gatev et al, 2006; Do and Faff, 2010) have extended the initial analysis of pairs trading to more updated samples, and documented economically and statistically significant profits using simple pairs trading rule.”
• The profitability of pairs trading strategies: distance, cointegration and copula methods (2016, unknown): “We use a time-varying dataset of transaction costs in line with Do and Faff (2012). The motivation behind this is that commissions are the first element of transaction costs to be considered.”
• Statistical arbitrage pairs trading strategies: Review and outlook (2017, unknown): “These unexplained excess returns elevate GGR’s pairs trading to one of the few capital market phenomena1 that stood the test of time2 as well as independent scrutiny by later authors, most notably Do and Faff (2010, 2012). Despite these findings, we have to recognize that academic research about pai”
• Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 (2017, result): “This is in line with Do and Faff (2010); Bogomolov (2013); Huck (2015), showing that liquidity-providing pairs trading strategies fare well in light of the financial crisis.”
• Developing Arbitrage Strategy in High-frequency Pairs Trading with Filterbank CNN Algorithm (2018, background): “In [6], pairs trading strategy is proved to have great performance when the market is bumpy.”