Statistical arbitrage in the US equities market

By Marco Avellaneda, Jeong-hyun Lee. 2008

335 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/c841c6a9cc9846e767c951f51430064ec1bbc46b

How researchers see this paper

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

In Economics and Econometrics

1. Christopher Krauss
2. Xuan Anh Do
3. Bo Liu
4. Helyette Geman
5. Lo-Bin Chang
6. Nicolas Huck
7. Omer Berat Sezer
8. A. Ozbayoglu

In Finance

1. Christopher Krauss
2. Xuan Anh Do
3. Bo Liu
4. Helyette Geman
5. Lo-Bin Chang
6. Nicolas Huck
7. A. Ozbayoglu
8. Omer Berat Sezer

In Management Science and Operations Research

1. Christopher Krauss
2. Xuan Anh Do
3. Bo Liu
4. Helyette Geman
5. Lo-Bin Chang
6. Nicolas Huck
7. Omer Berat Sezer
8. A. Ozbayoglu

Citations in the corpus

• Intraday pairs trading strategies on high frequency data: the case of oil companies (2017, methodology): “Even if we relax the estimate to a more conservative 20 bps per trade, we still have a 10 bps per trade profit and a 2.338 annualized Sharpe ratio, compared with the Sharpe ratio of 1.51 from 2003 to 2007 by Avellaneda and Lee (2010).”
• Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit (2014, neutral): “vels. For example, Gatev et al. (2006) study the historical returns from the buy-low-sell-high strategy where the entry/exit levels are set as ±1 standard deviation from the long-run mean. Similarly, Avellaneda and Lee (2010) consider starting and ending a pairs trade based on the spread’s distance ”
• Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 (2017, methodology): “We depict results prior to and after incorporating transaction costs of 0.05 percent per share per half-turn, following Avellaneda and Lee (2010).”