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While many methods weaken over time, favorable pairs trading options remain 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is the paper’s main focus?","Question",{"text":62,"@type":63},"The paper focuses on pairs trading for statistical arbitrage using high-frequency intraday data, including an overview of methods and strategy testing.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"Which market data and time span are used in the study?",{"text":67,"@type":63},"The strategies are tested on minute-by-minute prices of S&P 500 constituents from 1998 to 2015.",{"name":69,"@type":60,"acceptedAnswer":70},"What performance results are reported for the best strategy?",{"text":71,"@type":63},"The best-performing approach achieves 50.50% p.a. statistically and economically significant returns and an annualized Sharpe ratio of 8.14 after transaction 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Financial Issues, 2017, 7(4), 650-662. | |\n| --- | --- | --- |\n| Statistical Arbitrage Pairs Trading with High-frequency Data\u003Cbr>Johannes Stübinger1*, Jens Bredthauer2\u003Cbr>1Department of Statistics and Econometrics, University of Erlangen-Nürnberg, Lange Gasse 20, 90403 Nürnberg, Germany, 2Department of Statistics and Econometrics, University of Erlangen-Nürnberg, Lange Gasse 20, 90403 Nürnberg, Germany.\u003Cbr>*Email: [johannes.stuebinger@fau.de](johannes.stuebinger@fau.de)\u003Cbr>ABSTRACT\u003Cbr>In recent years, more sophisticated techniques for analyzing data and exponential increase in computing power allow high-frequency trading. This paper provides a detailed overview on pairs trading in the context of intraday data and applies different strategies to minute-by-minute prices ofthe S&P 500 constituents from 1998 to 2015. In the back-testing study, the best performing pairs trading approach produces statistically and economically significant returns of 50.50% p.a. and an annualized Sharpe ratio of 8.14 after transaction costs. Although most algorithms show declining returns over time, there still exist pairs trading strategies with favorable results in the recent past.\u003Cbr>Keywords: Finance, Pairs Trading, High-frequency data\u003Cbr>JEL Classifications: G10, G11, G14 |  |  |\n\n| Application |  |\n| --- | --- |\n| Data handling readr\u003Cbr>readxl xlsxxts zoo\u003Cbr>Financial modeling caTools cumstats longmemo quantmod timeSeries tseries\u003Cbr>TTR\u003Cbr>Financial analysis lmtest\u003Cbr>Performance analytics texreg | Wickham et al. (2016)\u003Cbr>Wickham (2016)\u003Cbr>Dragulescu (2014)\u003Cbr>Ryan and Ulrich (2014)\u003Cbr>Zeileis and Grothendieck (2005)\u003Cbr>Tuszynski (2014)\u003Cbr>Erdely and Castillo (2017) Beran et al. (2011)\u003Cbr>Ryan (2016)\u003Cbr>Rmetrics Core Team et al. (2015) Trapletti and Hornik (2017) Ulrich (2016)\u003Cbr>Zeileis and Hothorn (2002) Peterson and Carl (2014)\u003Cbr>Leifeld (2013) |\n\n| Selection | Trading thresholds |  |  |\n| --- | --- | --- | --- |\n|  | Static | Varying | Reverting |\n| Selection |  |  |  |\n| Euclidean distance | ES | EV | ER |\n| Correlation coefficient | CS | CV | CR |\n| Fluctuation behavior | FS | FV | FR |\n\n| Strategy | Return |  |  | Sharpe ratio |  |  |\n| --- | --- | --- | --- | --- | --- | --- |\n|  | S | V | R | S | V | R |\n| k=1 |  |  |  |  |  |  |\n| E | 0.3036 | 0.2389 | −0.6629 | 4.6672 | 1.8818 | −15.4011 |\n| C | 0.0425 | 0.1196 | −0.5482 | 0.3504 | 0.8183 | −7.5086 |\n| F | 0.0651 | 2.5190 | −0.5541 | 0.2154 | 5.9196 | −2.9137 |\n| k=1.5 |  |  |  |  |  |  |\n| E | 0.2933 | 0.5213 | −0.3945 | 5.1433 | 5.4074 | −9.4403 |\n| C | 0.0282 | 0.3111 | −0.3527 | 0.1261 | 2.7059 | −4.8238 |\n| F\u003Cbr>k=2 | 0.0907 | 0.9345 | −0.4375 | 0.3162 | 3.4146 | −2.3254 |\n| E | 0.2552 | 0.5726 | −0.1960 | 4.9565 | 7.5135 | −5.3947 |\n| C | 0.0406 | 0.3785 | −0.2129 | 0.3286 | 3.8191 | −3.2292 |\n| F | 0.1804 | 0.5375 | −0.3596 | 0.6467 | 2.2121 | −2.0071 |\n| k=2.5 |  |  |  |  |  |  |\n| E | 0.2152 | 0.5050 | −0.0898 | 4.4379 | 8.1404 | −3.1485 |\n| C | 0.0689 | 0.3646 | −0.1281 | 0.7810 | 4.1965 | −2.2920 |\n| F\u003Cbr>k=3 | 0.3706 | 0.3751 | −0.2794 | 1.2042 | 1.6842 | −1.6735 |\n| E | 0.1843 | 0.4225 | −0.0273 | 3.8801 | 8.0478 | −1.6239 |\n| C | 0.0576 | 0.3193 | −0.0778 | 0.7020 | 4.0091 | −1.7938 |\n| F | 0.5063 | 0.2911 | −0.2019 | 1.4494 | 1.4156 | −1.3314 |","cbCaiscOY0YvDp49","https://ap.wps.com/l/cbCaiscOY0YvDp49","pdf",852019,13,"English","# Abstract\n# Keywords\n# Application\n## Data handling\n## Financial modeling\n## Financial analysis\n## Performance analytics\n# Selection\n## Trading thresholds\n### Static\n### Varying\n### Reverting\n## Selection measures\n### Euclidean distance\n### Correlation coefficient\n### Fluctuation behavior\n# Strategy\n## Return\n## Sharpe ratio","[{\"question\":\"What is the paper’s main focus?\",\"answer\":\"The paper focuses on pairs trading for statistical arbitrage using high-frequency intraday data, including an overview of methods and strategy testing.\"},{\"question\":\"Which market data and time span are used in the study?\",\"answer\":\"The strategies are tested on minute-by-minute prices of S\\u0026P 500 constituents from 1998 to 2015.\"},{\"question\":\"What performance results are reported for the best strategy?\",\"answer\":\"The best-performing approach achieves 50.50% p.a. statistically and economically significant returns and an annualized Sharpe ratio of 8.14 after transaction costs.\"}]","Statistical Arbitrage Pairs Trading with High-frequency Data | PDF",1788442135]