Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/springer-journals/a-machine-learning-based-pairs-trading-investment-strategy-pairs-78yr0s4sA0
References (27)
-
J. Caldeira, G. Moura (2013)
Selection of a Portfolio of Pairs Based on Cointegration: A Statistical Arbitrage StrategyAdvanced Risk & Portfolio Management® Research Paper Series
-
Huafeng Chen, Shaojun Chen, Zhuo Chen, Feng Li (2017)
Empirical Investigation of an Equity Pairs Trading StrategyAmerican Finance Association Meetings (AFA)
-
H. Hurst (1951)
Long-Term Storage Capacity of ReservoirsTransactions of the American Society of Civil Engineers, 116
-
C. Dunis, Jason Laws, B. Evans (2009)
Modelling and Trading the Soybean-Oil Crush Spread with Recurrent and Higher Order Networks: A Comparative Analysis -
J. Armstrong (2001)
Principles of forecasting : a handbook for researchers and practitioners -
C. Dunis, Jason Laws, Peter Middleton, Andreas Karathanasopoulos (2015)
Trading and hedging the corn/ethanol crush spread using time-varying leverage and nonlinear modelsThe European Journal of Finance, 21
-
C. Krauss, Xuan Do, Nicolas Huck (2017)
Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500Eur. J. Oper. Res., 259
-
Nicolas Huck (2009)
Pairs selection and outranking: An application to the S&P 100 indexEur. J. Oper. Res., 196
-
Evan Gatev, W. Goetzmann, K. Rouwenhorst (1998)
Pairs Trading: Performance of a Relative Value Arbitrage RuleCapital Markets: Market Efficiency
-
Nicolas Huck, Komivi Afawubo (2015)
Pairs trading and selection methods: is cointegration superior?Applied Economics, 47
-
N. Thomaidis, N. Kondakis, G. Dounias (2011)
An Intelligent Statistical Arbitrage Trading System -
C. Dunis, Jason Laws, B. Evans (2006)
Modelling and trading the gasoline crack spread: A non-linear storyDerivatives Use, Trading & Regulation, 12
-
J. Ramos-Requena, J. Trinidad-Segovia, M. Sánchez-Granero (2017)
Introducing Hurst exponent in pair tradingPhysica A-statistical Mechanics and Its Applications, 488
-
H. Rad, R. Low, R. Faff (2016)
The profitability of pairs trading strategies: distance, cointegration and copula methodsQuantitative Finance, 16
-
Ratnadip Adhikari, R. Agrawal (2013)
An Introductory Study on Time Series Modeling and ForecastingArXiv, abs/1302.6613
-
D. Dickey, W. Fuller (1979)
Distribution of the Estimators for Autoregressive Time Series with a Unit RootJournal of the American Statistical Association, 74
-
E. Chan (2013)
Algorithmic Trading: Winning Strategies and Their Rationale -
J. Armstrong (2001)
Principles of forecasting -
Jozef Rudy, C. Dunis, G. Giorgioni, Jason Laws (2010)
Statistical Arbitrage and High-Frequency Data with an Application to Eurostoxx 50 EquitiesERN: Europe (Developed Markets) (Topic)
-
R. Engle, C. Granger (1987)
Co-integration and error correction: representation, estimation and testingEconometrica, 55
-
Nicolas Huck (2010)
Pairs trading and outranking: The multi-step-ahead forecasting caseEur. J. Oper. Res., 207
-
R. Kissell (2021)
Algorithmic TradingAlgorithmic Trading Methods
-
R. Cavalcante, Rodrigo Brasileiro, Victor Souza, Jarley Nóbrega, Adriano Oliveira (2016)
Computational Intelligence and Financial Markets: A Survey and Future DirectionsExpert Syst. Appl., 55
-
R. Elliott, John *, W. Malcolm (2005)
Pairs tradingQuantitative Finance, 5
-
C. Krauss (2017)
Statistical Arbitrage Pairs Trading Strategies: Review and OutlookWiley-Blackwell: Journal of Economic Surveys
-
S. Vempala (2007)
Geometric Random Walks: a Survey -
B. Do, R. Faff (2010)
Does Simple Pairs Trading Still Work?Financial Analysts Journal, 66
- Publisher
- Springer International Publishing
- Copyright
- © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
- ISBN
- 978-3-030-47250-4
- Pages
- 7 –19
- DOI
- 10.1007/978-3-030-47251-1_2
- Publisher site
- See Chapter on Publisher Site
Abstract
[This chapter introduces some important mathematical and financial concepts required to fully understand the workings of a Pairs Trading strategy. These include the concepts of mean-reversion, stationarity, and cointegration. Subsequently, it goes over some widely used strategies to find pairs for trading, mentioning relevant research work in the field. Next, the most commonly used model in a Pairs Trading setup, the threshold-based model, is explained in detail. To complement, other possible alternatives are introduced as well. At last, an overview of the current state-of-the-art concerning the application of Machine Learning in the field of Pairs Trading is provided.]
Published: Jul 14, 2020
Keywords: Mean-reversion; Stationarity; Correlation; Cointegration; Machine learning