Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Soft Computing
Published in: Soft Computing 12/2016

08-08-2016 | Methodologies and Application

Pairs trading strategy optimization using the reinforcement learning method: a cointegration approach

Authors: Saeid Fallahpour, Hasan Hakimian, Khalil Taheri, Ehsan Ramezanifar

Published in: Soft Computing | Issue 12/2016

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Recent studies show that the popularity of the pairs trading strategy has been growing and it may pose a problem as the opportunities to trade become much smaller. Therefore, the optimization of pairs trading strategy has gained widespread attention among high-frequency traders. In this paper, using reinforcement learning, we examine the optimum level of pairs trading specifications over time. More specifically, the reinforcement learning agent chooses the optimum level of parameters of pairs trading to maximize the objective function. Results are obtained by applying a combination of the reinforcement learning method and cointegration approach. We find that boosting pairs trading specifications by using the proposed approach significantly overperform the previous methods. Empirical results based on the comprehensive intraday data which are obtained from S&P500 constituent stocks confirm the efficiently of our proposed method.
previous article Realization of a data traceability and recovery service for a trusted authority service co-ordination within a Cloud environment

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Literature
Andrade S, Di Pietro V, Seasholes M (2005) Understanding the profitability of pairs trading. Unpublished working paper, UC Berkeley, Northwestern University
Bertram W (2010) Analytic solutions for optimal statistical arbitrage trading. Phys A 389(11):2234–2243CrossRef
Bogomolov T (2011) Pairs trading in the land down under. In: Finance and Corporate Governance Conference
Chiu MC, Wong HY (2015) Dynamic cointegrated pairs trading: mean-variance time-consistent strategies. J Comput Appl Math 290:516–534CrossRefMATHMathSciNet
Engle RF, Granger CWJ (1987) Co-integration and error correction: representation, estimation, and testing. Econometrica 55(2):251–276
Gao X, Chan L (2000) An algorithm for trading and portfolio management using Q-learning and sharpe ratio maximization. In: Proceedings of the international conference on neural information processing, pp 832–837
Gatev E, Goetzmann WN, Rouwenhorst KG (2006) Pairs trading: performance of a relative-value arbitrage rule. Rev Financ Stud 19(3):797–827CrossRef
Granger CW (1981) Some properties of time series data and their use in econometric model specification. J Econ 16(1):121–130CrossRef
Huang CF, Hsu CJ, Chen CC, Chang BR, Li CA (2015) An intelligent model for pairs trading using genetic algorithms. Comput Intell Neurosci 2015:16
Huck N, Afawubo K (2015) Pairs trading and selection methods: Is cointegration superior? Appl Econ 47(6):599–613CrossRef
Kawasaki Y, Tachiki S, Udaka H, Hirano T (2003) A characterization of long-short trading strategies based on cointegration. In: Computational intelligence for financial engineering, 2003. Proceedings. 2003 IEEE International Conference, IEEE, pp 411–416
Lee JW, Park J, Lee J, Hong E (2007) A multiagent approach to Q-learning for daily stock trading. IEEE Trans Syst Man Cybern Part A Syst Humans 37(6):864–877CrossRef
Moody J, Saffell M (2001) Learning to trade via direct reinforcement. IEEE Trans Neural Netw 12(4):875–889CrossRef
Muslumov A, Yuksel A, Yuksel SA (2009) The profitability of pairs trading in an emerging market setting: evidence from the Istanbul stock exchange. Empir Econ Lett 8(5):1–6
Puspaningrum H, Lin YX, Gulati CM (2010) Finding the optimal pre-set boundaries for pairs trading strategy based on cointegration technique. J Stat Theory Pract 4(3):391–419CrossRefMATHMathSciNet
Perlin MS (2009) Evaluation of pairs-trading strategy at the Brazilian financial market. J Deriv Hedge Funds 15(2):122–136CrossRefMathSciNet
Sutton RS, Barto AG (1998) Introduction to reinforcement learning. MIT Press, Cambridge
Tan Z, Quek C, Cheng PY (2011) Stock trading with cycles: a financial application of ANFIS and reinforcement learning. Expert Syst Appl 38(5):4741–4755CrossRef
Tourin A, Yan R (2013) Dynamic pairs trading using the stochastic control approach. J Econ Dyn Control 37(10):1972–1981CrossRefMathSciNet
Won Lee J (2001) Stock price prediction using reinforcement learning. In: Industrial electronics, 2001. Proceedings. ISIE 2001. IEEE International Symposium on vol. 1, IEEE, pp 690–695
Metadata
Title
Pairs trading strategy optimization using the reinforcement learning method: a cointegration approach
Authors
Saeid Fallahpour
Hasan Hakimian
Khalil Taheri
Ehsan Ramezanifar
Publication date
08-08-2016
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 12/2016
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-016-2298-4

Other articles of this Issue 12/2016

Soft Computing 12/2016 Go to the issue

Methodologies and Application

An objective reduction algorithm using representative Pareto solution search for many-objective optimization problems

Foundations

On congruences of weak lattices

Methodologies and Application

An improved artificial bee colony algorithm based on the strategy of global reconnaissance

Focus

Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images

Methodologies and Application

A systematic EHW approach to the evolutionary design of sequential circuits

Methodologies and Application

Test-cost-sensitive attribute reduction on heterogeneous data for adaptive neighborhood model

Premium Partner

    Image Credits