Skip to main content

Advertisement

SpringerLink
Log in

An Investigation into the Use of Intelligent Systems for Currency Trading

  • Published:
Computational Economics Aims and scope Submit manuscript

An Erratum to this article was published on 20 May 2011

Abstract

Investors use a number of technical trading tools to help them in their decision-making. This article aims to enhance this decision making process through the application of Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs). The signals generated by technical trading tools are optimised for maximum profit through the use of GAs. The optimised signals are fed into a variety of fully connected feed forward ANNs, which combine these signals and output a single set of signals of whether to buy, hold or sell in the current market state. The different solutions produced are compared and contrasted, to determine the best ANN architecture for this type of signal amalgamation problem, and the optimal population size and mutation function for the GA. The result is an autonomous trading system with intelligence. This system, as described in this article, has proven to be profitable based on data presented to it—which spans ten currencies over a five year period. The profit margins are statistically significant when compared to un-optimised trading rules as suggested by literature. Further, the margins are statistically significantly more profitable than other no-risk investment strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  • Andreou P., Charalambous C., Martzoukos S. H. (2006) Robust artificial neural networks for pricing of European options. Computational Economics 27(2): 329–351

    Article  Google Scholar 

  • Brock W., Lakonishok J., Lebaron B. (1992) Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance 47(5): 1731–1764

    Article  Google Scholar 

  • Cybenko G. (1989) Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems (MCSS) 2(4): 303–314

    Google Scholar 

  • Fama E. F. (1970) Efficient capital markets: A review of theory and empirical work. Journal of Finance 25(2): 383–417

    Article  Google Scholar 

  • Goldberg D. E. (1989) Genetic Algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing Co Inc, Boston, MA, USA

    Google Scholar 

  • Haykin S. (1994) Neural networks: A comprehensive foundation. Prentice Hall PTR, Upper Saddle River, NJ, USA

    Google Scholar 

  • Kiani K., Kastens T. (2008) Testing forecast accuracy of foreign exchange rates: Predictions from feed forward and various recurrent neural network architectures. Computational Economics 32(4): 383–406

    Article  Google Scholar 

  • Levich R. M., Thomas L. R. (1993) The merits of active currency risk management: Evidence from international bond portfolios. Financial Analysts Journal 49: 63

    Article  Google Scholar 

  • Levy R. (1967) Relative strength as a criterion for investment selection. Journal of Finance 22(4): 595–610

    Article  Google Scholar 

  • Mitchell M. (1996) An introduction to genetic algorithms. Bradford Books, Cambridge

    Google Scholar 

  • Neely, C. J., Weller, P., Dittmar, R. (1996). Is technical analysis in the foreign exchange market profitable? A genetic programming approach. Centre for Economic Policy Research.

  • Okunev J., White D. (2003) Do momentum-based strategies still work in foreign currency markets. Journal of Financial and Quantitative Analysis 38(2): 425–447

    Article  Google Scholar 

  • Papadamou S., Stephanides G. (2007) Improving technical trading systems by using a new matlab-based genetic algorithm procedure. Mathematical and Computer Modelling 46(1–2): 189–197

    Article  Google Scholar 

  • Park C. H. O., Irwin S. H. (2004) The profitability of technical analysis: A review. Urbana, Champaign

    Google Scholar 

  • Pring M. J. (1998) Introduction to Technical Analysis. McGraw-Hill, New York

    Google Scholar 

  • Reed R. D., Marks R. J. (1999) Neural smithing: Supervised learning in feed forward artificial neural networks. Bradford Books, Cambridge

    Google Scholar 

  • Sweeney R. J. (1986) Beating the foreign exchange market. Journal of Finance 41(1): 163–182

    Article  Google Scholar 

  • Taylor M. P., Allen H. (2002) The use of technical analysis in the foreign exchange market. International Library of Critical Writing in Economics 143: 379–389

    Google Scholar 

  • Vanstone B., Finnie G., Hahn T. (2009) Designing short term trading systems with artificial neural networks. In: Ao S.-I., Gelman L. (eds) Advances in electrical engineering and computational science Vol. 39. Springer, Netherlands, pp 401–409

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Rhodes University, Grahamstown, South Africa

    Hannah Thinyane & Jonathan Millin

Authors
  1. Hannah Thinyane
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonathan Millin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hannah Thinyane.

Additional information

An erratum to this article can be found at http://dx.doi.org/10.1007/s10614-011-9267-x

Rights and permissions

Reprints and permissions

About this article

Cite this article

Thinyane, H., Millin, J. An Investigation into the Use of Intelligent Systems for Currency Trading. Comput Econ 37, 363–374 (2011). https://doi.org/10.1007/s10614-011-9260-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10614-011-9260-4

Keywords

Search

Navigation