Technical Indicators for Forex Forecasting: A Preliminary Study

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9142)

Abstract

Traders and economists are often at odds with regards to the approach taken towards Forex financial market forecasting. Methods originating from the Artificial Intelligence (AI) area of study have been used extensively throughout the years in predicting the trading pattern as it is deemed to be robust enough to handle the uncertainty associated with Forex trading time series data. Herein this paper, the effects of different input types, in particular: close price as well as various technical indicators derived from the close price are investigated to determine its effects on the Forex trend predicted by an intelligent machine learning module.

Keywords

Forex forecasting Technical analysis Linear regression line Artificial Neural Network Dynamic Time Warping 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dunis, C.L., Laws, J., Schilling, U.: Currency trading in volatile markets: Did neural networks outperform for the EUR/USD during the financial crisis 2007–2009? J. Deriv. Hedge Funds. 18(1), 2–41 (2012)CrossRefGoogle Scholar
  2. 2.
    Malkiel, B.: A Random Walk Down Wall Street. W.W. Norton & Company Inc., New York (1973)Google Scholar
  3. 3.
    Fama, E.F.: Efficient Capital Markets: A Review of Theory and Empirical Work. J. Finance. 25(2), 383–417 (1970)CrossRefGoogle Scholar
  4. 4.
    Abdullah, M.H.L.B., Ganapathy, V.: Neural network ensemble for financial trend prediction. In: TENCON 2000, pp. 157–161 (2000)Google Scholar
  5. 5.
    Schulmeister, S.: Components of the Profitability of Technical Currency Trading. Appl. Financ. Econ. 18(11), 917–930 (2008)CrossRefGoogle Scholar
  6. 6.
    Neely, C.J., Weller, P.A.: Technical Analysis in the Foreign Exchange Market. Federal Reserve Bank of St. Louis Working Paper Series 2011-001 (2011)Google Scholar
  7. 7.
    Mochón, A., Quintana, D., Sáez, Y., Isasi, P.: Soft Computing Techniques Applied to Finance. Appl. Intell. 29(2), 111–115 (2008)CrossRefGoogle Scholar
  8. 8.
    Vanstone, B., Tan, C.: A Survey of the Application of Soft Computing to Investment and Financial Trading. Inf. Technol. Pap. 13 (2003)Google Scholar
  9. 9.
    De Brito, R.F.B., Oliveira, A.L.I.: Sliding window-based analysis of multiple foreign exchange trading systems by using soft computing techniques. In: IEEE International Joint Conference on Neural Networks (IJCNN), pp. 4251–4258 (2014)Google Scholar
  10. 10.
    Abraham, A.: Analysis of hybrid soft and hard computing techniques for forex monitoring systems. In: Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, pp. 1616–1622 (2002)Google Scholar
  11. 11.
    Canelas, A., Neves, R., Horta, N.: A new SAX-GA methodology applied to investment strategies optimization. In: Proceedings of The Fourteenth International Conference on Genetic And Evolutionary Computation Conference, pp. 1055–1062 (2012)Google Scholar
  12. 12.
    Canelas, A., Neves, R., Horta, N.: A SAX-GA Approach to Evolve Investment Strategies on Financial Markets Based on Pattern Discovery Techniques. Expert Syst. Appl. 40(5), 1579–1590 (2013)CrossRefGoogle Scholar
  13. 13.
    Canelas, A., Neves, R., Horta, N.: Multi-dimensional pattern discovery in financial time series using SAX-GA With extended robustness. In: Proceeding of The Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion, pp. 179–180 (2013)Google Scholar
  14. 14.
    Tiong, L.C.O., Ngo, D.C.L. Ngo, Lee, Y.: Forex trading prediction using linear regression line, artificial neural network and dynamic time warping algorithms. In: Proc. Fourth Int. Conf. Comput. Informatics, pp. 71–77 (2013)Google Scholar
  15. 15.
    Tiong, L.C.O., Ngo, D.C.L. Ngo, Lee, Y.: Forex prediction using support vector machine and dynamic time warping. In: Proceeding of Thirteenth International Conference on Electronics, Information and Communications (ICEIC), pp. 141–142 (2014)Google Scholar
  16. 16.
    Choi, J.H., Lee, M.K., Rhee, M.W.: Trading S&P 500 stock index futures using a neural network. In: Proceedings of the Third Annual International Conference on Artificial Intelligence Applications on Wall Street, pp. 63–72 (1995)Google Scholar
  17. 17.
    Quah, T.S., Srinivasan, B.: Improving Returns on Stock Investment Through Neural Network Selection. Expert Syst. Appl. 17(4), 295–301 (1999)CrossRefGoogle Scholar
  18. 18.
    Yao, J., Tan, C.L.: A Case Study on using Neural Networks to Perform Technical Forecasting of Forex. Neurocomputing 34(1), 79–98 (2000)CrossRefMATHGoogle Scholar
  19. 19.
    Emam, A.: Optimal Artificial Neural Network Topology for Foreign Exchange Forecasting. In: Proceedings of the 46th Annual Southeast Regional Conference on XX, pp. 63–68 (2008)Google Scholar
  20. 20.
    Sher, G.I.: Forex trading using geometry sensitive neural networks. In: Proceedings of The Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion, pp. 1533–1534 (2012)Google Scholar
  21. 21.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computing and Information SystemsSunway UniversityBandar SunwayMalaysia

Personalised recommendations