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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Malkiel, B.: A Random Walk Down Wall Street. W.W. Norton & Company Inc., New York (1973)
Fama, E.F.: Efficient Capital Markets: A Review of Theory and Empirical Work. J. Finance. 25(2), 383–417 (1970)
Abdullah, M.H.L.B., Ganapathy, V.: Neural network ensemble for financial trend prediction. In: TENCON 2000, pp. 157–161 (2000)
Schulmeister, S.: Components of the Profitability of Technical Currency Trading. Appl. Financ. Econ. 18(11), 917–930 (2008)
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)
Mochón, A., Quintana, D., Sáez, Y., Isasi, P.: Soft Computing Techniques Applied to Finance. Appl. Intell. 29(2), 111–115 (2008)
Vanstone, B., Tan, C.: A Survey of the Application of Soft Computing to Investment and Financial Trading. Inf. Technol. Pap. 13 (2003)
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)
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)
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)
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)
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)
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)
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)
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)
Quah, T.S., Srinivasan, B.: Improving Returns on Stock Investment Through Neural Network Selection. Expert Syst. Appl. 17(4), 295–301 (1999)
Yao, J., Tan, C.L.: A Case Study on using Neural Networks to Perform Technical Forecasting of Forex. Neurocomputing 34(1), 79–98 (2000)
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)
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)
HistData.com. http://www.histdata.com/download-free-forex-data
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Yong, Y.L., Ngo, D.C., Lee, Y. (2015). Technical Indicators for Forex Forecasting: A Preliminary Study. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9142. Springer, Cham. https://doi.org/10.1007/978-3-319-20469-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-20469-7_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20468-0
Online ISBN: 978-3-319-20469-7
eBook Packages: Computer ScienceComputer Science (R0)