Abstract
We analyze the potential of unsupervised neural networks when they are employed to support intraday trading activity on financial markets. Several time frequencies have been considered: from five minutes to daily trades. At the current stage our major findings may be summarized as follows: a) unsupervised neural networks are helpful to localize profitable intraday patterns, and they make possible to achieve higher performances than common trading rules; b) trading strategies based on neural networks make exploitable with profits almost continuous trades (i.e. scalping), until transaction costs maintain below proper thresholds.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Arifovic, J., Gençay, R.: Using genetic algorithms to select architecture of a feedforward artificial neural network. Physica A: Statistical Mechanics and its Applications 289(3-4), 574–594 (2001)
Adorno, M.C., Resta, M.: Reliability and Convergence on Kohonen Maps: An Empirical Study. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS, vol. 3213, pp. 426–433. Springer, Heidelberg (2004)
De Bodt, E., Cottrell, M., Verleysen, M.: Statistical tools to assess the reliability of self-organizing maps. Neural Networks 15, 967–978 (2002)
Deboeck, G.J.: Modeling non-linear market dynamics for intra-day trading (1999), Available on-line at: http://www.dokus.com/PapersontheWeb/intradaymodel.htm
Graifer, V.: How to Scalp Any Market, Reality Trading (2005)
Kohonen, T.: Self-Organizing Maps, 2nd edn. Springer, Heidelberg (1997)
Refenes, A.P.N., Burgess, A.N., Bentz, Y.: Neural networks in financial engineering: A study in methodology. IEEE Transactions on Neural Networks 8(6), 1222–1267 (1997)
Refenes, A.P.N., Azema-Barac, M., Chen, J., Karoussos, S.A.: Currency exchange rate prediction and neural network design strategies. Neural Computing & Applications 1(1), 46–58 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Resta, M. (2006). On the Profitability of Scalping Strategies Based on Neural Networks. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_81
Download citation
DOI: https://doi.org/10.1007/11893011_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46542-3
Online ISBN: 978-3-540-46544-7
eBook Packages: Computer ScienceComputer Science (R0)