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A neural networks filtering mechanism for foreign exchange trading signals
KTH, School of Information and Communication Technology (ICT).
2010 (English)In: Proceedings - 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2010: Volume 3, IEEE , 2010, 159-167 p.Conference paper, Published paper (Refereed)
Abstract [en]

Neural Networks have been successfully used in several financial applications. In the stock market and foreign exchange domains, Neural Networks have been used with considerable success to predict the future prices of stocks and currency pairs, their rate of return, risk analysis, and several other features that might be of benefit. In this paper, we present a methodology to filter the high-frequency signals of a rule-based foreign exchange trading strategy, through a neural network-based, intelligent selection mechanism. We then compare the results vs. a random selection mechanism and again vs. the overall signal pool, in terms of profit and correctness. We can clearly show that the neural network filtering approach yields a better performance than its random baseline.

Place, publisher, year, edition, pages
IEEE , 2010. 159-167 p.
Keyword [en]
Algorithmic trading, Artificial intelligence, Forex, Neural networks, Optimization, Stock market, Time series prediction
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-150005DOI: 10.1109/ICICISYS.2010.5658495Scopus ID: 2-s2.0-78651308202ISBN: 978-142446583-5 (print)OAI: oai:DiVA.org:kth-150005DiVA: diva2:742416
Conference
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2010, 29 October 2010 through 31 October 2010, Xiamen, China
Note

QC 20140901

Available from: 2014-09-01 Created: 2014-08-29 Last updated: 2014-09-01Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf