Abstract
This paper addresses the issue of discovering frequent patterns in order book shapes, in the context of the stock market depth, for ultra-high frequency data. It proposes a computational intelligence approach to building frequent patterns by clustering order book shapes with Self-Organizing Maps. An experimental evaluation of the approach proposed on the London Stock Exchange Rebuild Order Book database succeeded with providing a number of characteristic shape patterns and also with estimating probabilities of some typical transitions between shape patterns in the order book.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Engle, R., Fleming, M., Ghysels, E., Nguyen, G.: Liquidity and Volatility in the U.S. Treasury Market: Evidence From A New Class of Dynamic Order Book Models. http://www.unc.edu/maguilar/metrics/Giang.pdf, (accessed February 21 2012)
Goodhart, C., O’Hara, M.: High frequency data in financial markets: Issues and applications. Journal of Empirical Finance 4, 73–114 (1997)
Heston, S., Korajczyk, R., Sadka, R.: Intraday patterns in the crosssection of stock returns. Journal of Finance 65(4), 1369–1407 (2010)
Kohonen, T.: Self-Organizing Maps. Springer (2000)
Lee, Y., Fok, R., Liu, Y.: Explaining intraday pattern of trading volume from the order flow data. Journal of Business Finance and Accounting 28(3), 199–230 (2001)
McInish, T., Wood, R.: An analysis of intraday patterns in bid-ask spreads for nyse stocks. Journal of Finance 47(2), 753–764 (1992)
O’Hara, M.: Market Microstructure Theory. Blackwell, Oxford (1995)
Tian, G., Guo, M.: Interday and intraday volatility: additional evidence from the shanghai stock exchange. Review of Quantitative Finance and Accounting 28(3), 287–306 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lipinski, P., Brabazon, A. (2014). Pattern Mining in Ultra-High Frequency Order Books with Self-Organizing Maps. In: Esparcia-Alcázar, A., Mora, A. (eds) Applications of Evolutionary Computation. EvoApplications 2014. Lecture Notes in Computer Science(), vol 8602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45523-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-662-45523-4_24
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45522-7
Online ISBN: 978-3-662-45523-4
eBook Packages: Computer ScienceComputer Science (R0)