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.
CITATION STYLE
Lipinski, P., & Brabazon, A. (2014). Pattern mining in ultra-high frequency order books with self-organizing maps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8602, pp. 288–298). Springer Verlag. https://doi.org/10.1007/978-3-662-45523-4_24
Mendeley helps you to discover research relevant for your work.