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Data stream mining for market-neutral algorithmic trading

Montana, G., Triantafyllopoulos, K. and Tsagaris, T. (2008) Data stream mining for market-neutral algorithmic trading. In: Symposium on Applied Computing: Proceedings of the 2008 ACM Symposium on Applied Computing. Symposium on Applied Computing, March 16-20, 2008, Fortaleza, Ceara, Brazil. ACM , New York , pp. 966-970. ISBN 978-1-59593-753-7

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Abstract

In algorithmic trading applications, a large number of co-evolving financial data streams are observed and analyzed. A recurrent and important task is to determine how a given stream depends on others, over time, accounting for dynamic dependence patterns and without imposing any probabilistic law governing this dependence. We demonstrate how Flexible Least Squares (FLS), a penalized version of ordinary least squares that accommodates for dynamic regression coefficients, can be deployed successfully in this context. We describe a market-neutral algorithmic trading system based on a combined use of on-line feature extraction and recursive regression. The system has been proved to perform successfully when trading the S&P 500 Futures Index.

Item Type: Proceedings Paper
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield)
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Depositing User: Mrs Megan Hobbs
Date Deposited: 09 Apr 2010 09:52
Last Modified: 07 Jun 2010 11:09
Published Version: http://dx.doi.org/10.1145/1363686.1363910
Status: Published
Publisher: ACM
Identification Number: 10.1145/1363686.1363910
URI: http://eprints.whiterose.ac.uk/id/eprint/10626

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