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
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.
|Institution:||The University of Sheffield|
|Academic Units:||The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield)|
|Depositing User:||Mrs Megan Hobbs|
|Date Deposited:||09 Apr 2010 09:52|
|Last Modified:||16 Nov 2015 11:49|