[ACM Press the 2008 ACM symposium - Fortaleza, Ceara, Brazil (2008.03.16-2008.03.20)] Proceedings of the 2008 ACM symposium on Applied computing - SAC '08 - Data stream mining for market-neutral algorithmic trading

✍ Scribed by Montana, Giovanni; Triantafyllopoulos, Kostas; Tsagaris, Theodoros

• Book ID: 118119518
• Publisher: ACM Press
• Year: 2008
• Tongue: English
• Weight: 172 KB
• Volume: 0
• Category: Article
• ISBN: 1595937536
• DOI: 10.1145/1363686.1363910

✦ Synopsis

In algorithmic trading applications, a large number of coevolving 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.