Montana, G., Triantafyllopoulos, K. and Tsagaris, T. (2009) Flexible least squares for temporal data mining and statistical arbitrage. Expert Systems with Applications, 36 (2). pp. 2819-2830. ISSN 0957-4174Full text not available from this repository. (Request a copy)
A number of recent emerging applications call for studying data streams, potentially infinite flows of information updated in real-time. When multiple co-evolving data streams are observed, an important task is to determine how these streams depend on each other, accounting for dynamic dependence patterns without imposing any restrictive probabilistic law governing this dependence. In this paper we argue that flexible least squares (FLS), a penalized version of ordinary least squares that accommodates for time-varying regression coefficients, can be deployed successfully in this context. Our motivating application is statistical arbitrage, an investment strategy that exploits patterns detected in financial data streams. We demonstrate that FLS is algebraically equivalent to the well-known Kalman filter equations, and take advantage of this equivalence to gain a better understanding of FLS and suggest a more efficient algorithm. Promising experimental results obtained from a FLS-based algorithmic trading system for the S&P 500 Futures Index are reported.
|Keywords:||Temporal data mining; Flexible least squares; Time-varying regression; Algorithmic trading system; Statistical arbitrage|
|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:||22 Mar 2010 10:33|
|Last Modified:||16 Nov 2015 11:49|