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Flexible least squares for temporal data mining and statistical arbitrage

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Author Info

  • Giovanni Montana
  • Kostas Triantafyllopoulos
  • Theodoros Tsagaris

Abstract

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.

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File URL: http://arxiv.org/pdf/0709.3884
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Bibliographic Info

Paper provided by arXiv.org in its series Papers with number 0709.3884.

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Date of creation: Sep 2007
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Publication status: Published in Expert Systems with Applications (2009), 36, 2819-2830.
Handle: RePEc:arx:papers:0709.3884

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Web page: http://arxiv.org/

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  1. Manishi Prasad & Peter Wahlqvist & Rich Shikiar & Ya-Chen Tina Shih, 2004. "A," PharmacoEconomics, Springer Healthcare | Adis, vol. 22(4), pages 225-244.
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Cited by:
  1. K. Triantafyllopoulos & G. Montana, 2011. "Dynamic modeling of mean-reverting spreads for statistical arbitrage," Computational Management Science, Springer, vol. 8(1), pages 23-49, April.
  2. Kuethe, Todd H. & Foster, Kenneth A. & Florax, Raymond J.G.M., 2008. "A Spatial Hedonic Model with Time-Varying Parameters: A New Method Using Flexible Least Squares," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6306, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  3. Zsolt Darvas & Balázs Varga, 2012. "Uncovering Time-Varying Parameters with the Kalman-Filter and the Flexible Least Squares: a Monte Carlo Study," Working Papers 1204, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
  4. Theodoros Tsagaris & Ajay Jasra & Niall Adams, 2010. "Robust and Adaptive Algorithms for Online Portfolio Selection," Papers 1005.2979, arXiv.org.
  5. Zsuzsanna Zsibók & Balázs Varga, 2012. "Inflation Persistence in Hungary: a Spatial Analysis," Working Papers 1203, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.

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