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Robust methods for stock market data analysis

Author

Listed:
  • Antoniou, I.
  • Akritas, P.
  • Burak, D.A.
  • Ivanov, V.V.
  • Kryanev, A.V.
  • Lukin, G.V.

Abstract

We consider the problem of extraction of trend and chaotic components from irregular stock market time series. The proposed methods also permit to extract a part of chaotic component, the so-called anomalous term, caused by the transient short-time surges with high amplitudes. This provides more accurate determination of the trend component. The methods are based on the M-evaluation with decision functions of Huber and Tukey type. The iterative numerical schemes for determination of trend and chaotic components are briefly presented, resulting in an acceptable solution within a finite number of iterations. The optimal level for extraction of the chaotic component is determined by a new numerical scheme based on the fractal dimension of the chaotic component of the analyzed series. Forecasting from the realized part of the analyzed series and a priori expert information is also discussed.

Suggested Citation

  • Antoniou, I. & Akritas, P. & Burak, D.A. & Ivanov, V.V. & Kryanev, A.V. & Lukin, G.V., 2004. "Robust methods for stock market data analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 538-548.
  • Handle: RePEc:eee:phsmap:v:336:y:2004:i:3:p:538-548
    DOI: 10.1016/j.physa.2003.12.052
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    Cited by:

    1. J. E. Wesen & V. VV. Vermehren & H. M. de Oliveira, 2015. "Adaptive Filter Design for Stock Market Prediction Using a Correlation-based Criterion," Papers 1501.07504, arXiv.org.

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