Robust methods for stock market data analysis

I. Antoniou, P. Akritas, D.A. Burak, V.V. Ivanov, A.V. Kryanev and G.V. Lukin

Physica A: Statistical Mechanics and its Applications, 2004, vol. 336, issue 3, 538-548

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.

Keywords: Stock market; Robust methods; Analysis and forecasting (search for similar items in EconPapers)
Date: 2004
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:336:y:2004:i:3:p:538-548

DOI: 10.1016/j.physa.2003.12.052

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