Robust methods for stock market data analysis

• Published in: Physica A Vol. 336; no. 3; pp. 538 - 548
• Main Authors: Antoniou, I., Akritas, P., Burak, D.A., Ivanov, V.V., Kryanev, A.V., Lukin, G.V.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.05.2004
• Subjects: Analysis and forecasting; Robust methods; Stock market; Robust methods; Stock market; 02.05.−r; 89.65.Gh; 05.45.Tp; Analysis and forecasting
• ISSN: 0378-4371; 1873-2119
• DOI: 10.1016/j.physa.2003.12.052

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.