IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v345y2005i1p196-206.html
   My bibliography  Save this article

Clustering stock market companies via chaotic map synchronization

Author

Listed:
  • Basalto, N.
  • Bellotti, R.
  • De Carlo, F.
  • Facchi, P.
  • Pascazio, S.

Abstract

A pairwise clustering approach is applied to the analysis of the Dow Jones index companies, in order to identify similar temporal behavior of the traded stock prices. To this end, the chaotic map clustering algorithm is used, where a map is associated to each company and the correlation coefficients of the financial time series to the coupling strengths between maps. The simulation of a chaotic map dynamics gives rise to a natural partition of the data, as companies belonging to the same industrial branch are often grouped together. The identification of clusters of companies of a given stock market index can be exploited in the portfolio optimization strategies.

Suggested Citation

  • Basalto, N. & Bellotti, R. & De Carlo, F. & Facchi, P. & Pascazio, S., 2005. "Clustering stock market companies via chaotic map synchronization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 345(1), pages 196-206.
    • Handle: RePEc:eee:phsmap:v:345:y:2005:i:1:p:196-206
    DOI: 10.1016/j.physa.2004.07.034
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437104009872
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2004.07.034?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    2. Jaydip Sen & Tamal Datta Chaudhuri, 2017. "An Investigation of the Structural Characteristics of the Indian IT Sector and the Capital Goods Sector: An Application of the R Programming in Time Series Decomposition and Forecasting," Papers 1706.07821, arXiv.org.
    3. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.
    4. Sidra Mehtab & Jaydip Sen, 2020. "A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models," Papers 2004.11697, arXiv.org, revised May 2021.
    5. Wafaa S. Sayed & Ahmed G. Radwan & Ahmed A. Rezk & Hossam A. H. Fahmy, 2017. "Finite Precision Logistic Map between Computational Efficiency and Accuracy with Encryption Applications," Complexity, Hindawi, vol. 2017, pages 1-21, February.
    6. Liu, Shen & Maharaj, Elizabeth Ann, 2013. "A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 32-49.
    7. N. C. Suganya & G. A. Vijayalakshmi Pai, 2010. "Pareto‐archived evolutionary wavelet network for financial constrained portfolio optimization," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 17(2), pages 59-90, April.
    8. Vásquez Sáenz, Javier & Quiroga, Facundo Manuel & Bariviera, Aurelio F., 2023. "Data vs. information: Using clustering techniques to enhance stock returns forecasting," International Review of Financial Analysis, Elsevier, vol. 88(C).
    9. Mahdi Massahi & Masoud Mahootchi & Alireza Arshadi Khamseh, 2020. "Development of an efficient cluster-based portfolio optimization model under realistic market conditions," Empirical Economics, Springer, vol. 59(5), pages 2423-2442, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:345:y:2005:i:1:p:196-206. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.