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On the Use of Multidimensional Data Analysis Techniques for Corporate Valuation

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  • Georgeta Vintila
  • Stefan Gherghina

Abstract

The aim of this research consists in the investigation of a random sample of companies which belong to five European emerging countries, respectively Hungary, Poland, Russia, Slovakia, and Ukraine, from the valuation perspective, by using multidimensional data analysis techniques. Thus, by employing the principal component analysis, after transforming the initial characteristics there resulted two principal components, also considering the restriction of minimizing the loss of information. Subsequently, by the instrumentality of factor analysis, there resulted two factors required to explain the correlations existing between variables. The usefulness of both multidimensional data analysis techniques emerges from the reduction of the significant number of variables in a lesser number of principal components, respectively factors.

Suggested Citation

  • Georgeta Vintila & Stefan Gherghina, 2014. "On the Use of Multidimensional Data Analysis Techniques for Corporate Valuation," Modern Applied Science, Canadian Center of Science and Education, vol. 8(3), pages 202-202, June.
    • Handle: RePEc:ibn:masjnl:v:8:y:2014:i:3:p:202
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    References listed on IDEAS

    as
    1. Dray, Stephane, 2008. "On the number of principal components: A test of dimensionality based on measurements of similarity between matrices," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2228-2237, January.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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