| Issue |
SHS Web Conf.
Volume 65, 2019
The 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019)
|
|
|---|---|---|
| Article Number | 04007 | |
| Number of page(s) | 6 | |
| Section | Mathematical Methods, Models, Informational Systems and Technologies in Economics | |
| DOI | https://doi.org/10.1051/shsconf/20196504007 | |
| Published online | 29 May 2019 | |
Investment attractiveness modeling using multidimensional statistical analysis methods
1
Odessa National Economic University, Mathematical methods of economic analysis Department, Odessa, Ukraine
2
Odessa National Economic University, Economic analysis Department, Odessa, Ukraine
3
Fujitsu Technology Solutions, IT Department, Lodz, Poland
* Corresponding author: shinkar@te.net.ua
The article examines the investment attractiveness of the main branches of the food industry of Ukraine as a latent variable. For the first time in this area, a combination of various methods of multivariate statistical analysis is used for research (cluster analysis and factor analysis – the principal component method). These methods made it possible to use a large number of various indicators of the activities of industries to characterize investment attractiveness. As a result, the set of the branches was divided into three groupsclusters: “leaders” are the most attractive sectors for investment, “middle peasants” are attractive branches for investment, and “outsiders” are the least attractive branches for investment. The generalizing factors (principal components), which influence the resulting factor - investment attractiveness, were found. The interrelation of the generalizing factors and initial indicators is established. As a result of the research, it was possible to make an objective assessment of the investment attractiveness (as a latent indicator) of the main branches of the food industry in Ukraine, using instead of a multitude of indicators only three latent factors.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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