Abstract
One of the goals of macroeconomic analysis is to rank and segment enterprises described by many financial indicators. The segmentation can be used for investment strategies or risk evaluation. The aim of this research was to distinguish groups of similar objects and visualize the results in a low dimensional space. In order to obtain clusters of similar objects, the authors applied a DEA BCC model and archetypal analysis for a set of companies described by financial indicators and listed on the Warsaw Stock Exchange. The authors showed that both methods give consistent results. To get a better insight into the data structure as well as a visualization of the similarities between objects, the authors used a new approach called the PHATE algorithm. It allowed the results of DEA and archetypal analysis to be visualized in a low dimensional space.
Highlights
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One of the goals of macroeconomic analysis is to rank and segment enterprises described by numerous indicators in terms of their economic efficiency
We performed data envelopment analysis (DEA) for the set of 68 companies described with six financial indicators
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. One of the goals of macroeconomic analysis is to rank and segment enterprises described by numerous indicators in terms of their economic efficiency. There are many classical methods that can be used to do this, such as, e.g., clustering methods
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