Full metadata record
DC poleHodnotaJazyk
dc.contributor.authorBoďa, Martin
dc.contributor.authorÚradníček, Vladimír
dc.date.accessioned2020-09-23T09:32:05Z
dc.date.available2020-09-23T09:32:05Z
dc.date.issued2020
dc.identifier.citationE+M. Ekonomie a Management = Economics and Management. 2020, roč. 23, č. 3, s. 120-137.cs
dc.identifier.issn2336-5604 (Online)
dc.identifier.issn1212-3609 (Print)
dc.identifier.urihttp://hdl.handle.net/11025/39775
dc.format18 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherTechnická univerzita v Libercics
dc.rightsCC BY-NC 4.0en
dc.subjectstatistiky průmyslucs
dc.subjectfinanční poměrycs
dc.subjectoříznutý průměrcs
dc.subjectwinsorized průměrcs
dc.subjectkvantilcs
dc.subjectnesmyslné hodnotycs
dc.subjectzákon nepřímé úměrnostics
dc.titleMethodology of industry statistics: averages, quantiles, and responses to atypical valuesen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe paper notices troublesome aspects of compiling industry statistics for the purpose of inter-enterprise comparison in corporate financial analysis. Whilst making a caveat that this issue is unbeknownst to practitioners and underrated by theorists, the goal of the paper is two-fold. For one thing, the paper demonstrates that financial ratios are inclined to frequency distributions characteristic of power-law (fat) tails and their typical shape precludes a simple treatment. For the other, the paper explores different approaches to compiling industry statistics by considering trimming and winsorizing cleansing protocols, and by confronting trimmed, winsorized as well as quantile measures of central tendency. The issues are empirically illustrated on data for a great number of Slovak construction enterprises for two years, 2009 and 2018. The empirical distribution of eight financial ratios is studied for troublesome features such as asymmetry and power-law (fat) tails that hamper usefulness of traditional descriptive measures of location without considering different possibilities of handling atypical values (such as infinite and outlying values). The confrontation of diverse approaches suggests a plausible route to compiling industry statistics that consists in reporting a 25% trimmed mean alongside 25% and 75% quantiles, all applied to trimmed data (i.e. data after discarding infinite values). The paper also highlights the sorely unnoticed fact that the key ratio of financial analysis, return on equity, may easily attain non-sense values and these should be removed prior to compiling financial analysis; otherwise, industry statistics is biased upward regardless of what measure of central tendency is made use of.en
dc.subject.translatedindustry statisticsen
dc.subject.translatedfinancial ratiosen
dc.subject.translatedtrimmed meanen
dc.subject.translatedwinsorized meanen
dc.subject.translatedquantileen
dc.subject.translatednon-sense valuesen
dc.subject.translatedpower law in the tailen
dc.identifier.doihttps://doi.org/10.15240/tul/001/2020-3-008
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Číslo 3 (2020)
Číslo 3 (2020)

Soubory připojené k záznamu:
Soubor Popis VelikostFormát 
EM_3_2020_08.pdfPlný text870,64 kBAdobe PDFZobrazit/otevřít
Zobrazit minimální záznam Zobrazit statistiky


Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam: http://hdl.handle.net/11025/39775

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.

hledání
navigace
  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Ekonomie a management
  5. 2020
  6. Číslo 3 (2020)