Methodology of Industry Statistics: Averages, Quantiles and Responses to Atypical Value

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dc.contributor.author Boďa, Martin
dc.contributor.author Úradníček, Vladimír
dc.contributor.other Ekonomická fakulta cs
dc.date.accessioned 2020-09-02T09:42:31Z
dc.date.available 2020-09-02T09:42:31Z
dc.identifier.issn 1212-3609
dc.identifier.uri https://dspace.tul.cz/handle/15240/157484
dc.description.abstract The 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.format text
dc.language.iso en
dc.publisher Technická Univerzita v Liberci cs
dc.publisher Technical university of Liberec, Czech Republic en
dc.relation.ispartof Ekonomie a Management cs
dc.relation.ispartof Economics and Management en
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dc.rights CC BY-NC
dc.subject Industry statistics en
dc.subject financial ratios en
dc.subject trimmed mean en
dc.subject winsorized mean en
dc.subject quantile en
dc.subject non-sense values en
dc.subject power law in the tail en
dc.subject.classification C19
dc.subject.classification M10
dc.subject.classification M40
dc.title Methodology of Industry Statistics: Averages, Quantiles and Responses to Atypical Value en
dc.type Article en
dc.publisher.abbreviation TUL
dc.relation.isrefereed true
dc.identifier.doi 10.15240/tul/001/2020-3-008
dc.identifier.eissn 2336-5604
local.relation.volume 3
local.relation.issue 23
local.relation.abbreviation E+M cs
local.relation.abbreviation E&M en
local.faculty Faculty of Economics
local.citation.spage 120
local.citation.epage 137
local.access open
local.fulltext yes
local.filename EM_3_2020_8


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