| SYS | | 0288508 |
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| LBL | | 01009^^^^^2200289^^^450 |
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| 005 | | 20240509095519.4 |
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| 014 | | $a 000570968300008 $2 WOS CC. SSCI |
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| 014 | | $a 2-s2.0-85090987573 $2 SCOPUS |
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| 017 | 70 | $a 10.15240/tul/001/2020-3-008 $2 DOI |
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| 035 | | $a biblio/213788 $2 CREPC2 |
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| 100 | | $a 20201013d2020 m y slo 03 ba |
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| 101 | 0- | $a eng |
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| 102 | | $a CZ |
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| 200 | 1- | $a Methodology of industry statistics: averages, quartiles, and responses to atypical values $f Martin Boďa, Vladimír Úradníček |
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| 330 | | $a 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. |
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| 463 | -1 | $1 001 umb_un_cat*0297915 $1 011 $a 1212-3609 $1 011 $a 2336-5064 $1 200 1 $a E+M Ekonomie a Management $d E&M Economics and Management $v Roč. 23, č. 3 (2020), s. 120-137 $1 210 $a Liberec $c Technická univerzita v Liberci $d 2020 |
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| 606 | 0- | $3 umb_un_auth*0283304 $a priemyselné štatistiky $X industry statistics |
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| 606 | 0- | $3 umb_un_auth*0146357 $a finančné pomery |
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| 606 | 0- | $3 umb_un_auth*0283306 $a kvartily $X quartiles |
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| 606 | 0- | $3 umb_un_auth*0039325 $a hodnoty $X values |
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| 606 | 0- | $3 umb_un_auth*0137360 $a priemery |
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| 608 | | $3 umb_un_auth*0273282 $a články $X journal articles |
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| 700 | -1 | $3 umb_un_auth*0132221 $a Boďa $b Martin $p UMBEF05 $4 070 $9 60 $f 1984- $T Katedra kvantitatívnych metód a informačných systémov |
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| 701 | -0 | $3 umb_un_auth*0002985 $a Úradníček $b Vladimír $p UMBEF05 $9 40 $f 1963-2021 $4 070 $T Katedra kvantitatívnych metód a informačných systémov |
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| 801 | | $a SK $b BB301 $g AACR2 $9 unimarc sk |
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| T85 | | $x existuji fulltexy |
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