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METHODOLOGY OF INDUSTRY STATISTICS: AVERAGES, QUANTILES, AND RESPONSES TO ATYPICAL VALUES


Finance

METHODOLOGY OF INDUSTRY STATISTICS: AVERAGES, QUANTILES, AND RESPONSES TO ATYPICAL VALUES

Name and surname of author:

Martin Boďa, Vladimír Úradníček

Year:
2020
Volume:
23
Issue:
3
Keywords:
Industry statistics, financial ratios, trimmed mean, winsorized mean, quantile, nonsense values, power law in the tail
DOI (& full text):
Anotation:
The paper studies the somewhat neglected and underestimated issue of constructing industry statistics for the purpose of inter-enterprise comparisons that are an indispensable ingredient for a…more
The paper studies the somewhat neglected and underestimated issue of constructing industry statistics for the purpose of inter-enterprise comparisons that are an indispensable ingredient for a sensible corporate financial analysis grounded in financial ratios. A comprehensive financial analysis requires that financial ratios computed for an enterprise being analyzed be compared to typical values of financial ratios of enterprises in the same industry. Several approaches are available as to how to obtain typical values, e.g. averages, quantiles, robust measures of location; and, yet, none is generally accepted and widely used. Whereas in other countries it seems that average values are favoured to describe the financial image of an industry, in Slovak conditions the preferred methodology is making use of quantile values that are compiled from financial statements of numerous Slovak enterprises. Whereas CRIF – Slovak Credit Bureau, Ltd. (henceforth referred to as CRIF) uses traditionally three quartile values (with a possibility of extending the report by averages), DataSpot, Ltd. (henceforth referred to as DataSpot) summarizes industries by second deciles, medians and eight deciles.
Section:
Finance

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