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


Finance

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

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.
Jméno a příjmení autora:

Martin Boďa, Vladimír Úradníček

Rok:
2020
Ročník:
23
Číslo:
3
Klíčová slova:
Industry statistics, financial ratios, trimmed mean, winsorized mean, quantile, nonsense values, power law in the tail
DOI (& full text):
Anotace:
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…více
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.
Sekce:
Finance

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