Forecasting financial performance for quarries. Geoscience Engineering

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27350%2F14%3A86091343" target="_blank" >RIV/61989100:27350/14:86091343 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.2478/gse-2014-0012" target="_blank" >http://dx.doi.org/10.2478/gse-2014-0012</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2478/gse-2014-0012" target="_blank" >10.2478/gse-2014-0012</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Forecasting financial performance for quarries. Geoscience Engineering

Popis výsledku v původním jazyce

Linear and quadratic discriminate analyses and a logistic regression analysis were applied to a sample file of 233 annual data from 3 countries for a period of 2008-2012 concerning quarries extracting building materials. These methods provided for distributing the sample file quarries into two classes of profitable and loss-making enterprises. Their financial performance had been known, which enabled to assess the classification accuracy of individual method applications. The average classification accuracy was about 86%. The linear discriminate analysis made possible to identify the most influential discriminators that contributed to the classification into the specific groups. In case of our investigation, prices per production unit, direct variablecosts, and ratio of fixed costs to total costs were the most important factors of influence.

Název v anglickém jazyce

Forecasting financial performance for quarries. Geoscience Engineering

Popis výsledku anglicky

Linear and quadratic discriminate analyses and a logistic regression analysis were applied to a sample file of 233 annual data from 3 countries for a period of 2008-2012 concerning quarries extracting building materials. These methods provided for distributing the sample file quarries into two classes of profitable and loss-making enterprises. Their financial performance had been known, which enabled to assess the classification accuracy of individual method applications. The average classification accuracy was about 86%. The linear discriminate analysis made possible to identify the most influential discriminators that contributed to the classification into the specific groups. In case of our investigation, prices per production unit, direct variablecosts, and ratio of fixed costs to total costs were the most important factors of influence.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

AE - Řízení, správa a administrativa

OECD FORD obor

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Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

GeoScience Engineering

ISSN

1802-5420

e-ISSN

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Svazek periodika

LX

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

9

Strana od-do

1-9

Kód UT WoS článku

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EID výsledku v databázi Scopus

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