scholarly journals Practical application of the CCB model in Czechia

2021 ◽  
Vol 39 (2) ◽  
pp. 299-323
Author(s):  
Vitezslav Halek
Keyword(s):  
Czech Republic ◽  
Financial Distress ◽  
Prediction Method ◽  
Bankruptcy Prediction ◽  
Clothing Industry ◽  
Financial Indicators ◽  
Practical Application ◽  
Data Set ◽  
The Czech Republic ◽  
Financial Situation

This research aimed to present a new bankruptcy prediction model and apply this original prediction method in practice. The Come Clean Bankruptcy (or CCB) model uses relevant financial indicators and ratios to detect the signs of impending financial distress in time so that the management can take appropriate measures to avoid it. The model was applied to the data reported by 199 entities operating in the textile/clothing industry in the Czech Republic. Analyzing data reported for the previous seven years enabled us to predict which companies are more likely to end in a difficult financial situation. Afterward, comparing these predictions with the actual development of those companies in 2013-2020 serves to verify the efficacy and usability of the model to corporate reality. The research has shown that companies that went bankrupt in the analyzed period represented only a fraction of the data set (roughly 4.5%). Despite the small number of financial failures occurring during the analyzed period, the CCB model could detect impending bankruptcy in one-third of the cases.

scholarly journals Analysis of decreasing natality trend in the Czech Republic

2008 ◽  
Vol 56 (3) ◽  
pp. 147-156
Author(s):  
Jitka Poměnková ◽  
Lenka Němcová
Keyword(s):  
Czech Republic ◽  
Regression Model ◽  
Multiple Regression Model ◽  
Living Standard ◽  
Second Step ◽  
Economic Factors ◽  
The Czech Republic ◽  
Financial Situation ◽  
Evaluation Influence ◽  
Family State

The aim of this paper is factors identification of the decreasing natality trend in the Czech Republic between years 1991–2005. This identification is done with respect to the financial situation and living standard of families.The first step, analysis of natality factor – animation natality, is performed. Animation natality is divi­ded according to the mother family state in the time of the birth. Trend of born in marriage and trend out of marriage are described. Following analysis is focused on decreasing component of natality – number of born in marriage.The second step is time series correlation analysis used for identification and evaluation influence of demographic and economic factors on decreasing component of natality. Based on this analysis, in­fluen­cing factors for regression model describing natality are selected.The last step is formulation and estimation of multiple regression model describing causality between natality in marriage and selected factors.

scholarly journals COMPETITIVENESS ANALYSIS OF KAZAKHSTAN CONFECTIONARY SECTOR USING FINANCIAL DISCRIMINANT MODELS

2017 ◽  
Vol 5 ◽  
pp. 144-153
Author(s):  
Petr Hájek ◽  
Gulnar Zhunissova ◽  
Taťjana Čábelová ◽  
Adilya Baidildina
Keyword(s):  
Czech Republic ◽  
Predictive Models ◽  
Local Market ◽  
Z Score ◽  
The Czech Republic ◽  
Financial Situation ◽  
Central Asian ◽  
Discriminant Models ◽  
Score Model ◽  
Political Turmoil

Measuring competitiveness offers hundreds of analytical options. We have chosen to analyze and compare companies of confectionery sector in Kazakhstan. We opted to use bankruptcy and creditworthiness models and compare competitiveness through the financial situation of main competitors on that market. Companies analyzed comprise of two Ukrainian companies (Konti and Roshen), Russian companies (Nestlé Russian branch serving also Central Asian markets and KDV - Yaskino) and three local corporations Rakhat, Bayan Sulu and Konfety Karagandy. Models used for analysis are Altman z-score model, Taffler z-score model, IN99, IN01, IN05, and creditworthiness model. The IN models were created in the Czech Republic based on companies' data from the 1990s which was the period of higher inflation, small currency an big banking crisis, massive imports, developing competition and infrequent political turmoil. These models have comparably much greater benefits for analyzing companies in Kazakhstan because they are based on hundreds of companies in contrast to tens of companies on which Altman or Taffler based their famous and highly predictive models. We present an analysis of models in 2007 – 2016 period based on publicly accessible data. We show the IN models have valuable benefits for comparison compared with other older models and that they can disclose certain events or corporate situations in a clearer way than other Altman or Taffler z-score models and should be used in Kazakhstan and improved to suit better the local market environment.

scholarly journals Relationship of activities in reverse logistics according to sell point in Czech Republic

2019 ◽  
Vol 61 ◽  
pp. 01016 ◽  
Author(s):  
František Milichovský
Keyword(s):  
Czech Republic ◽  
Reverse Logistics ◽  
Descriptive Statistics ◽  
Primary Research ◽  
Data Set ◽  
The Czech Republic ◽  
Chi Squared ◽  
Statistical Program ◽  
Relationship Of ◽  
Nominal Variables

The paper is focused on findings, if final customers in sell points reflect activities of reverse logistics. Main result of research provides relationship between sell point and reverse activity. The research was aimed at random chosen group of 811 respondents in the Czech Republic. Real participants have been 293, what is effectiveness in 36.13%. The primary research provides possible approaches for companies in sell points within reverse logistics activities to final consumers. To process the results of the questionnaire survey were used basic types of descriptive statistics on the selected data set. The data were processed by using the statistical program IBM SPSS Statistics 24, which was subsequently analysed the dependency between the two nominal variables by means of contingency tables and Pearson's chi-squared test. Limitation for this research is because of the chosen sample and targeting only on Czech Republic.

Synanthropic vegetation of the Eragrostion cilianensi-minoris alliance in the Czech Republic

Biologia ◽  
2011 ◽  
Vol 66 (6) ◽  
Author(s):  
Radomír Němec ◽  
Zdeňka Lososová ◽  
Pavel Dřevojan ◽  
Kristýna Žáková
Keyword(s):  
Czech Republic ◽  
Correspondence Analysis ◽  
Detrended Correspondence Analysis ◽  
Vegetation Types ◽  
Data Set ◽  
The Czech Republic ◽  
Synanthropic Vegetation ◽  
Arable Fields ◽  
Twinspan Classification ◽  
Southern Moravia

AbstractA synthesis of the alliance Eragrostion cilianensi-minoris in the Czech Republic is presented on the basis of 82 relevés including new unpublished data. A TWINSPAN classification and detrended correspondence analysis were used to identify the main vegetation types included in the alliance Eragrostion cilianensi-minoris. A syntaxonomic revision of the data set revealed five associations of the alliance: Digitario sanguinalis-Eragrostietum minoris, Portulacetum oleraceae, Eragrostio poaeoidis-Panicetum capillaris, Cynodontetum dactyli, and Hibisco trioni-Eragrostietum poaeoidis. The latter was recently found in several arable fields in Southern Moravia (Czech Republic) and was newly characterized.

scholarly journals The ParlSpeech V2 data set: Full-text corpora of 6.3 million parliamentary speeches in the key legislative chambers of nine representative democracies

2020 ◽  
Author(s):  
Christian Rauh ◽  
Jan Schwalbach
Keyword(s):  
United Kingdom ◽  
Czech Republic ◽  
New Zealand ◽  
The Netherlands ◽  
Full Text ◽  
Data Set ◽  
Meta Data ◽  
The Czech Republic ◽  
The United Kingdom ◽  
Text Corpora

ParlSpeech V2 contains complete full-text vectors of more than 6.3 million parliamentary speeches in the key legislative chambers of Austria, the Czech Republic, Germany, Denmark, the Netherlands, New Zealand, Spain, Sweden, and the United Kingdom, covering periods between 21 and 32 years. Meta-data include information on date, speaker, party, and partially agenda item under which a speech was held. This release note provides a more detailed guide to the data.

scholarly journals Assessment of subjective aspects of the quality of life in the various regions of the Czech Republic

2011 ◽  
Vol 59 (7) ◽  
pp. 267-274 ◽  
Author(s):  
Julie Poláčková ◽  
Andrea Jindrová
Keyword(s):  
Quality Of Life ◽  
Principal Component Analysis ◽  
Czech Republic ◽  
Social Services ◽  
Principal Component ◽  
Well Being ◽  
Component Analysis ◽  
Data Set ◽  
The Czech Republic

The paper is focused on the methodological approaches to assess subjective aspects of the quality of life in the various regions. Besides, directly measurable indicators, which may not always correspond with the quality of life of the individuals in the regions, the subjective aspects of well-being are also in the spotlight. The pilot analysis examined the answers to questions such as: Are you satisfied with the health and social services, the cost of living, safety of public spaces, affordability of housing, or your personal job situation? These answers were used for an assessment of the quality of life in the different regions of the Czech Republic. We used multivariate modeling to explicitly account for the hierarchical structure of respondents within the Czech Republic, and for understanding patterns of variation between regions. The principal component analysis (PCA) was used for the general analysis of regional differences. The overall goal of principal component analysis is to reduce the dimensionality of a data set, while simultaneously retaining the information present in the data. The differences were illustrated by cartographic visualization and by scatter plots of the first three principal components. The cluster analysis was used to discover similarities and differences of the quality of life within various regions of the Czech Republic.

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