Business′ Financial Problems Prediction - Croatian Experience

Zenzerović, Robert

doi:10.1080/1331677x.2009.11517387

Cited by 17 publications

(13 citation statements)

References 6 publications

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“…Different methods to develop models of bankruptcy prediction of companies are applied all over the world. The most commonly used are: (1) LR (Jakubík & Teplý, 2008); (2) discriminant analysis (Stroe & Bărbuţă-Mişu, 2010;Zenzerović, 2009); and (3) neural network (Ecer, 2013), etc. The reasons for the wide use of LR models according to Masten and Masten (2012) are: (1) is relatively easy to understand; (2) available in most software packages; and (3) fairly robust (strong, powerful) and reliable tool for predicting the financial problems of the company.…”

Section: Research Methodology and Hypothesismentioning

confidence: 99%

Insolvency prediction model of the company: the case of the Republic of Serbia

Obradović

Jakšić

Rupić

et al. 2018

Economic Research-Ekonomska Istraživanja

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In this article, the authors analyse the existing foreign insolvency prediction models of the company and on the basis of the sample of solvent and insolvent companies they aim to develop a new model to predict insolvency of a company by binomial logistic regression (LR), which will be suitable for the business environment in the Republic of Serbia. The research seeks to determine statistically most important financial ratios in predicting insolvency of Serbian companies. As a result of research, a model for the prediction of bankruptcy was created, which accurately classifies 82.9% of solvent ('healthy') Serbian companies and 93.3% of Serbian companies which have undergone bankruptcy proceedings (Serbian insolvent companies), while the average (total) accuracy of the prediction model is 88.4% of the cases. JEL CLASSIFICATIONSc38; c50; m21; G33 ARTICLE HISTORY

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Section: Research Methodology and Hypothesismentioning

confidence: 99%

Insolvency prediction model of the company: the case of the Republic of Serbia

Obradović

Jakšić

Rupić

et al. 2018

Economic Research-Ekonomska Istraživanja

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“…In a relevant part of this literature (e.g., Altman, 1968;Andrew, 1986;Beaver, 1967;Edmister, 1972;Fulmer, Moon, Gavin, & Erwin, 1984;Lugovskaja, 2009;Nam & Jinn, 2000;Pindado & Rodrigues, 2004;Pompe & Bilderbeek, 2005;Zenzerović, 2009) a "matched-pairs" design has been used, implying a sample with 50% of the observations being from bankrupt firms. 13 The main aim of this research is to highlight the effect of a group of independent variables (financial ratios and corporate governance characteristics) on the likelihood that a small firm may go bankrupt, and not to represent (and investigate) the behavior of the entire population of Italian small firms.…”

Section: Percentage Held By Managers and Directorsmentioning

confidence: 99%

Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms

Ciampi

2015

Journal of Business Research

147

145

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“…The population object of this study was made up by all manufacturing firms with a turnover below 5 million Euro operating in Central Italy. In line with the main literature (e.g., Altman, 1968;Beaver, 1966;Edmister, 1972;Fulmer, Moon, Gavin, & Erwin, 1984;Pindado & Rodrigues, 2004;Pompe & Bilderbeek, 2005;Zenzerović, 2009) the sample analysed in this study was built using a "matched-pairs" approach. A training sample made up of 980 companies was used to design default prediction models.…”

Section: The Analysed Samplementioning

confidence: 99%

Using Prior Payment Behavior Variables for Small Enterprise Default Prediction Modelling

Ciampi¹

2018

IJBM

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This study aims to verify the potential of combining prior payment behavior variables and financial ratios for SE default prediction modelling. Logistic regression was applied to a sample of 980 Italian SEs in order to calculate and compare two categories of default prediction models, one exclusively based on financial ratios and the other based also on company payment behavior related variables. The main findings are: 1) using prior payment behavior variables significantly improves the effectiveness of SE default prediction modelling; ii) the longer the forecast horizon and/or the smaller the size of the firms which are the object of analysis, the higher the improvements in prediction accuracy that can be obtained by using also prior payment behavior variables as default predictors; iii) SE default prediction modelling should be separately implemented for different size groups of firms.

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Business′ Financial Problems Prediction - Croatian Experience

Abstract: ABSTRACT

Cited by 17 publications

References 6 publications

Insolvency prediction model of the company: the case of the Republic of Serbia

Insolvency prediction model of the company: the case of the Republic of Serbia

Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms

Using Prior Payment Behavior Variables for Small Enterprise Default Prediction Modelling

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