Predicting Bankruptcy with Semi-Parametric Single-Index Model

Masten, Arjana Brezigar; Masten, Igor

doi:10.1080/1331677x.2012.11517497

Cited by 2 publications

(3 citation statements)

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“…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. Ebrahimi and Nikbakht (2011) showed that their LR model developed on a sample of companies listed on the stock exchange in Tehran for the period between 2001 and 2009 is more superior in predicting the bankruptcy of the company than Altman's (1968) model, Ohlson's (1980) model, Zmijewski's (1984 model and Shumway's (2001) model.…”

Section: Research Methodology and Hypothesismentioning

confidence: 99%

“…Solvent companies are, therefore, companies in which bankruptcy proceedings did not start within one year. Due to the fact that a large discrepancy between the two groups of companies (Masten & Masten, 2012) may result in a higher probability of correct classification of a group, in this study a sample of insolvent companies that was randomly selected has been expanded with an equal number of solvent companies. The appropriate pattern for the development of models of the 86 cases, therefore, include the financial statements for the period between 2010 and 2011 for two groups of companies in the Republic of Serbia: the first group consists of 43 insolvent companies (companies which were not subject to bankruptcy proceedings during one year) while the second group consists of 43 solvent companies.…”

Section: Sample Selectionmentioning

confidence: 99%

See 1 more Smart Citation

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%

Section: Sample Selectionmentioning

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

View full text Add to dashboard Cite

show abstract

“…These are: the size of the company; a measure(s) of the financial structure; a measure(s) of performance; and a measure(s) of current liquidity. In connection with the logit model, it is interesting to point to the research conducted by Masten and Masten (2012). They compare the logit model, as a standard parametric model for predicting bankruptcy, with semi-parametric model.…”

Section: Theoretical Framework Of the Researchmentioning

confidence: 99%

Financial Indicators as Predictors of Illiquidity

2017

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The main objective of this study is the development of the model for predicting illiquidity, i.e. identification of financial indicators on the basis of which one can predict illiquidity. The research focus is on large companies in the Republic of Serbia. Bearing in mind the results of previous research and the assumptions underlying the logistic regression, the paper relied on logistic regression for drawing conclusions. For each of the 426 companies included in the sample, based on data from financial statements, financial ratios were calculated in respect of: liquidity, activity, solvency, profitability, and effectiveness, which were used as independent variables in the study. The research results show that in the prediction of illiquidity of large companies in Serbia, from a total of 23 financial indicators included in the model, the following distinguish themselves as significant: capital turnover ratio, inventory turnover ratio, fixed-asset turnover ratio, real asset coverage ratio, net profit ratio, return on total assets, return on equity, and effectiveness of main business activity.

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Predicting Bankruptcy with Semi-Parametric Single-Index Model

Cited by 2 publications

References 10 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

Financial Indicators as Predictors of Illiquidity

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