Modelo de classificação de risco de crédito de empresas

Brito, Giovani Antonio Silva; Neto, Alexandre Assaf

doi:10.1590/s1519-70772008000100003

Cited by 32 publications

(46 citation statements)

References 6 publications

(3 reference statements)

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“…The model is accurate for 89% of the cases and performs satisfactorily compared with the main Brazilian prediction studies (Altman, Baidya & Dias, 1979;Brito & Assaf Neto, 2008;Elizabetsky, 1976;Kanitz, 1976;Matias 1978;Sanvicente & Minardi, 1998;Silva, 1982). Korol and Korodi (2010) report that no single factor is responsible for a company's bankruptcy.…”

Section: Introductionmentioning

confidence: 55%

“…Once the bankrupt companies (25 cases) were identified in the sample, the group of solvent companies (25 cases) were selected; that is, for each bankrupt company one solvent company was selected, a procedure also known as the pairing method (Brito & Assaf Neto, 2008;Kanitz, 1976;Matias, 1978;Sanvicente & Minardi, 1998).…”

Section: Resultsmentioning

confidence: 99%

“…As well as companies in composition with creditors, and in accordance with Brito and Assaf Neto (2008), those companies that appeared as being bankrupt during this period in the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários -CVM) registry of publicly-traded companies were also considered as being bankrupt. The year in which the bankruptcy event occurred was defined as that in which the company's shares were traded as being in composition with creditors and in which it appeared in the CVM register as being bankrupt.…”

Section: Samplementioning

confidence: 99%

“…For the financial variables, the same indicators from Brazilian studies on forecasting bankruptcy were used (Altman et al, 1979;Brito & Assaf Neto, 2008;Elizabetsky, 1976;Kanitz, 1976;Matias, 1978;Sanvicente & Minardi, 1998;Silva, 1982). These studies were chosen due to their representativeness in the literature, their good predictive ability, and because they use Brazilian companies, the same scope of this article.…”

Section: Forecasting Modelmentioning

confidence: 99%

“…Kanitz (1976) 80 68 Elizabetsky (1976) 74 63 Matias (1978) 70 77 Altman et al (1979) 83 77 Silva (1982) 90 86 Brito and Assaf Neto (2008) 93 90 Sanvicente and Minardi (1998) 82 82 Source: Elaborated by the authors based on Matarazzo (2010).…”

Section: Modelmentioning

confidence: 99%

See 4 more Smart Citations

Predicting financial distress in publicly-traded companies

Rezende

Montezano

Oliveira

et al. 2017

Rev. contab. finanç.

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Several models for forecasting bankruptcy have been developed over the years, one of the reasons for which is the important part it plays in decision-making. However, forecasting a company's bankruptcy leaves a very short time for stakeholders to change the situation. It is in this context that this paper arises in order to develop a model for predicting financial distress, which is identified as a step prior to bankruptcy. The predictive model uses the logistic regression technique with panel data and a sample of Brazilian publicly-traded companies with shares listed on the São Paulo Stock, Commodities, and Futures Exchange between 2001 and 2014. As well as financial variables, the final model includes market expectations (macroeconomic) and sector variables. These variables are statistically tested and the hypothesis is confirmed that they improve the accuracy of the model. The research identified the existence of financial distress in 96% of the companies that went bankrupt. In addition, the relationship between the phenomena of bankruptcy and financial distress is verified, using financial and macroeconomic explanatory variables. The results demonstrate that most (83%) of the explanatory variables in the model for predicting bankruptcy are also present in the model for predicting the phenomenon of financial distress. The expected gross domestic product variables and the quick ratio, asset turnover, and net equity over total liabilities financial variables are statistically significant in predicting both phenomena. With this evidence, the study suggests the use of the concept of financial distress as a stage prior to bankruptcy and provides a model for predicting financial distress with 89% accuracy when applied to publicly-traded companies in Brazil in the period examined.

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Section: Introductionmentioning

confidence: 55%

Section: Resultsmentioning

confidence: 99%

Section: Samplementioning

confidence: 99%

Section: Forecasting Modelmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

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Predicting financial distress in publicly-traded companies

Rezende

Montezano

Oliveira

et al. 2017

Rev. contab. finanç.

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Defined strategies for financial working capital management

Talonpoika

Kärri

Pirttilä

et al. 2016

International Journal of Managerial Finance

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Purpose – The purpose of this paper is to develop strategies for financial working capital management and to present previous literature on financial working capital management and its measures. Design/methodology/approach – Qualitative comparative analysis is used to formulate the strategies, and the variables in the analysis have been selected from previous literature. Empirical data consists of 91 companies listed in the Helsinki Stock Exchange during 2008-2012. Findings – The results indicate 11 possible strategies for financial working capital management which all aim at increasing financial working capital. There are suitable strategies for all companies independent from their profitability, capital intensity or working capital requirements. Research limitations/implications – The presented strategies have been created theoretically and have not been tested in companies, which could be done in future research. Originality/value – This study has three contributions. First, previous literature on financial working capital management is reviewed. Second, a novel measure for financial working capital is developed. Third, strategies for financial working capital management are presented.

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Default prediction of small and medium enterprises: Portuguese construction sector

Costa

Lisboa

2023

JFMPC

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Purpose This paper aims to study the default risk of small and medium-sized enterprises in the construction sector. Design/methodology/approach An unbalanced sample of 2,754 Portuguese companies from the construction sector, from 2008 to 2020, is analysed. Companies are classified in default or compliant following an ex-ante criterion. Then, using the stepwise analysis, the most relevant variables are selected, which are later used in the logit model. To verify the robustness of the results, a sample of legally insolvent companies is added (mixed criterion) and the initial sample is split into two subperiods. Findings Financial variables are the most relevant to predict the pattern for this sample. The main conclusions show that smaller and older companies, more indebted, with more liquidity and with higher EBIT have a higher probability of default. These conclusions are confirmed using a mixed criterion to classify companies as default or compliant and including a macroeconomic dummy. Practical implications This work not only contributes to enlarging the literature review but also makes relevant contributions to practice. Companies from the construction sector can understand which indicators must control to avoid financial problems. The government also has relevant information that can help in adapting or creating regulations for recovering or revitalizing companies. Originality/value This study proposed an ex-ante criterion that can be used for all types of companies. Most works use a legal or a mixed criterion that does not allow for detecting signs of financial problems in advance. Moreover, the sample used is almost unexplored – SMEs from a sector with great mortality rate.

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Modelo de classificação de risco de crédito de empresas

Cited by 32 publications

References 6 publications

Predicting financial distress in publicly-traded companies

Predicting financial distress in publicly-traded companies

Defined strategies for financial working capital management

Default prediction of small and medium enterprises: Portuguese construction sector

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