The bankruptcy prediction study under the financial crisis: The case of Chinese export-oriented enterprises

Author(s):  
Qin Zheng ◽  
Chen Yanjun
2020 ◽  
Vol 31 (84) ◽  
pp. 542-559
Author(s):  
Wanderson Rocha Bittencourt ◽  
Pedro H. M. Albuquerque

ABSTRACT This study sought to analyze the variables that can influence company bankruptcy. For several years, the main studies on bankruptcy reported on the conventional methodologies with the aim of predicting it. In their analyses, the use of accounting variables was massively predominant. However, when applying them, the accounting variables were considered as homogenous; that is, for the traditional models, it was assumed that in all companies the behavior of the indicators was similar, and the heterogeneity among them was ignored. The relevance of the financial crisis that occurred at the end of 2007 is also observed; it caused a major global financial collapse, which had different effects on a wide variety of sectors and companies. Within this context, research that aims to identify problems such as the heterogeneity among companies and analyze the diversities among them are gaining relevance, given that the sector-related characteristics of capital structure and size, among others, vary depending on the company. Based on this, new approaches applied to bankruptcy prediction modeling should consider the heterogeneity among companies, aiming to improve the models used even more. A causal tree and forest were used together with quarterly accounting and sector-related data on 1,247 companies, 66 of which were bankrupt, 44 going bankrupt after 2008 and 22 before. The results showed that there is unobserved heterogeneity when the company bankruptcy processes are analyzed, raising questions about the traditional models such as discriminant analysis and logit, among others. Consequently, with the large volume in terms of dimensions, it was observed that there may be a functional form capable of explaining company bankruptcy, but this is not linear. It is also highlighted that there are sectors that are more prone to financial crises, aggravating the bankruptcy process.


2020 ◽  
Vol 07 (04) ◽  
pp. 2050039
Author(s):  
Isabel Linda Moyo ◽  
Victor Gumbo ◽  
Eriyoti Chikodza ◽  
Brian Jones

Prediction of financial distress for lending institutions has been a major concern since the financial crisis of 2008. The motivation for empirical research in bank bankruptcy prediction is clear — the early detection of financial distress and the use of corrective measures are preferable to protection under bankruptcy law. If it is possible to recognize failing banks in advance, then appropriate action can be taken to reverse the process before it is too late. This study uses panel multi-state Markov (MSM) chains to build a predictive model for financial distress of banks in Zimbabwe. Microeconomic factors and the CAMELS ratings were used in the construction of the MSM model. Distress probabilities were calculated using hazard ratios found by MSM and then the Altman [Formula: see text]-Scores were generated for each bank as a means of validating the built MSM model. The scores generated were very similar to the current CAMELS ratings.


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