Abstract
The accurate prediction of corporate bankruptcy for the firms in different industries is of a great concern to investors and creditors, as the reduction of creditors' risk and a considerable amount of saving for an industry economy can be possible. Financial statements vary between industries. Therefore, economic intuition suggests that industry effects should be an important component in bankruptcy prediction. This study attempts to detail the characteristics of each industry using sector indicators. The results show significant relationship between probability of default and sector indicators. The results of this study may improve the default prediction models performance and reduce the costs of risk management.
Highlights
Prediction of corporate default is of a great concern to investors/creditors, borrowing firms and governments
According to Dimitras et al (1996), logistic regression is in the second place, after Multiple Discriminant Analysis (MDA), in default prediction models
Data description: The dataset was used to classify a set of firms into those that would default and those that would not default on loan payments. It consists of 285 observations of Malaysian companies during 20072012 from four different sectors including: trading and services, manufacturing sectors and Construction and Property Sector
Summary
Prediction of corporate default is of a great concern to investors/creditors, borrowing firms and governments. The world has experienced a large number of financial crisis in emerging market economies of Latin America and Asia during 1994-1998 and the recent crisis in USA due to the sub-prime mortgage during 2008 These financial crisis were not confined individual economy, but affected directly or indirectly almost all the countries of the world. An improvement in model accuracy in the default likelihood assessment leads to enormous future savings for the credit industry. Various profits such as cost decline in credit analysis, an increased debt collection rate and better monitoring attain as of accurate default prediction
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