• Asian Journal of Business Environment
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• v.11 no.2
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• pp.27-38
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• 2021

Purpose: The study mainly investigates bankruptcy risk and income smoothing tendency of Non-Banking Financial Institutions (NBFIs) in Bangladesh. External parties of NBFIs take investment decisions based on financial reports. Stable and predictable income is one of their preference. On the other hand, poor income is one of the signs of NBFIs having bankruptcy risk. Hence the study tries to find whether the NBFIs having bankruptcy are involved in income smoothing or not. Research design, data and methodology: Data were collected from the annual report of twenty-two listed NBFIs in Bangladesh. Data from 2013 to 2017 were used. Altman's Z score and Eckel's model are used to detecting bankruptcy risk and income smoothing respectively. Results: Result implies that most of the NBFIs which have bankruptcy risk are not involved in income smoothing. Therefore, NBFIs which has bankruptcy risk are involved less with income smoothing. Conclusions: The present study revealed that most of the listed NBFIs in Bangladesh are facing bankruptcy risk. They didn't use any fraudulent technique to show smooth income. The findings will help the investor to take an investment decision on NBFIs in Bangladesh. It will convey signals to the stock market in Bangladesh.

• Korean Management Science Review
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• v.33 no.3
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• pp.105-117
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• 2016

In this paper, we present empirical testing result to examine the validity of inbound supply and outbound demand risk factors in the sense of early predicting the firm's bankruptcy risk level. The risk factors are drawn from industry uncertainty attributes categorized as uncertainties of input market (inbound supply), and product market (outbound demand). On the basis of input-output table, industry level inbound and outbound sectors are identified to formalize supply chain structures, relevant inbound and outbound uncertainty attributes and corresponding risk factors. Subsequently, publicly available macro-economic indicators are used to appropriately quantify these risk factors. Total 68 industry level bankruptcy risk forecasting results are presented with the average R-square scores of between 53.4% and 37.1% with varying time lag. The findings offers useful insights to incorporate supply chain risk to the body of firm's bankruptcy risk level prediction literature.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.133-142
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• 2022

This study examines the relationship between debt maturity structure and bankruptcy risk. There are various studies of leverage's effect on bankruptcy risk. Debt maturity, however, has not received the attention it deserves, especially in emerging markets with a high degree of information asymmetry. Using Vietnamese listed company data and various estimations, we find that leverage is positively associated with the likelihood of default. Importantly, short-term leverage shows a significantly positive effect on bankruptcy risk, while long-term leverage does not show significant results. The findings highlight that rollover risk firms are exposed to when using short-term debt increases bankruptcy risk. Meanwhile, firms do not cope with this risk in case of long-term debt adoption. High information asymmetry in emerging markets may be the main reason for the difference. The result is robust for subsamples of firms in different financial conditions, in concentrated and competitive industries, as well as for manufacturing and non-manufacturing companies. We also find that firms in a better financial situation and concentrated industries experience a higher short-term leverage effect than their counterparts. We, however, do not find a significant difference in the impact between manufacturing and non-manufacturing companies. This paper is among the first to examine the relation between debt maturity and bankruptcy risk in Vietnam.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.275-283
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• 2022

This study aims to investigate the influence of internal factors on the bankruptcy risk of an enterprise through a sample of 439 companies listed on the Vietnamese stock exchange. The research collected secondary data from annual audited financial statements from 2008 to 2019 of listing companies. Using two different regression models with two dependent variables, six independent and control variables, we discovered that three of the model's six factors, namely return on total assets, current payment rate, and financial leverage, influence the risk of bankruptcy and account for 86.78% of the variations in firm bankruptcy risk. Financial leverage has the opposite effect on the Z-score index, increasing the risk of bankruptcy of listed firms. Return on total assets and current ratio have a positive impact on the Z-score index, reducing the risk of bankruptcy of listed companies. The findings also revealed that there is no evidence that the size of a corporation, its fixed asset investment ratio, or the size of an auditing firm have an impact on the Z-score index. These findings provide crucial evidence for business owners and managers, as well as shareholders making future capital investment decisions. Our findings can be applied to other businesses in Vietnam and similar jurisdictions.

• Journal of Information Technology Applications and Management
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• v.20 no.4
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• pp.221-234
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• 2013

In this paper, we report bankruptcy risk level forecasting result for automobile parts manufacturing industry. With the premise that upstream supply risk and downstream demand risk could impact on automobile parts industry bankruptcy level in advance, we draw upon industry input-output table to use the economic indicators which could reflect the extent of supply and demand risk of the automobile parts industry. To verify the validity of each economic indicator, we applied simple linear regression for each indicators by varying the time lag from one month (t-1) to 12 months (t-12). Finally, with the valid indicators obtained through the simple regressions, the composition of valid economic indicators are derived using stepwise linear regression. Using the monthly automobile parts industry bankruptcy frequency data accumulated during the 5 years, R-square values of the stepwise linear regression results are 68.7%, 91.5%, 85.3% for the 3, 6, 9 months time lag cases each respectively. The computational testing results verifies the effectiveness of our approach in forecasting bankruptcy risk forecasting of the automobile parts industry.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.81-90
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• 2020

This study investigates the nexus between the level of Corporate Social Responsibility Disclosures (CSRD) and Risk of Bankruptcy of companies that are listing in the Stock Exchanges of Vietnam. To investigate that relationship, this study collected secondary data from annual audited financial statements from 2014 to 2018 of listing companies. Applying two different regression models with two dependent variables and six independent and control variables, we find out that Vietnamese firms with higher level of CSRD performance can rapidly reduce their risk of bankruptcy. This phenomenon happens in the current year and in the coming years in all firms in the research sample. This result may be that the disclosures of social responsibility information can bring financial and non-financial benefits to the firms. In addition, the results also point out that there is a difference in risk of bankruptcy between the group of companies, which discloses and the one which does not disclose corporate social responsibility on their annual reports. This might be from the effects of various factors such as business size, financial leverage, market to book ratio, return on assets, cash flow from operations, etc. Our research results can be applied to other firms in Vietnam and in other similar jurisdictions.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.228-234
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• 2021

The article defines the problems of forming the models of government regulation of clustering, marketing management and labor market in the context of globalization, business bankruptcy risk and services market development. The clustering models based on the optimal partner network cooperation were proposed in order to ensure the strategic development of territories, to attract budget leading enterprises and to support small businesses. A descriptive model of government regulation of clustering, marketing management and labor market in the context of globalization, business bankruptcy risk and Covid-19 was determined.

• IE interfaces
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• v.20 no.4
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• pp.448-457
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• 2007

The small & micro business has the characteristics of both consumer credit risk and business credit risk. In predicting the bankruptcy for small-micro businesses, the problem is that in most cases, the financial data for evaluating business credit risks of small & micro businesses are not available. To alleviate such problem, we propose a bankruptcy prediction mechanism using the credit card sales information available, because most small businesses are member store of some credit card issuers, which is the main purpose of this study. In order to perform this study, we derive some variables and analyze the relationship between good and bad signs. We employ the new statistical learning technique, support vector machines (SVM) as a classifier. We use grid search technique to find out better parameter for SVM. The experimental result shows that credit card sales information could be a good substitute for the financial data for evaluating business credit risk in predicting the bankruptcy for small-micro businesses. In addition, we also find out that SVM performs best, when compared with other classifiers such as neural networks, CART, C5.0 multivariate discriminant analysis (MDA), and logistic regression.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.58-65
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• 2023

Bankruptcy is a significant risk for start-up companies, but with the help of cutting-edge artificial intelligence technology, we can now predict bankruptcy with detailed explanations. In this paper, we implemented the Category Boosting algorithm following data cleaning and editing using OpenRefine. We further explained our model using the Shapash library, incorporating domain knowledge. By leveraging the 5C's credit domain knowledge, financial analysts in banks or investors can utilize the detailed results provided by our model to enhance their decision-making processes, even without extensive knowledge about AI. This empowers investors to identify potential bankruptcy risks in their business models, enabling them to make necessary improvements or reconsider their ventures before proceeding. As a result, our model serves as a "glass-box" model, allowing end-users to understand which specific financial indicators contribute to the prediction of bankruptcy. This transparency enhances trust and provides valuable insights for decision-makers in mitigating bankruptcy risks.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.608-613
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• 2007

Prediction of corporate bankruptcies has long been an important topic and has been studied extensively in the finance and management literature because it is an essential basis for the risk management of financial institutions. Recently, support vector machines (SVMs) are becoming popular as a tool for bankruptcy prediction because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. In addition, they don't require huge training samples and have little possibility of overfitting. However. in order to Use SVM, a user should determine several factors such as the parameters ofa kernel function, appropriate feature subset, and proper instance subset by heuristics, which hinders accurate prediction results when using SVM In this study, we propose a novel hybrid SVM classifier with simultaneous optimization of feature subsets, instance subsets, and kernel parameters. This study introduces genetic algorithms (GAs) to optimize the feature selection, instance selection, and kernel parameters simultaneously. Our study applies the proposed model to the real-world case for bankruptcy prediction. Experimental results show that the prediction accuracy of conventional SVM may be improved significantly by using our model.