• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.441-449
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• 2021

Bankruptcy is indicated by the inability of the company to meet its maturity obligations. The Covid-19 pandemic has had a terrible impact on the economy and businesses. The aim of this study to determine the effect of the ratios of activity, growth, leverage, and profitability in predicting bankruptcy projected by earnings per share (EPS). The sample of this research was non-banking financial sector companies listed on the Indonesia Stock Exchange in 2015-2019 and the purposive sampling technique was used. The data analysis method used was the logistic regression method to test the hypotheses. Company growth shows the company's ability to manage sales and generate high company profits, as such, the probability of the company experiencing bankruptcy will be lower. The results of this study showed that the debt to assets ratio (DAR), debt to equity ratio (DER), and return on assets (ROA) can predict bankruptcy. Meanwhile, this research found that the total assets turnover (TATO) ratio, sales growth, and net profit margin (NPM) cannot be used to predict bankruptcy.

• The Journal of Asian Finance, Economics and Business
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• 제9권3호
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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.

• The Journal of Asian Finance, Economics and Business
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• 제9권1호
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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.

• 산업융합연구
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• 제15권1호
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• pp.17-32
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• 2017

The purpose of this study is to evaluate financial models that can predict corporate bankruptcy with diverse studies on evaluation models. The study uses discriminant analysis, logistic model, decision tree, neural networks as analyses tools with 18 input variables as major financial factors. The study found meaningful variables such as current ratio, return on investment, ordinary income to total assets, total debt turn over rate, interest expenses to sales, net working capital to total assets and it also found that prediction performance of suggested method is a bit low compared to that in literature review. It is because the studies in the past uses the data set on the listed companies or companies audited from outside. And this study uses data on the companies whose credibility is not verified enough. Another finding is that models based on decision tree analysis and discriminant analysis showed the highest performance among many bankruptcy forecasting models.

• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.432-438
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• 2011

The purpose of the present thesis is to develop bankruptcy prediction models capable of being applied to the Korean construction industry and to deduce an optimal model through comparative evaluation of final developed models. A study population was selected as general contractors in the Korean construction industry. In order to ease the sample securing and reliability of data, it was limited to general contractors receiving external audit from the government. The study samples are divided into a bankrupt company group and a non-bankrupt company group. The bankruptcy, insolvency, declaration of insolvency, workout and corporate reorganization were used as selection criteria of a bankrupt company. A company that is not included in the selection criteria of the bankrupt company group was selected as a non-bankrupt company. Accordingly, the study sample is composed of a total of 112 samples and is composed of 48 bankrupt companies and 64 non-bankrupt companies. A financial ratio was used as early predictors for development of an estimation model. A total of 90 financial ratios were used and were divided into growth, profitability, productivity and added value. The MDA (Multivariate Discriminant Analysis) model and BLRA (Binary Logistic Regression Analysis) model were used for development of bankruptcy prediction models. The MDA model is an analysis method often used in the past bankruptcy prediction literature, and the BLRA is an analysis method capable of avoiding equal variance assumption. The stepwise (MDA) and forward stepwise method (BLRA) were used for selection of predictor variables in case of model construction. Twenty two variables were finally used in MDA and BLRA models according to timing of bankruptcy. The ROC-Curve Analysis and Classification Analysis were used for analysis of prediction performance of estimation models. The correct classification rate of an individual bankruptcy prediction model is as follows: 1) one year ago before the event of bankruptcy (MDA: 83.04%, BLRA: 93.75%); 2) two years ago before the event of bankruptcy (MDA: 77.68%, BLRA: 78.57%); 3) 3 years ago before the event of bankruptcy (MDA: 84.82%, BLRA: 91.96%). The AUC (Area Under Curve) of an individual bankruptcy prediction model is as follows. : 1) one year ago before the event of bankruptcy (MDA: 0.933, BLRA: 0.978); 2) two years ago before the event of bankruptcy (MDA: 0.852, BLRA: 0.875); 3) 3 years ago before the event of bankruptcy (MDA: 0.938, BLRA: 0.975). As a result of the present research, accuracy of the BLRA model is higher than the MDA model and its prediction performance is improved.

• 대한가정학회지
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• 제41권10호
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• pp.199-209
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• 2003

The purpose of this study was to investigate the causes of bankruptcy in small apparel stores. Data were collected from 153 apparel retail store owners who experienced failure in small apparel stores in Busan. The results showed as follows; The internal factors that caused bankruptcy in small apparel stores were the problems related with employees, capital, investment, weak marketing strategies, inadequate management, and characteristics of store owners. The external factors were economic condition, unexpected incidents, and the condition of market. There were significant differences in the perception of factors which caused the store bankruptcy according to prior business experience before opening apparel stores, the level of education, and the period between store opening and closing.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2004년도 품질경영모델을 통한 가치 창출
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• pp.655-673
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• 2004

This paper is concerned with analysing the bankruptcy prediction power of three models: Multivariate Discriminant Analysis(MDA ), Logit Analysis, Neural Network. The after-crisis bankrupted companies were limited to the research data and the listed companies belonging to manufacturing industry was limited to the research data so as to improve prediction accuracy and validity of the model. In order to assure meaningful bankruptcy prediction, training data and testing data were not extracted within the corresponding period. The result is that prediction accuracy of neural network model is more excellent than that of logit analysis and MDA model when considering that execution of testing data was followed by execution of training data.

• 산업공학
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• 제20권4호
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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.

• 한국경영과학회지
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• 제26권1호
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• pp.109-116
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• 2001

The detection of corporate failures is a subject that has been particularly amenable to cross-sectional financial ratio analysis. In most of firms, however, the financial data are available over past years. Because of this, a model utilizing these longitudinal data could provide useful information on the prediction of bankruptcy. To correctly reflect the longitudinal and firm-specific data, the generalized linear model with assuming the first order AR(autoregressive) process is proposed. The method is motivated by the clinical research that several characteristics are measured repeatedly from individual over the time. The model is compared with several other predictive models to evaluate the performance. By using the financial data from manufacturing corporations in the Korea Stock Exchange (KSE) list, we will discuss some experiences learned from the procedure of sampling scheme, variable transformation, imputation, variable selection, and model evaluation. Finally, implications of the model with repeated measurement and future direction of research will be discussed.

• 한국콘텐츠학회논문지
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• 제18권8호
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• pp.525-535
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• 2018

분류 문제에서 특정 범주의 빈도가 다른 범주에 비해 과도하게 높은 경우, 왜곡된 기계 학습을 유발할 수 있는 데이터 불균형(imbalanced data) 문제가 발생한다. 기업부도 예측 문제도 그 중 하나인데, 일반적으로 금융기관과 거래하는 기업들의 부도율은 대단히 낮아서, 부도 사례보다 정상 사례의 빈도가 월등히 높은 데이터 불균형 문제가 발생하고 있다. 이러한 데이터 불균형 문제를 해결하기 위해서는 적절한 표본추출 기법이 적용될 필요가 있으며, 지금껏 소수 범주 데이터를 복원 추출함으로써 다수 범주 데이터와 비율을 맞추어 데이터 불균형을 해결하는 오버 샘플링(oversampling) 기법이 주로 활용되어 왔다. 그러나 전통적인 오버 샘플링은 과적합화(overfitting)가 발생할 위험이 높아질 수 있는 단점이 있다. 이러한 배경에서 본 연구는 효과적인 기업부도 예측 모형 학습을 위한 표본추출 기법으로 2014년에 Menardi와 Torelli가 제안한 ROSE(random over sampling examples) 기법을 제안한다. ROSE 기법은 학습에 사용될 사례를 반복적으로 새롭게 합성하여 생성(synthetic generation)하는 기법으로, 과적합화 문제를 회피하면서도 분류 예측 정확도 개선에 도움을 줄 수 있다. 이에 본 연구에서는 ROSE 기법을 가장 성능이 우수한 이분류기로 알려진 SVM(support vector machine)과 결합하여 국내 한 대형 은행의 기업부도 예측에 적용해 보고, 다른 표본추출 기법들과의 비교연구를 수행하였다. 실험 결과, ROSE 기법이 다른 기법에 비해 통계적으로 유의한 수준으로 SVM의 예측정확도 개선에 기여할 수 있음을 확인하였다. 이러한 본 연구의 결과는 부도예측 외에 다른 사회과학 분야 예측문제의 데이터 불균형 문제 해결에도 ROSE가 우수한 대안이 될 수 있다는 사실을 시사한다.