Early warning models of financial distress. Case study of the Romanian firms listed on RASDAQ
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In this paper we design an early warning model for Romanian distressed firms. The logit model was built based on financial ratios of 66 Romanian firms listed on RASDAQ that were facing financial difficulties in 2011. In addition, we identified the main principal components obtained with minimum loss of information after applying the principal component analysis and proposed a new estimation of the logit model by replacing the initial set of input data matrix with the main principal components of the financial observations. The results indicated an increase with 12 percentage points in the performance of the one year ahead prediction of financial distress of the new warning model.Keywords:
Romanian
Financial Distress
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Nowadays, as a result of the global competition encountered, numerous companies come up against financial distresses. To predict and take proactive approaches for those problems is quite important. Thus, the prediction of crisis and financial distress is essential in terms of revealing the financial condition of companies. In this study, financial ratios relating to 156 industrial firms that are quoted in the Istanbul Stock Exchange are used and probabilities of financial distress are predicted by means of an ordered logit regression model. By means of Altman's Z Score, the dependent variable is composed by scaling the level of risk. Thus, a model that can compose an early warning system and predict financial distress is proposed.
Financial Distress
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The article selects 12 financial indicators from the six aspects of listed companies' profitability, operating capacity, solvency, growth capacity, cash flow and capital structure to construct Probit model and Logit model to study the financial difficulties of listed companies in China. The results show that earnings per share, equity ratio, interest protection multiples and inventory turnover ratio have a significant early warning effect on the financial distress of listed companies in China. The Probit model has a prediction accuracy rate of 78.31% for whether the company will fall into financial difficulties, and the Logit model has a prediction accuracy rate of 92.18%. The Logit model's prediction ability is significantly better than the Probit model.
Probit
Ordered probit
Multivariate probit model
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The recent studies on financial distress are mostly confined to static econometric or statistical methods based on cross-sectional financial data ,and it is ignored that the variation of companies' financial status is a dynamic process. In order to show the change of companies in financial position, this study constructed the financial distress prediction model based on panel logit. On the selection of variables,we select not only financial ratios,but also indexes on ownership concentration, corporate governance and audit opinion. Empirical results indicate that the probabilistic predictions generated by the panel logit model can provide a measure of the company's financial position.
Financial Distress
Position (finance)
Ordered logit
Proxy (statistics)
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The recent studies on financial distress are mostly confined to static econometric and statistical method based on cross-sectional financial data,and ignore the fact that the companies' financial status is a dynamic process.In order to show the change of companies in financial position,the paper established the financial distress prediction model based on panel logit.Empirical Results indicate that the forecast accuracy of panel logit model is better than ordinary logit model.
Financial Distress
Ordered logit
Position (finance)
Panel analysis
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In the context of the current financial crisis, when more companies are facing bankruptcy or insolvency, the paper aims to find methods to identify distressed firms by using financial ratios. The study will focus on identifying a group of Romanian listed companies, for which financial data for the year 2008 were available. For each company a set of 14 financial indicators was calculated and then used in a principal component analysis, followed by a cluster analysis, a logit model, and a CHAID classification tree.
Romanian
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This paper is based on the list companies incorporated in Shanghai. The samples of our model include both twenty-three companies which stepped into financial distress the first time in the year 2004 and twenty-three companies which is normal and with the same size to the formers'. We build two financial distress prediction models with the Logistic regression after the adjustment of related financial indexes, and take them into practice. The conclusion is that the financial distress model which is based on the adjusted financial indexes is better than the model which is based on the original financial indexes in the prediction of financial distress.
Financial Distress
Predictive modelling
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In this paper,the listed companies of China from 2005 to 2007 are studied,the Logit model is used to conduct an empirical analysis of financial early-warning for listed enterprises,and then the effect of the models are tested.The results show that the logit model can be used to predict if there is a financial crisis for the listed companies one year later.
Listed company
Ordered logit
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Purpose: This study aims to compare the prediction accuracy of traditional distress prediction models for the firms which are at an early and advanced stage of distress in an emerging market, Pakistan, during 2001–2015. Design/methodology/approach: The methodology involves constructing model scores for financially distressed and stable firms and then comparing the prediction accuracy of the models with the original position. In addition to the testing for the whole sample period, comparison of the accuracy of the distress prediction models before, during, and after the financial crisis was also done. Findings: The results indicate that the three-variable probit model has the highest overall prediction accuracy for our sample, while the Z-score model more accurately predicts insolvency for both types of firms, i.e., those that are at an early stage as well as those that are at an advanced stage of financial distress. Furthermore, the study concludes that the predictive ability of all the traditional financial distress prediction models declines during the period of the financial crisis. Originality/value: An important contribution is the widening of the definition of financially distressed firms to consider the early warning signs related to failure in dividend/bonus declaration, quotation of face value, annual general meeting, and listing fee. Further, the results suggest that there is a need to develop a model by identifying variables which will have a higher impact on the financial distress of firms operating in both developed and developing markets.
Predictive modelling
Financial Distress
Sample (material)
Bankruptcy Prediction
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The recent studies on financial distress are mostly confined to static econometric method based on cross-sectional financial data,and ignore the fact that the companies' financial status is a dynamic process.In order to show the change of companies in financial position,the paper established the financial distress prediction model based on panel Logit.On the selection of variables,we select not only financial ratios,but also indexes on ownership concentration, corporate governance and audit opinion.Empirical Results indicate that the forecast accuracy of panel Logit model is very high,so we can forecast the financial distress probability of next year by the current year's data.
Financial Distress
Ordered logit
Position (finance)
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The authors apply the trisection method,which classifies the listed corporations into three categories: financial distress corporation,financial unstable corporation and financial healthy corporation,and adopt the principal component analysis methods and the ternary Logistic model to construct a financial distress prediction model.The empirical research results indicate that the financial distress early warning model based on ternary Logistic model with good forecast ability and low misjudge costs can give investors,corporate managements and creditors more definite information on future financial state of the corporation.
Corporation
Financial Distress
Empirical Research
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