Research on the Financial Crisis Early Warning of Listed Companies in China—Based on Probit model and Logit model
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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.Keywords:
Probit
Ordered probit
Multivariate probit model
In this paper,with logistic regression model,from the four aspects of corporation finance we select four indicators:equity ratio(solvency),account receivable turnover(operating capacity),total asset growth rate(growth capacity),and growth rate of profit before tax(earning eapacity).By using the SPSS software, we study the financial risk of listed companies.The results show that the regression model established with the selected factors fits reality very well,and has good predictive accuracy and practical promotional value.
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The financial pre-warning in listed companies proved to be good reference to the rational investors. Based on the Logit nonlinear mode, also with the consideration of available of public information, the paper efforts on an empirical test on all data using several financial indexes, constructs the financial pre-warning logit mode, and predicts the companies which could be denounced as Special Treat.
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Listed company
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This study aims to provide empirical evidence on the factors influencing a company's financial distress. This study examines the role of financial ratios attained from financial statements in predicting the financial distress of manufacturing companies listed in the Indonesia Stock Exchange from 2009 to 2011. The research sample consists of a group of 100 healthy companies, the group of negative net income companies that experience distress for two consecutive years consists of 14 companies, and the group of negative equity book value that experiences financial distress for two consecutive years consists of five companies. The multinomial logit regression was used to test the hypothesis. Results indicate that financial ratios attained from financial statements, namely, profit margin ratio, profitability, and financial leverage, are significant variables in predicting the financial distress of manufacturing companies listed in the Indonesia stock exchange.
Leverage (statistics)
Profit margin
Financial Distress
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Following the customary research results abroad,this article takes public listed enterprises that have been marked ST due to abnormal financial situation as enterprises in financial distress.The principal component analysis is employed to identify model variants and modeling prewarning of financial distress by the Logit model. Sample data of enterprises in financial distress are analyzed and compared with those in normal financial operation. It is conclude that financial indicators are informative in forecasting financial distress.
Financial Distress
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Predicting financial distress have significant importance in corporate finance as it serves as an effective early warning system for the related stakeholders.The study applies the most admired financial distress prediction O-score model and compares its predictive accuracy with estimated logit model. The study estimates logit model by including the profitability ratios, liquidity ratios, leverage ratios, and cash flow ratios. This study filled the gap by using the cash flow ratios to predict financial distress for Pakistani listed firms. The sample for the estimation model consists of 290 firms with 45 distressed and 245 healthy firms for the period 2006-2016 and covers all sectors of Pakistan Stock Exchange. The study provides important insights on the role of different financial ratio in predicting financial distress and shows that estimated logit model produces higher accuracy rate in predicting financial distress.
Leverage (statistics)
Financial Distress
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This paper mainly discusses the study of models for financial distress pre-warning, trying to select general financial indexes by principal component analysis, and meanwhile adding nonfinancial indexes which reflect corporate governance state to complement. Logit Model which is more accurate in prediction is selected, with the 56 company samples including both delisting pre-warned companies and counterparts without financial distress. Old Logit Model with 9 integrative financial indexes and new model with 6 more nonfinancial indexes are respectively built and pass the tests finally. By adding nonfinancial indexes into Logit Model, this paper goes to the conclusion that the new index system was more precise than the old one.
Financial Distress
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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.
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Financial Distress
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Nowadays,in China,more and more investors invest in listed companies through the stock market.If financial crisis occurs in a listed company,investors of any kind,without obtaining relevant information in time,will face enormous risk, which will probably result in personal financial losses and even in threat to the stability of the whole society.While building models,on the basis of those research and literature reviewed,besides financial ratio variables,we add corporate governance variables,macro-economic variables and business operation efficiency variables sequentially and progressively to establish synthesized models.Then the related data of 126 ST companies and 252 non-ST matching companies listed as A-Shares in China,covering the time span from 2002 to 2005,is processed to help construct models through statistical procesures accordingly.By employing DEA,Logit model and neural network model,we apply the data progressively,yet employ the models respectively.An optimum comprehensive financial crisis predicting model is constructed and confirmed through above mentioned processes. At the end,the optimum Logit Model IV is further verified to be robust in predicting financial crisis by applying the data of 1379 A-Share listed companies in the year 2006 as testing sample.
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Variables
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The study aims to prove whether good corporate governance (GCG) is able to predict the probability of companies experiencing financial difficulties.Financial ratios that traditionally used for predicting bankruptcy remains used in this study.Besides, this study also compares logit and probit regression models, which are widely used in research related accounting bankruptcy prediction.Both models will be compared to determine which model is more superior.The sample in this study is the infrastructure, transportation, utilities & trade, services and hotels companies experiencing financial distress in the period 2008-2011.The results show that GCG and other three variables control i.e DTA, CR and company category do not prove significantly to predict the probability of companies experiencing financial difficulties.NPM, the only variable that proved significantly distinguishing healthy firms and distress.In general, logit and probit models do not result in different conclusions.Both of the models confirm the goodness of fit of models and the results of hypothesis testing.In terms of classification accuracy, logit model proves more accurate predictions than the probit models.
Ordered probit
Financial Distress
Probit
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In this paper,Probit model is used to select 36 delisted companies,using their financial data as basic sample and compared with other 36 normal performance non-delisted listed companies.The results show that seven financial indicators including net profit rate of total assets,net profit rate of sales,profit growth rate of main business,growth rate of total assets,ratio of liabilities to assets,current ratio and quick ratio,can better reflect the difference between delisted companies and non-delisted ones,and Probit model based on the study has a good warning effect on delisting risks of listed companies in China.
Ordered probit
Net income
Probit
Warning signs
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