Прогнозирование банкротства средних и малых российских компаний
The aim of the research is to develop the methodology of bankruptcy prediction applying the specified statutory values of the existing models with a glance to company’s industry and developing the author’s prediction model. Initially authors estimated the forecast accuracy of the existing models for the enterprises of 8 industries. Using CART (Classification And Regression Tree) methodology the original statutory values of the models were specified for every industry under research. The calculated statutory values demonstrated the high level of prediction accuracy and balanced the indicators of accuracy for bankrupt and non-bankrupt companies. The indicators with the maximum level of significance for bankruptcy prediction were selected from all the models. They formed a basis for a new developed model, which has demonstrated the high level of prediction accuracy on a sample under research. The statutory values for the new model were also developed. The implementation of the research’s results will increase the efficiency of bank ruptcy prediction and low the number of bank rupt companies.
The chief aim of this paper is to analyse dynamics of linear and non-linear methods to predict bankruptcy for Russian private small and medium-sized retail and wholesale trade companies. We use financial and non-financial data prior and subsequent to the economic crisis of 2008—2009. We use the following methods: logistic regression and random forest.
This research will be of vital importance especially to banks and other credit organisations providing loans to small and medium businesses.
Our dataset comprises from 200,000 to 600,000 companies depending on specific year. We use data from the Ruslana database which covers the period from 2004 to 2012.
The definition of default is extended to financial difficulties by adding voluntary liquidated firms to those liquidated as a result of legal bankruptcy. We study active companies and two types of liquidated ones.
Heterogeneity of Russian companies is taken into account in several ways. In addition to financial ratios derived from financial statements we include non-financial variables such as regional distribution, age, size and legal form into statistical models.
Evaluation of the prediction performance is done with the help of out-of-sample forecasts. We obtain models with quite high predictive power, area under ROC curve reaches 0.75. Random forest outperformed logit-model. Adding non-financial information such as age and federal region leads to the improved forecasts while legal form and size do not have a great impact on the outcome. Among financial measures liquidity, profitability and leverage ratios turned out to be essential. Moreover, our models captured a structural change which was likely to be caused by the crisis of 2008—2009.
This article provides the results of development of bankruptcy prediction static model and its testing on the sample of more than thousand companies of manufacturing industry. The main scenarios of bankruptcy are identified and it is shown that depending on the bankruptcy scenario possible insolvency can be predicted one or four years before.