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.
Analysis of the problem of predicting bankruptcy shows that foreign and domestic models included only internal factors of enterprises. But the same indicators of internal factors in the rapidly changing external environment can lead to bankruptcy, and in others not. External factors are the most dangerous, because the possible influence on them is minimal and the impact of their implementation can be devastating. This paper aims to - on the same factors to assess the impact of the Russian enterprises account macroeconomic indicators (external factors) on the parameters of static models predict a local approximation of the crisis at the plant. To accomplish the purpose of the database was compiled Spark set of 100 companies, including 50 companies officially declared bankrupt in the period 2000-2009 and 50 stable operating companies with a random sample of the same time period. External factors were extracted from the Joint Economic and Social Data Archive . The author compared two data set: (1) microeconomic indicators—money to the total liabilities, retained earnings to total assets, net profit to revenue, Earnings Before Interest and Taxes (EBIT) to assets, net income to equity, net profit to total liabilities, current liabilities to total assets, the totality of short-term and long-term loans to total assets, current assets to current liabilities, assets to revenue, equity to total assets, current assets to revenue; and (2) external factors—index of real gross domestic product (GDP), industrial production index, the index of real cash incomes, an index of real investments, consumer price index, the refinancing rate, unemployment rate, the price of electricity, gas prices, oil price, gas price, dollar to ruble, ruble euro Standard & Poor (S&P) index, the Russian Trading System (RTS) index, and region. The aim of the comparison results paging classes “insolvent” and “non-bankrupt” is achieved using two methods: classification and discrimination. In both methods, computational procedures are realized with the use of algorithms: linear regression, artificial neural network, and genetic algorithm. In the 2-m model, data set includes both internal and external factors. The results showed that the inclusion of only the microeconomic indicators, excluding external factors impedes models about 2 times.
This paper is concerned with stock liquidity as a factor in making capital structure decisions by managers of Russian firms. Although a big number of studies on capital structure occurred over the last few decades, stock liquidity has only recently attracted scholars’ attention as a possible driver for the choice of capital structure. Yet the existing papers are based on data from the developed capital markets. The latter differ substantially from the Russian market in terms of institutional environment and more liquid stocks. Against the background of revisions in the Russian clearing system that are expected to boost liquidity of stocks, this paper gains in currency.
The theoretic mechanisms behind the interplay of stock liquidity and capital structure are discussed in previous studies. Lower stock liquidity is associated with higher transaction costs and informational asymmetry, and thus with higher required return. Therefore it is assumed that the managers aiming at firm value maximization would prefer debt to equity financing in case if stock is not liquid enough. There are also theoretic grounds to expect an opposite impact of capital structure on stock liquidity.