Dynamic Bankruptcy Prediction Models for European Enterprises

Abstract

This manuscript is devoted to the issue of forecasting corporate bankruptcy. Determining a firm’s bankruptcy risk is one of the most interesting topics for investors and decision-makers. The aim of the paper is to develop and to evaluate dynamic bankruptcy prediction models for European enterprises. To conduct this objective, four forecasting models are developed with the use of four different methods—fuzzy sets, recurrent and multilayer artificial neural network, and decision trees. Such a research approach will answer the question of whether changes in indicators are relevant predictors of a company’s coming financial crisis because declines or increases in values do not immediately indicate that the company’s economic situation is deteriorating. The research relies on two samples of firms—the learning sample of 50 bankrupt and 50 non-bankrupt enterprises and the testing sample of 250 bankrupt and 250 non-bankrupt firms.

Financial Distress Prediction and Feature Selection in Multiple Periods by Lassoing Unconstrained Distributed Lag Non-linear Models

“…One of the main limitations of this study is that we limited the research only to the listed manufacturing companies. Both Korol et al [28] and Kovacova [30] emphasized that the type of industry affects the risk of deterioration in the financial situation of companies. More specifically, distinguished by factors such as intensity of competition, life cycle of products, demand, changes in consumer preferences, technological change, reducing entry barriers into the industry and susceptibility of the industry to business cycles, different industries are at different levels of risk [28].…”

“…Both Korol et al [28] and Kovacova [30] emphasized that the type of industry affects the risk of deterioration in the financial situation of companies. More specifically, distinguished by factors such as intensity of competition, life cycle of products, demand, changes in consumer preferences, technological change, reducing entry barriers into the industry and susceptibility of the industry to business cycles, different industries are at different levels of risk [28]. The manufacturing sector, which includes the metal, mining, automotive, aerospace and housing industries, is highly susceptible to demands, technological changes and macroeconomic conditions, thus making it at a high level of risk, while agriculture may be at a relatively low risk level.…”

“…The authors of [7] introduced 31 financial indicators and found that the most important financial indicators may be related to net profit, earnings before income tax, cash flow and net assets. Along this line, with machine learning methods developing, more diversified financial indicators have been considered in very recent studies of Hosaka [27], Korol et al [28], and Gregova et al [29].…”

Influence of age on selected parameters of insolvent companies

“…This distribution is positively skewed and could be described as in-verted U. The visualization according to Korol (2019) is available in the Discussion section. This theoretical model has become an inspiration for Cressey (2006) and Kücher et al (2018).…”

“…Nehrebecka (2011) conducted the research of corporate life expectancy in Poland. Korol (2019), who focuses on corporate bankruptcy forecasting in Poland, also addressed this issue. Lukason (2018) analyzed dependencies of bankruptcies on corporate age and size in Estonia.…”