Abstract
The issue of enterprise financial distress represents the actual and interdisciplinary topic for the economic community. The bankrupt is thus one of the major externalities of today’s modern economies, which cannot be avoided even with every effort. Where there are investment opportunities, there are individuals and businesses that are willing to assume their financial obligations and the resulting risks to maintain and develop their standard of living or their economic activities. The decision tree algorithm is one of the most intuitive methods of data mining that can be used for financial distress prediction. Systematization literary sources and approaches prove that decision trees represent the part of the innovations in financial management. The main propose of the research is a possibility of application of a decision tree algorithm for the creation of the prediction model, which can be used in economy practice. The Paper's main aim is to create a comprehensive prediction model of enterprise financial distress based on decision trees, under the conditions of emerging markets. Paper methods are based on the decision tree, with emphasis on algorithm CART. Emerging markets included 17 countries: Slovak Republic, Czech Republic, Poland, Hungary, Romania, Bulgaria, Lithuania, Latvia, Estonia, Slovenia, Croatia, Serbia, Russia, Ukraine, Belarus, Montenegro, and Macedonia. Paper research is focused on the possibilities of implementation of a decision tree algorithm for the creation of a prediction model in the condition of emerging markets. Used data contained 2,359,731 enterprises from emerging markets (30% of total amount); divided into prosperous enterprises (1,802,027) and non-prosperous enterprises (557,704); obtained from Amadeus database. Input variables for the model represented 24 financial indicators, 3 dummy variables, and the countries' GDP data, in the years 2015 and 2016. The 80% of enterprises represented the training sample and 20% test sample, for model creation. The model correctly classified 93.2% of enterprises from both the training and test sample. Correctly classification of non-prosperous enterprises was 83.5% in both samples. The result of the research brings a new model for the identification of bankrupt enterprises. The created prediction model can be considered sufficiently suitable for classifying enterprises in emerging markets. Keywords prediction model, decision tree, emerging markets.
Highlights
2012; Rajnoha and Lorincova, 2015; Afonina, 2015; Zyka and Drahotsky, 2019)
Prediction models can early predict the probability of failure of the business entity
The paper assumption is a possibility of application of a decision tree algorithm for the creation of the prediction model, which can be used in economy practice
Summary
2012; Rajnoha and Lorincova, 2015; Afonina, 2015; Zyka and Drahotsky, 2019). For this reason, it is desirable for an enterprise to continually investigate its financial health; to know its strengths and weaknesses; to know the direction to prevent the enterprise from failing. It should be stressed that decision trees are at the interface between predictive and descriptive method because they create the classification structure of the data set to which they apply. Paper main aim is to create a comprehensive prediction model of enterprise financial distress based on decision trees, under the conditions of emerging markets. Paper consists of five chapters, namely: 1) literature review which includes the brief historical development of decision trees algorithm; 2) methodology which includes the methodology of decision trees creation and methods which were used in the paper; 3) empirical results which include results of empirical research and prediction model of enterprise financial distress based on decision trees; 4) discussion; 5) conclusions
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