Abstract
There is no doubt that the issue of making a good prediction about a company’s possible failure is very important, as well as complicated. A number of models have been created for this very purpose, of which one, the long short-term memory (LSTM) model, holds a unique position in that it generates very good results. The objective of this contribution is to create a methodology for the identification of a company failure (bankruptcy) using artificial neural networks (hereinafter referred to as “NN”) with at least one long short-term memory (LSTM) layer. A bankruptcy model was created using deep learning, for which at least one layer of LSTM was used for the construction of the NN. For the purposes of this contribution, Wolfram’s Mathematica 13 (Wolfram Research, Champaign, Illinois) software was used. The research results show that LSTM NN can be used as a tool for predicting company failure. The objective of the contribution was achieved, since the model of a NN was developed, which is able to predict the future development of a company operating in the manufacturing sector in the Czech Republic. It can be applied to small, medium-sized and manufacturing companies alike, as well as used by financial institutions, investors, or auditors as an alternative for evaluating the financial health of companies in a given field. The model is flexible and can therefore be trained according to a different dataset or environment.
Highlights
What are the future prospects of a company? Will it survive potential financial distress? Will it show positive development or is it heading towards bankruptcy? According to Tang et al [1], Kliestik et al [2], or Kliestik et al [3], these are key questions that financial institutions must ask themselves prior to making decisions
The second value in the neural networks (NN) structure indicates the number of elements of the vector (1 × n matrix) of the new state of the variables from the long short-term memory (LSTM) layer, i.e., the output of the first hidden layer of the NN
A NN was obtained that, at first sight, is able to predict, with a high probability, the future development of a company operating in the manufacturing sector in the Czech Republic
Summary
What are the future prospects of a company? Will it survive potential financial distress? Will it show positive development or is it heading towards bankruptcy? According to Tang et al [1], Kliestik et al [2], or Kliestik et al [3], these are key questions that financial institutions must ask themselves prior to making decisions. As Alaka et al [11] and Eysenck et al [12] stated, current research in this field is focused on two statistical tools (multiple discriminant analysis and logistic regression) and six artificial intelligence tools (support vector machines, casuistic reasoning, decision trees, genetic algorithms, rough sets and, in particular, artificial neural networks) Their application is logical, especially due to the fact that, according to the results of an extensive study by Barboza et al [13] and Horak et al [14], those models that use machine learning are, on average, 10% more accurate than traditional models created on the basis of statistical methods. It is surprising that there are not many bankruptcy models based on this exceptionally progressive method
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