Financial Health of Enterprises Introducing Safeguard Procedure Based on Bankruptcy Models

Schönfeld, Jaroslav; Kuděj, Michal; Smrčka, Luboš

doi:10.3846/jbem.2018.7063

Cited by 18 publications

(15 citation statements)

References 15 publications

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“…According to Aruldoss et al (2015), Cultrera and Bredart (2016), Salehi and Pour (2016), the information rendered by bankruptcy forecasting models is bounded to separate industries. Alaka et al (2018), Schonfeld et al (2018), Svabova and Kliestik (2018), and Slefendorfas (2016) believe that the traditional bankruptcy prediction models are not suited to analyzing modern enterprises because the dynamic macroeconomic environment and business are interdependent. As a result, different economic environments possess diverse characteristics that restrict a repeated application of models and sets of related factors under different conditions.…”

Section: Introductionmentioning

confidence: 99%

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

et al. 2019

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Different economic environments differ in their characteristics; this prevents the usage of the same bankruptcy prediction models under different conditions. Objectively, the abundance of bankruptcy prediction models gives rise to the idea that these models are not in compliance with the changing business conditions in the market and do not meet the increasing complexity of business tasks. The purpose of this study is to assess the suitability of existing bankruptcy prediction models and the possibilities to increase the effectiveness of their application. In order to analyze theoretical aspects of the application of bankruptcy forecasting models and frame the research methodology, a systemic comparative and logical analysis of the scientific literature and statistical data, graphic data representation, induction, deduction and abstraction are employed. Results of the analysis confirm research hypotheses that bankruptcy prediction models based on macroeconomic variables are effective in identifying the number of corporate bankruptcies in a country and that the application of the model created on the grounds of macroeconomic indicators together with the traditional bankruptcy prediction model can improve the reliability of bankruptcy prediction. However, it was identified that models which are not specially adapted to companies in the construction sector are also suitable for forecasting their bankruptcies.

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Section: Introductionmentioning

confidence: 99%

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

et al. 2019

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“…A section in this contribution is dedicated to the potential application of LSTM to the management of financial risks or to the prediction of company bankruptcy. Despite this, many authors continue to work with traditional bankruptcy models, such as Altman Z-Score, Kralicek Quick Test, IN 99, IN05 [53], hybrid models of classification and regression trees (CART), multivariate adaptive regression spline (MARS) models [54], or evolutionary dynamics and the optimization of the strategy for certain types of interconnected evolutionary games [55]. In other cases, for the verification of statistical bankruptcy models, the Monte Carlo method is used [56].…”

Section: Literature Reviewmentioning

confidence: 99%

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

2020

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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.

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“…Moyer (2005) compares corporate financial distress to the situation when the box of assets becomes smaller than the box of debts. Using this approach, the enterprises are distinguished through their over indebtedness, such as in Schönfeld et al (2018). Insolvency is mostly connected with the inability to pay debts, which can be short or long-termed (Crone and Finlay 2012;Deakin 1972;Du Jardin 2017;Foster 1986).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Prediction Models Applied in Economic Recession and Expansion

Čámská

Klečka

2020

JRFM

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As a rule, the economy regularly undergoes various phases, from a recession up to expansion. This paper is focused on models predicting corporate financial distress. Its aim is to analyze impact of individual phases of the economic cycle on final scores of the prediction models. The prediction models may be used for quick, inexpensive evaluation of a corporate financial situation leading to business risk mitigation. The research conducted is drawn from accounting data extracted from the prepaid corporate database, Albertina. The carried-out analysis also highlights and examines industry specifics; therefore, three industry branches are under examination. Enterprises falling under Manufacture of metal products, Machinery, and Construction are categorized into insolvent and healthy entities. In this study, 18 models are selected and then applied to the business data describing recession and expansion. The final scores achieved are summarized by the main descriptive statistics, such as mean, median, and trimmed mean, followed by the absolute difference comparing expansion and recession. The results confirm the expectations, assuming that final scores with higher values describe better corporate financial standing during the expansion phase. Similar results are achieved for both healthy and insolvent enterprises. The paper highlights exceptions and offers possible interpretations. As a conclusion, it is recommended that users need to respect the current phase of the economic cycle when interpreting particular results of the prediction models.

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Financial Health of Enterprises Introducing Safeguard Procedure Based on Bankruptcy Models

Cited by 18 publications

References 15 publications

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

Comparison of Prediction Models Applied in Economic Recession and Expansion

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