Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries

Prusak, Błażej

doi:10.3390/ijfs6030060

Cited by 52 publications

(58 citation statements)

References 87 publications

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“…-the changes concerning the corporate governance in the menace of bankruptcy, -the costs of bankruptcy, -the prices of shares and the long-term results, -the changes in the legal regulations concerning bankruptcy. Many of the research studies were focused on the forecast of the bankruptcy of enterprises (Altman & Hotchkiss, 2006;Prusak, 2018;Durica et al, 2019aDurica et al, , 2019bKliestik et al, 2018).…”

Section: The Overview Of the Literaturementioning

confidence: 99%

The Analysis of the Bankruptcy of Enterprises Exemplified by the Visegrad Group

Krajewski

Tokarski

2020

Journal of Business Economics and Management

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The phenomenon of enterprises bankruptcy is an extremely complex process of economic, legal, social and even psychological nature. In the developed countries, the first studies on forecasting bankruptcy date to the early 20th century. In Central and Eastern Europe, due to, among other factors, the geopolitical situation and the introduced economic system, this issue became the subject of researchers’ interest only in the 1990s. The universality and complexity of the phenomenon incline the Authors to make an attempt to identify the scale of the bankruptcy processes in the states of the Visegrad Group (V4). The countries of V4 were selected because of their common history and similar economic development. The aim of this article is to analyze the dynamics of the phenomenon of bankruptcy of enterprises in the states of the V4 Group in the years 2005–2016 on the basis of the data taken from the Credit reform reports. The estimation of trend models in time series on the basis of the bankruptcy in the Visegrad Group countries in the years 2005–2016 was used in the article as the primary research method. The descriptive analysis was also applied in the article.

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Section: The Overview Of the Literaturementioning

confidence: 99%

The Analysis of the Bankruptcy of Enterprises Exemplified by the Visegrad Group

Krajewski

Tokarski

2020

Journal of Business Economics and Management

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“…During past decades, some literature has reviewed corporate failure forecasting in detail, such as Sun et al [5], Alaka et al [20], Prusak [32], etc. Here, we briefly review more recent literature of corporate failure forecasting (shown as Table A1) and summarize the recent development as follows.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To achieve a better forecasting performance, integrated models, which employ individual models as basis classifiers, have become a new exploring trend. A great deal of literature has demonstrated how to integrate basis classifiers for corporate failure forecasting [5,32]. To date, integrated models can be divided into two groups.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Furthermore, considering the big challenge in forecasting textual data and numerical data simultaneously (due to their widely different features), we believe that an effective way to get a better forecasting performance is by adopting a model to forecast with textual data and numerical data respectively, as shown as Figure 1. demonstrated how to integrate basis classifiers for corporate failure forecasting [5,32]. To date, integrated models can be divided into two groups.…”

Section: The Proposed Nimmentioning

confidence: 99%

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Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine

Pan

Chen

et al. 2019

Energies

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Accurate forecasts of corporate failure in the Chinese energy sector are drivers for both operational excellence in the national energy systems and sustainable investment of the energy sector. This paper proposes a novel integrated model (NIM) for corporate failure forecasting in the Chinese energy sector by considering textual data and numerical data simultaneously. Given the feature of textual data and numerical data, convolutional neural network oriented deep learning (CNN-DL) and support vector machine (SVM) are employed as the base classifiers to forecast using textual data and numerical data, respectively. Subsequently, soft set (SS) theory is applied to integrate outputs of CNN-DL and SVM. Hence, NIM inherits advantages and avoids disadvantages of CNN-DL, SVM, and SS. It is able to improve the forecasting performance by taking full use of textual data and numerical data. For verification, NIM is applied to the real data of Chinese listed energy firms. Empirical results indicate that, compared with benchmarks, NIM demonstrates superior performance of corporate failure forecasting in the Chinese energy sector.

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“…This different approach used by SMEs causes the popularity of basic statistical techniques to remain unchanged in daily practice and a number of scientific papers can be found as well. Prusak (2018) or Klieštik et al (2018) provide an overview of the research conducted in selected central and eastern European countries. Research carried out in the area of the Czech Republic involves the works by Karas and Režňáková (2013), Klečka and Scholleová (2010), Čámská (2015, 2016), Machek (2014), and Pitrová (2011).…”

Section: Introductionmentioning

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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Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries

Cited by 52 publications

References 87 publications

The Analysis of the Bankruptcy of Enterprises Exemplified by the Visegrad Group

The Analysis of the Bankruptcy of Enterprises Exemplified by the Visegrad Group

Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine

Comparison of Prediction Models Applied in Economic Recession and Expansion

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