Innovation Management of the Bankruptcy: Case Study of Visegrad Group Countries

Kováčová, Mária; Valášková, Katarína; Ďurana, Pavol; Klieštiková, Jana

doi:10.21272/mmi.2019.4-19

Cited by 6 publications

(8 citation statements)

References 32 publications

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“…In total, 14% of publications deal with the predictors’ analysis (Karas & Režňáková, 2017; Kliestik, Valaskova, Lazaroiu, Kovacova, & Vrbka, 2020), methods’ comparison (Barboza, Kimura, & Altman, 2017), comparison of existing models (Kovacova et al. , 2019a, b), or comparison of models’ performance or efficiency (Altman, Iwanicz-Drozdowska, Laitinen, & Suvas, 2020; Liang, Lu, Tsai, & Shih, 2016).…”

Section: Resultsmentioning

confidence: 99%

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A state-of-the-art appraisal of bankruptcy prediction models focussing on the field’s core authors: 2010–2022

Soukal,

Mačí,

Trnková

et al. 2023

CEMJ

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PurposeThe primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.Design/methodology/approachThe authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.FindingsThis paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.Research limitations/implicationsThe authors identify several gaps in the literature which this research does not address but could be the focus of future research.Practical implicationsThe authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.Social implicationsBankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.Originality/valueTo the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

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Section: Resultsmentioning

confidence: 99%

“…primary groups of financial indicators (Kovacova et al. , 2019a, b). Multiple discriminant analysis: it aims to understand group differences and predict the likelihood that an entity (individual or object) will belong to a particular class or group based on several metric-independent variables (Hair, 2014).…”

Section: Methodsmentioning

confidence: 99%

A state-of-the-art appraisal of bankruptcy prediction models focussing on the field’s core authors: 2010–2022

Soukal,

Mačí,

Trnková

et al. 2023

CEMJ

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“…Despite the existence of many alternative models that have been created using a variety of methodologies to obtain the best results, many authors claim that it is still challenging to estimate bankruptcy risk. Nowadays, there are several prediction models developed globally that describe the conditions of the business environment (Hu, 2020;Kliestik et al, 2020;Kovacova et al, 2019;Kou et al, 2021;Valaskova et al, 2020;Zhang et al, 2021;and many others).…”

Section: Literature Reviewmentioning

confidence: 99%

Bankruptcy Prediction Model Development and its Implications on Financial Performance in Slovakia

Gajdosikova

Valášková

2023

Economics and Culture

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Research purpose. Financial distress being a global phenomenon makes it impact firms in all sectors of the economy and predicting corporate bankruptcy has become a crucial issue in economics. At the beginning of the last century, the first studies aimed to predict corporate bankruptcy were published. In Slovakia, however, several prediction models were developed with a significant delay. The main aim of this paper is to develop a model for predicting bankruptcy based on the financial information of 3,783 Slovak enterprises operating in the manufacturing and construction sectors in 2020 and 2021. Design / Methodology / Approach. A prediction model that uses the appropriate financial indicators as predictors may be developed using multiple discriminant analysis. Multiple discriminant analysis is currently used in prediction model development. In this case, financial health is assessed using several variables that are weighted in order to maximise the difference between the average value calculated in the group of prosperous and non-prosperous firms. When developing a bankruptcy prediction model based on multiple discriminant analysis, it is crucial to determine the independent variables used as primary financial health predictors. Findings. Due to the discriminant analysis results, the corporate debt level of the monitored firms may be regarded as appropriate. Despite the fact that the model identified 215 firms in financial distress due to an insufficient debt level, 3,568 out of 3,783 Slovak enterprises operating in the manufacturing and construction sectors did not have any problems with financing their debts. The self-financing ratio was identified in the developed model as the variable with the highest accuracy. Based on the results, the developed model has an overall discriminant ability of 93% since bankruptcy prediction models require strong discriminating abilities to be used in practice. Originality / Value / Practical implications. The principal contribution of the paper is its application of the latest available data, which could help in more accurate financial stability predictions for firms during the current difficult period. Additionally, this is a ground-breaking research study in Slovakia that models the financial health of enterprises in the post-pandemic period.

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“…According to this, our study is focused on the creation of a new Slovak national model using a real sample of Slovak companies and their financial data from the period of the years 2016 -2018. (Kovacova et al, 2019) We apply a CART algorithm to create this model. The originality of our models lies in the use of real data on more than 100,000 Slovak companies.…”

Section: Literature Reviewmentioning

confidence: 99%