Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets

Zoričak, Martin; Gnip, Peter; Drotár, Peter; Gazda, Vladimír

doi:10.1016/j.econmod.2019.04.003

Cited by 73 publications

(44 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…there are only a few bankrupt companies present) or the population of the data for companies is extremely low. This could by one of the reasons, why the use of ANN models has not been significantly developed in Slovakia, with a few notable exceptions (for example, Boďa, 2009;Cút, 2013;Mihalovič, 2016;Valášková et al, 2018;Zoričák et al (2020). The claim is true despite the introduction of the national register of financial statements in 2013 by Slovak law, making financial statements for almost any company in the country publicly available for the external users.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Bankruptcy of Slovak Companies Using Neural Networks with SMOTE

Tumpach¹,

Surovičová²,

Juhászová³

et al. 2020

ekoncas

View full text Add to dashboard Cite

Although the bankruptcy prediction models can be a stabilizing element on both macro and microeconomic levels, they are rather a domain of academic research than an instrument, widely applied in a business practice. It is especially true if the models are reflecting the conditions of countries of their origin, rather than countries of their intended uses. Besides, few of the models contain inherent flaws, including the absence of a methodical approach addressing this problem of the severely imbalanced representation of bankrupt companies in financial datasets. The article is focused on the use of oversampling with SMOTE (Synthetic Minority Oversampling Technique) algorithm under the condition of extremely imbalanced data sets of Slovak companies. While the model does not provide a single answer in many (if not most) of the situations, it still could be used for the selection of companies for which the more detailed (and expensive) analysis is not required.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of the Bankruptcy of Slovak Companies Using Neural Networks with SMOTE

Tumpach¹,

Surovičová²,

Juhászová³

et al. 2020

ekoncas

View full text Add to dashboard Cite

show abstract

“…The prediction of bankruptcy or the prediction of financial distress has been a muchdiscussed topic in various countries around the world for several decades [64,65] and at the same time it is an essential and widely studied topic [66,67], which is receiving special attention [68]. Due to the instability of the economic environment, costs increase, and certain problems arise both for businesses and other parties, which are dependent either directly or indirectly on the activities or the very existence of these businesses, so it is for these stakeholders (business owners, potential investors, management, employees, creditors, government and others) important to monitor whether business is at risk of bankruptcy or not [69].…”

Section: Discussionmentioning

confidence: 99%

The Impact of Equity Information as An Important Factor in Assessing Business Performance

Tasáryová¹,

Pakšiová²

2021

Information

View full text Add to dashboard Cite

Assessing the business performance is an important aspect of almost all economic decisions at the microeconomic and macroeconomic level, in the short and long term. Information about the partners' relationship to the business, their interest in the evaluation of investments can be explained by various indicators. It is relevant to understand the dependencies of the business performance and the amount of equity, while negative equity can be considered as critical information of existence. The purpose of quantitative research is to identify the relationship between reported negative equity and the business performance in Slovakia on an exhaustive sample of financial data of businesses with negative equity in the period 2014–2018. The business performance with negative equity is assessed through the Altman Z-score and the IN05 index, by classifying businesses into bankruptcy, prosperity and gray zones. Pearson's correlation analysis between negative equity and Altman Z-score performance confirms the strong direct relationship between negative equity and the bankruptcy zone, the weaker indirect relationship between negative equity and the gray zone, and almost no dependence of negative equity and prosperity zone. In the case of the IN05 index, a low correlation was found between negative equity and all three zones. Although businesses with negative equity are in a bankruptcy zone, they do not have to close automatically, but they have to improve resource management, in particular to increase equity, for example by making a profit and good financial management.

show abstract

“…In the first issue, it is important to bring forward the works of Le et al [18,19], who proposed compensating the unbalanced datasheets when using complex algorithms, due to the fact that there were always many more non-bankrupt companies in the datasets than firms that had gone bankrupt. This concern has also been addressed by Zoričák et al, who used three algorithms in turn [20]. This paper approaches the issue in a different way, addressing it either in the technique or the method, by combining the relatively new survival analysis with a time-fixed data scheme.…”

Section: Models and Techniques Used In Corporate Failure Predictionmentioning

confidence: 97%

Key Ratios for Long-Term Prediction of Hotel Financial Distress and Corporate Default: Survival Analysis for an Economic Stagnation

Peláez-Verdet

Loscertales-Sanchez

2021

Sustainability

View full text Add to dashboard Cite

Hospitality companies often face economic crises, which stress their financial structure. In 2008, Spanish hotels were jeopardized when the travelers’ flows became stagnated, in either domestic and foreign markets. Most of them overcame the crisis, but not all, in part depending on their capital structure at the moment the downturn loomed upon them. This study analyzes the financial ratios registered in 2008 by 3.341 Spanish lodging enterprises, to find out the most relevant ratios that were associated with an eventual breakdown. The analyzed ratios have been largely suggested by previous literature for anticipating financial distress; however, using survival tables and Kaplan–Meier estimates we could also find new insights about several promising variates for future research. In the end, by performing a Cox regression, we could isolate the return on capital employed (ROCE) ratio as a long-term predictor for small hotels’ bankruptcy after a market downturn. Moreover, the legal status seems to be a key predictor concerning medium-sized hotels.

show abstract

Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets

Cited by 73 publications

References 62 publications

Prediction of the Bankruptcy of Slovak Companies Using Neural Networks with SMOTE

Prediction of the Bankruptcy of Slovak Companies Using Neural Networks with SMOTE

The Impact of Equity Information as An Important Factor in Assessing Business Performance

Key Ratios for Long-Term Prediction of Hotel Financial Distress and Corporate Default: Survival Analysis for an Economic Stagnation

Contact Info

Product

Resources

About