Abstract

Research Question: This research has been conducted with the aim of determining whether it is possible to generate a model that can reliably predict bankruptcy of Serbian small and medium-sized enterprises (SMEs) using Working Capital Management (WCM) and Asset Management (AM) efficiency ratios. Motivation: Motive for this research is the fact there are not many business failure prediction models related to SMEs. In addition, existing models are not focused on efficiency of the two above mentioned categories. Also, previously developed models, especially traditional and ground-breaking ones, are not necessarily aligned with accounting procedures and economic environment of all countries, which indicates the need to develop a model that is adapted for Serbian territory. Idea: The idea is to develop a model that has the ability to predict whether an entity has a tendency to initiate bankruptcy proceedings in the next year. This is useful both for external stakeholders and for SMEs’ owners themselves, as it allows them to better manage resources. Data: The research was conducted on a sample of 100 Serbian SMEs. Data for the calculation of ratio indicators is available on the Business Registers Agency webpage. Tools: The research was conducted as a combination of financial and statistical analysis instruments. Ratio indicators were used for financial analysis part, while statistical analysis was conducted in SPSS program v.26 and includes logistic (binary) regression. Findings: Results of the research indicate that AM efficiency indicators are suitable for SMEs bankruptcy prediction modelling, but also indicate that WCM ratios don’t have great contribution in bankruptcy prediction for Serbian SMEs. A model that has a classification accuracy of 79% has been developed. Contribution: This research empirically tests how selected ratio indicators support SMEs bankruptcy prediction, and therefore can be beneficial for all SMEs stakeholders, but also other researches since the research methodology is explained in details.

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