| Abstract [eng] |
92 pages, 5 pictures, 22 tables, 44 references. The main purpose of this master’s thesis is to assess the suitability of bankruptcy prediction models for Lithuanian companies operating in the wholesale and retail sector. The work consists of three main parts: the analysis of literature, methodology for conducting research, the research and its results, conclusion, and recommendations. The analysis of the scientific literature has shown that the recent global crises have severely damaged economies, and that during the COVID-19 pandemic, industrial slowdowns and restrictions have severely damaged businesses, some of which went bankrupt. The energy crisis has also contributed to the financial difficulties of Lithuanian companies. Almost every company is exposed to the impact and consequences of the crisis, and for all these reasons, there is a growing demand for solutions that can help predict the risk of bankruptcy. Various financial analysis techniques can be used to monitor a company's financial situation, and bankruptcy prediction tools can be used as an early warning system to detect impending financial distress. The main tool for bankruptcy prediction is bankruptcy prediction models. These are valuable analytical tools that can be used to assess the financial distress of a company and to predict the probability of bankruptcy. The performed research revealed that the logistic regression models showed opposite results, with the Chesser model being the most accurate predictor of bankruptcies, while Zavgren was rejected as completely inappropriate for predicting bankruptcies in the wholesale and retail sector. The quality of financial statements and data is crucial for estimating the probability of bankruptcy using logistic regression models. It has been observed that the probability of bankruptcy cannot be calculated without some financial indicators. The conclusions and suggestions summarize the main results of the literature and empirical research and offer recommendations. |
