scholarly journals REASSESSMENT OF CORPORATE BANKRUPTCY PREDICTION MODELS EFFICIENCY: EVIDENCE FROM SERBIA/ПОНОВНА ПРОЦЕНА ПРОГНОСТИЧКЕ МОЋИ МОДЕЛА ЗА ПРЕДВИЂАЊЕ СТЕЧАЈА У РЕПУБЛИЦИ СРБИЈИ

TEME ◽  
2017 ◽  
pp. 1367
Author(s):  
Vule Mizdraković ◽  
Milena Bokić
Keyword(s):  
Prediction Models ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
The Other ◽  
Z Score ◽  
Corporate Bankruptcy ◽  
Statistical Relevance ◽  
The Republic ◽  
Business Entities ◽  
Corporate Bankruptcy Prediction

Having in mind various negative influences that corporate bankruptcy has on the economy of the Republic of Serbia, corporate bankruptcy prediction is of extreme importance. Therefore, the basic motive for writing this paper was an attempt to assess the possibility of forecasting bankruptcy of business entities which operate on the Republic of Serbia's market. We have calculated the already formed M-score, formed based on the data from the financial statements of Serbian business entities. As a comparison models, we have calculated the two most acknowledged Z-score models. The randomly chosen sample consisted of 35 entities in bankruptcy and the same number of non-bankrupt entities. The goal of the research was to reassess the relevance of the tested models for a longer period, as well as their precision in the corporate bankruptcy prediction in an unstable economic environment of the Republic of Serbia. According to the results, the conclusion is that the tested M-score proved its precision in bankruptcy prediction in Serbia, and its use is, therefore, recommended. On the other hand, the Altman’s Z-score models do not have statistical relevance and hence we recommend that their use for bankruptcy prediction in the Republic of Serbia should be with caution.

scholarly journals An overview of bankruptcy prediction models for corporate firms: A Systematic literature review

2019 ◽  
Vol 15 (2) ◽  
pp. 114 ◽  
Author(s):  
Yin Shi ◽  
Xiaoni Li
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Prediction Models ◽  
Global Financial Crisis ◽  
Bankruptcy Prediction ◽  
Corporate Bankruptcy ◽  
Comprehensive Overview ◽  
Innovative Methods ◽  
Working Together ◽  
Corporate Bankruptcy Prediction

Purpose: This paper aims to provide a comprehensive overview of literature related to corporate bankruptcy prediction, to investigate and address the link between different authors (co-authorship), and to identify the primary models and methods that are used and studied by authors of this area in the past five decades.Design/methodology/approach: A systematic literature review (SLR) has been conducted, using the Scopus database for identifying core international academic papers related to the established research topic from the year 1968 to 2017.Findings: It has been verified, firstly, that bankruptcy prediction in the corporate world is a field of growing interest, as the number of papers has increased significantly, especially after 2008 global financial crisis, demonstrating the importance of this topic for corporate firms. Secondly, it should be mentioned that there is little co-authorship in this researching area, as the researchers with a lot of influence were basically not working together during the last five decades. Thirdly, it has been identified that the two most frequently used and studied models in bankruptcy prediction area are Logistic Regression (Logit) and Neural Network. However, there are many other innovative methods as machine learning models applied in this field lately due to the emerging technology of computer science and artificial intelligence.Originality/value: We applied the SLR approach that allows a better view of the academic contribution related to the corporate bankruptcy prediction; this contributes as the link among different elements of the concept studied, and it demonstrates the growing interest in this area.

scholarly journals ANALISIS FINANCIAL DISTRESS DENGAN MENGGUNAKAN BANKRUPTCY PREDICTION MODELS PADA EMITEN ROKOK PERIODE 2014-2018

2021 ◽  
Vol 4 (1) ◽  
pp. 16-27
Author(s):  
Ani Wahyuningsih ◽  
Hartono Hartono ◽  
Rini Armin
Keyword(s):  
Early Warning ◽  
Financial Distress ◽  
Prediction Models ◽  
Stock Exchange ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Correct Prediction ◽  
Financial Health ◽  
Z Score ◽  
Financial Difficulties

ABSTRACT Financial Distress is a condition of financial difficulties where if this happens to the company foa along period of time, the company is in the initial stages before bankruptcy. Bankruptcy is a state of being or a situation in which company failed to or not able to meet obligations because firm experienced lack of. If the company goes bankrupt there will be many parties who are harmed. Therefore it is necessary to conduct financial distress analysis for early warning. The research aims to determine the financial health of the cigarette sub-sector companies by analyzing financial distress using three bankruptcy prediction models with Altman Z-Score, Springate, Grover and to determine which of these three models has the highest level of accuracy. The data used in this research is the company’s financial statements published on the Indonesia Stock Exchange website. The population in this research is the cigarette sub-sector companies listed on the Indonesia Stock Exchange in the 2014-2018 period. Based on the result of research shows that in the calculation Altman and Springate models, PT. Bentoel International Investama in the category of the company experiencing symptoms of bankruptcy. While in the Grover model calculation, all companies fall into category healthy companies. Of the three models that have the highest level of accuracy are Altman and Springate models by one hundred percent. This shows that Altman and Springate models have the correct prediction of the company correctly.

scholarly journals Corporate bankruptcy prediction in the Republic of Serbia

Industrija ◽  
2013 ◽  
Vol 41 (4) ◽  
pp. 145-159 ◽  
Author(s):  
Stanisic Nemanja ◽  
Mizdrakovic Vule ◽  
Knezevic Goranka
Keyword(s):  
Bankruptcy Prediction ◽  
Corporate Bankruptcy ◽  
The Republic ◽  
Corporate Bankruptcy Prediction
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