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 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 Evaluating risk prediction models for adults with heart failure: A systematic literature review

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0224135 ◽  
Author(s):  
Gian Luca Di Tanna ◽  
Heidi Wirtz ◽  
Karen L. Burrows ◽  
Gary Globe
Keyword(s):  
Heart Failure ◽  
Literature Review ◽  
Risk Prediction ◽  
Systematic Literature Review ◽  
Prediction Models ◽  
Risk Prediction Models

scholarly journals Rethinking SME default prediction: a systematic literature review and future perspectives

2021 ◽  
Author(s):  
Francesco Ciampi ◽  
Alessandro Giannozzi ◽  
Giacomo Marzi ◽  
Edward I. Altman
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Global Financial Crisis ◽  
Analytical Techniques ◽  
Future Research ◽  
Finance Management ◽  
Default Prediction ◽  
Emerging Issues ◽  
Medium Enterprise ◽  
Modern Analytical

AbstractOver the last dozen years, the topic of small and medium enterprise (SME) default prediction has developed into a relevant research domain that has grown for important reasons exponentially across multiple disciplines, including finance, management, accounting, and statistics. Motivated by the enormous toll on SMEs caused by the 2007–2009 global financial crisis as well as the recent COVID-19 crisis and the consequent need to develop new SME default predictors, this paper provides a systematic literature review, based on a statistical, bibliometric analysis, of over 100 peer-reviewed articles published on SME default prediction modelling over a 34-year period, 1986 to 2019. We identified, analysed and reviewed five streams of research and suggest a set of future research avenues to help scholars and practitioners address the new challenges and emerging issues in a changing economic environment. The research agenda proposes some new innovative approaches to capture and exploit new data sources using modern analytical techniques, like artificial intelligence, machine learning, and macro-data inputs, with the aim of providing enhanced predictive results.

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