Systematic review of bankruptcy prediction models: Towards a framework for tool selection

Alaka, Hafiz; Oyedele, Lukumon O.; Owolabi, Hakeem A.; Kumar, Vikas; Ajayi, Saheed O.; Akinadé, Olúgbénga O.; Bilal, Muhammad

doi:10.1016/j.eswa.2017.10.040

Cited by 208 publications

(167 citation statements)

References 142 publications

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“…According to Aruldoss et al (2015), Cultrera and Bredart (2016), Salehi and Pour (2016), the information rendered by bankruptcy forecasting models is bounded to separate industries. Alaka et al (2018), Schonfeld et al (2018), Svabova and Kliestik (2018), and Slefendorfas (2016) believe that the traditional bankruptcy prediction models are not suited to analyzing modern enterprises because the dynamic macroeconomic environment and business are interdependent. As a result, different economic environments possess diverse characteristics that restrict a repeated application of models and sets of related factors under different conditions.…”

Section: Introductionmentioning

confidence: 99%

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

et al. 2019

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Different economic environments differ in their characteristics; this prevents the usage of the same bankruptcy prediction models under different conditions. Objectively, the abundance of bankruptcy prediction models gives rise to the idea that these models are not in compliance with the changing business conditions in the market and do not meet the increasing complexity of business tasks. The purpose of this study is to assess the suitability of existing bankruptcy prediction models and the possibilities to increase the effectiveness of their application. In order to analyze theoretical aspects of the application of bankruptcy forecasting models and frame the research methodology, a systemic comparative and logical analysis of the scientific literature and statistical data, graphic data representation, induction, deduction and abstraction are employed. Results of the analysis confirm research hypotheses that bankruptcy prediction models based on macroeconomic variables are effective in identifying the number of corporate bankruptcies in a country and that the application of the model created on the grounds of macroeconomic indicators together with the traditional bankruptcy prediction model can improve the reliability of bankruptcy prediction. However, it was identified that models which are not specially adapted to companies in the construction sector are also suitable for forecasting their bankruptcies.

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Section: Introductionmentioning

confidence: 99%

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

et al. 2019

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“…The systematic review of bankruptcy prediction models is processed in the studies of Alaka et al [25] or Peres and Antao [26]. The reviews show that there are two groups of popular and promising tools within the bankruptcy prediction models research area, i.e., statistical tools (multiple discriminant analysis and logistic regression) and artificial intelligence tools (decision trees, neural networks, etc.).…”

Section: Literature Reviewmentioning

confidence: 99%

Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis

et al. 2018

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Abstract:The issue of the debt, bankruptcy or non-bankruptcy of a company is presented in this article as one of the ways of conceiving risk management. We use the Amadeus database to obtain the financial and accounting data of Slovak enterprises from 2015 and 2016 to calculate the most important financial ratios that may affect the financial health of the company. The main aim of the article is to reveal financial risks of Slovak entities and to form a prediction model, which is done by the identification of significant predictors having an impact on the health of Slovak companies and their future prosperity. Realizing the multiple regression analysis, we identified the significant predictors in conditions of the specific economic environment to estimate the corporate prosperity and profitability. The results gained in the research are extra important for companies themselves, but also for their business partners, suppliers and creditors to eliminate financial and other corporate risks related to the unhealthy or unfavorable financial situation of the company.

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“…The FPMs (see a comprehensive review of FPM studies in [42]). This led to the tuning of nnet (ANN) parameters, towards achieving the first objective of this study.…”

Section: Evaluation Criteriamentioning

confidence: 99%

A Big Data Analytics Approach for Construction Firms Failure Prediction Models

Alaka

Oyedele

Owolabi

et al. 2019

IEEE Trans. Eng. Manage.

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Systematic review of bankruptcy prediction models: Towards a framework for tool selection

Cited by 208 publications

References 142 publications

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis

A Big Data Analytics Approach for Construction Firms Failure Prediction Models

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