The Application of Graphic Methods and the DEA in Predicting the Risk of Bankruptcy

Štefko, Róbert; Horváthová, Jarmila; Mokrišová, Martina

doi:10.3390/jrfm14050220

Cited by 14 publications

(7 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study employs a sophisticated methodology that combines established research approaches with tailored adaptations to assess the efficiency of economic support policies amid the COVID-19 pandemic, with a specific emphasis on their influence on supply chain dynamics. Essential methodologies are adapted from prior studies by [34][35][36].…”

Section: Methodsmentioning

confidence: 99%

Urban Economic Resilience and Supply Chain Dynamics: Evaluating Monetary Recovery Policies in Global Cities during the Early COVID-19 Pandemic

Li,

Fu,

Zhao

2024

Mathematics

View full text Add to dashboard Cite

The COVID-19 pandemic has profoundly impacted global economies, underscoring the urgency of deriving lessons to enhance future crisis preparedness. This study explores the effects of monetary recovery policies on supply chain dynamics across key global cities during the pandemic’s initial phase, emphasising policy interactions, industry engagement, and economic resilience. Utilising principal component analysis (PCA), data envelopment analysis (DEA), and tobit regression, we present a pioneering method to unravel the complex relationship between economic policies and urban supply chains. PCA simplifies data complexity and reveals complex policy-resilience relationships, while DEA facilitates a comparative efficiency analysis. Our findings underscore the critical importance of supply chain resilience in fostering early economic recovery, indicating that cities implementing diverse, sector-specific policies achieved more notable improvements in gross domestic product (GDP). This research not only advances methodological approaches for policy evaluation but also provides valuable insights for optimising urban economic recovery strategies amidst global challenges.

show abstract

Section: Methodsmentioning

confidence: 99%

Urban Economic Resilience and Supply Chain Dynamics: Evaluating Monetary Recovery Policies in Global Cities during the Early COVID-19 Pandemic

Li,

Fu,

Zhao

2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…Sueyoshi and Goto (2009) applied Principal Component Analysis to reduce the number of financial factors in order to reduce the computational burden of the DEA-DA model. Stefko et al (2021) used Principal Component Analysis and Multidimensional Scaling when selecting inputs and outputs for DEA models. Huang et al (2015) selected variables for DEA models based on gray relational analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Domain Knowledge Features versus LASSO Features in Predicting Risk of Corporate Bankruptcy—DEA Approach

Mokrišová,

Horváthová

2023

Risks

Self Cite

View full text Add to dashboard Cite

Predicting the risk of corporate bankruptcy is one of the most important challenges for researchers dealing with the issue of financial health evaluation. The risk of corporate bankruptcy is most often assessed with the use of early warning models. The results of these models are significantly influenced by the financial features entering them. The aim of this paper was to select the most suitable financial features for bankruptcy prediction. The research sample consisted of enterprises conducting a business within the Slovak construction industry. The features were selected using the domain knowledge (DK) approach and Least Absolute Shrinkage and Selection Operator (LASSO). The performance of VRS DEA (Variable Returns to Scale Data Envelopment Analysis) models was assessed with the use of accuracy, ROC (Receiver Operating Characteristics) curve, AUC (Area Under the Curve) and Somers’ D. The results show that the DK+DEA model achieved slightly better AUC and Somers’ D compared to the LASSO+DEA model. On the other hand, the LASSO+DEA model shows a smaller deviation in the number of identified businesses on the financial distress frontier. The added value of this research is the finding that the application of DK features achieves significant results in predicting businesses’ bankruptcy. The added value for practice is the selection of predictors of bankruptcy for the analyzed sample of enterprises.

show abstract

“…Its main idea is to establish a new multi-dimensional coordinate plane, then to project the most variable information onto the axis, on the condition that the number of axes is fewer than the number of variables. Examples of a PCA integrated DEA can be found in Štefko et al ( 2021 ), Liang et al ( 2009 ), Premachandra et al ( 2009 ), Adler and Yazhemsky ( 2010 ), etc.. It has been shown that PCA can improve the discriminant power of the overall analysis (Adler & Yazhemsky, 2010 ).…”

Section: Data and Variable Selectionmentioning

confidence: 99%

Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis

Chen

Tang

2022

Ann Oper Res

View full text Add to dashboard Cite

For decades, the prediction of bank failure has been a popular topic in credit risk and banking studies. Statistical and machine learning methods have been working well in predicting the probability of bankruptcy for different time horizons prior to the failure. In recent years, bank efficiency has attracted much interest from academic circles, where low productivity or efficiency in banks has been regarded as a potential reason for failure. It is generally believed that low efficiency implies low-quality management of the organisation, which may lead to bad performance in the competitive financial markets. Previous papers linking efficiency measures calculated by Data Envelopment Analysis (DEA) to bank failure prediction have been limited to cross sectional analyses. A dynamic analysis with the updated samples is therefore recommended for bankruptcy prediction. This paper proposes a nonparametric method, Malmquist DEA with Worst Practice Frontier, to dynamically assess the bankruptcy risk of banks over multiple periods. A total sample of 4426 US banks over a period of 15 years (2002–2016), covering the subprime financial crisis, is used to empirically test the model. A static model is used as the benchmark, and we introduce more extensions for comparisons of predictive performance. Results of the comparisons and robustness tests show that Malmquist DEA is a useful tool not only for estimating productivity growth but also to give early warnings of the potential collapse of banks. The extended DEA models with various reference sets and orientations also show strong predictive power.

show abstract

The Application of Graphic Methods and the DEA in Predicting the Risk of Bankruptcy

Cited by 14 publications

References 42 publications

Urban Economic Resilience and Supply Chain Dynamics: Evaluating Monetary Recovery Policies in Global Cities during the Early COVID-19 Pandemic

Urban Economic Resilience and Supply Chain Dynamics: Evaluating Monetary Recovery Policies in Global Cities during the Early COVID-19 Pandemic

Domain Knowledge Features versus LASSO Features in Predicting Risk of Corporate Bankruptcy—DEA Approach

Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis

Contact Info

Product

Resources

About