Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

Papíková, Lenka; Papík, Mário

doi:10.1002/isaf.1521

Cited by 10 publications

(8 citation statements)

References 76 publications

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“…In addition, the development of adaptability and flexibility of business models can allow entrepreneurs to quickly adjust their strategies according to changing circumstances (Acosta-Ormaechea & Morozumi, 2022). It is also important to implement support policies, such as providing loans at low interest rates, providing financial guarantees or creating special funds to support affected enterprises, which can help reduce liquidity problems and prevent bankruptcy (Papíková & Papík, 2022).…”

Section: Discussionmentioning

confidence: 99%

Problems of legal regulation of bankruptcy during armed conflict

Vereshchahina

2023

SLS

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The relevance of the study is due to the inconsistency of the national legislation of Ukraine on bankruptcy with the current conditions on the territory of the state, which are connected with the full-scale invasion of the Russian Federation. Martial law causes high risks of destruction of medium, small and large business enterprises, in particular in uncontrolled and temporarily occupied territories. Thus, the purpose of the study is to justify the need to improve the national legislation on bankruptcy, increase the efficiency of the mechanisms for the realization of rights, the fulfilment of obligations and the protection of the interests of economic entities in the conditions of military confrontation. The main methods used to write the work: historical, comparative, statistical, method of analysis, synthesis. The results of this work turned out to be the following: the legislation regulating legal relations regarding bankruptcy was investigated, it was found out which subjects of economic activity are at risk of bankruptcy, and the impact of the war on the state of legal regulation of the economic sphere was investigated. A conclusion was made regarding the main shortcomings of the national bankruptcy legislation. The question of the possibility of preserving the debtor’s further reputation and the proportional satisfaction of creditors’ rights by following the bankruptcy procedure, which was developed in accordance with modern conditions, was also investigated. The possibility of compensation for losses from the side of the aggressor country for those enterprises that suffered losses and destruction as a result of Russian military aggression is also indicated. The expediency of introducing a moratorium on bankruptcy and the main disadvantages of this type of restriction are discussed. Further directions for improving and increasing the level of effectiveness of the legally provided mechanisms for the implementation of the bankruptcy procedure in the conditions of war on the territory of Ukraine are proposed. The results of research work can be used as a theoretical basis for further legislative developments in the field of bankruptcy.

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Section: Discussionmentioning

confidence: 99%

Problems of legal regulation of bankruptcy during armed conflict

Vereshchahina

2023

SLS

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“…Class imbalance occurs when one class in a dataset has fewer instances than the other class [6]. Classification models often presume the equal representation of all classes.…”

Section: Balancing Techniquesmentioning

confidence: 99%

“…The utilization of data-level approach combinations is more frequently observed in comparison to algorithm-level combinations, owing to the independent creation of processes from sampling and classifier training, and the ability to utilize a wider range of machine learning algorithms in subsequent analyses. Researchers usually involve random oversampling (ROS), random undersampling (RUS), and SMOTE techniques in the analysis [6,124,132] or analyze the improvements of SMOTE methods among themselves [4,108,121,134]. Veganzones and Séverin (2018) [124] analyzed ROS, RUS, SMOTE, and easy ensemble techniques with different class imbalance ratios and machine learning approaches.…”

Section: Hybrid IImentioning

confidence: 99%

“…It is known that, to overcome the poor performance of the model, data level balancing techniques, e.g., oversampling, undersampling, or hybrid, are often used. Researchers frequently concentrate on eliminating undersampling techniques due to their insufficient ability to provide enough information and their inaccurate reflection of the proportion of companies facing bankruptcy in the actual business environment [6]. However, the main advantage of undersampling techniques is the elimination of redundant information.…”

Section: Introductionmentioning

confidence: 99%

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Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Kuizinienė,

Krilavičius

2024

Electronics

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Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of ML, struggling with this issue. Furthermore, the topic of financial distress holds considerable interest for both academics and practitioners due to the non-determined indicators of condition states. This research focuses on the involvement of balancing techniques according to different FD condition states. Moreover, this research was expanded by implementing ML models and dimensionality reduction techniques. During the course of this study, a Combined FD was constructed using five distinct conditions, ten distinct class balancing techniques, five distinct dimensionality reduction techniques, two features selection strategies, eleven machine learning models, and twelve weighted majority algorithms (WMAs). Results revealed that the highest area under the receiver operating characteristic (ROC) curve (AUC) score was achieved when using the extreme gradient boosting machine (XGBoost) feature selection technique, the experimental max number strategy, the undersampling methods, and the WMA 3.1 weighted majority algorithm (i.e., with categorical boosting (CatBoost), XGBoost, and random forest (RF) having equal voting weights). Moreover, this research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment. These outcomes have been achieved utilizing authentic enterprise data from small and medium Lithuanian enterprises.

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“…ROAAltman[50]; Li and Sun[65]; Hu[88]; Chen and Du[66]; Premachandra et al[89]; Kainulainen et al[67]; Zhou et al[90]; Cultera and Bredart[61]; Zelenkov et al[72]; Volkov et al[74]; Korol[43]; Farooq and Qamar[76]; Shen et al[44]; Tumpach et al[78]; Qian et al[86]; Pavlicko and Mazanec[84] TATR Altman[50]; Platt and Platt[91]; Li and Sun[65]; Chen and Du[66]; Kainulainen et al[67]; Tomczak et al[92]; Rež ňáková and Karas[70]; Lin et al[69]; Zelenkov et al[72]; Du Jardin[93]; Chou et al[73]; Vuković et al[77]; Park et al[81]; Chen et al[31]; Rahman et al[80]; Papíková and Papík[83]; Pavlicko and Mazanec[84] TDTA Šnircová[62]; Platt and Platt[91]; Premachandra et al[89]; Chen and Du[66]; Hu[88]; Yeh et al[94]; Kainulainen et al[67]; Rež ňáková and Karas[70]; Lin et al[69]; Zhou et al[90]; Chou et al[73]; Volkov et al…”

mentioning

confidence: 99%

Bankruptcy Prediction for Sustainability of Businesses: The Application of Graph Theoretical Modeling

Horváthová,

Mokrišová,

Bača

2023

Mathematics

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Various methods are used when building bankruptcy prediction models. New sophisticated methods that are already used in other scientific fields can also be applied in this area. Graph theory provides a powerful framework for analyzing and visualizing complex systems, making it a valuable tool for assessing the sustainability and financial health of businesses. The motivation for the research was the interest in the application of this method rarely applied in predicting the bankruptcy of companies. The paper aims to propose an improved dynamic bankruptcy prediction model based on graph theoretical modelling. The dynamic model considering the causality relation between financial features was built for the period 2015–2021. Financial features entering the model were selected with the use of Domain knowledge approach. When building the model, the weights of partial permanents were proposed to determine their impact on the final permanent and the algorithm for the optimalisation of these weights was established to obtain the best performing model. The outcome of the paper is the improved dynamic graph theoretical model with a good classification accuracy. The developed model is applicable in the field of bankruptcy prediction and is an equivalent sophisticated alternative to already established models.

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Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

Cited by 10 publications

References 76 publications

Problems of legal regulation of bankruptcy during armed conflict

Problems of legal regulation of bankruptcy during armed conflict

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Bankruptcy Prediction for Sustainability of Businesses: The Application of Graph Theoretical Modeling

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