A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction

Abid, Ilyes; Ayadi, Rim; Guesmi, Khaled; Mkaouar, Farid

doi:10.1007/s10479-021-04236-4

Cited by 12 publications

(2 citation statements)

References 33 publications

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“…Typically, the first group of researchers uses a data-level approach, and mostly uses the Synthetic Minority Oversampling Technique (SMOTE) [24,34,54,93,[137][138][139], or the random sampling (undersampling) [77,145] or random sampling with the matching parameter (sector, size, etc.) [50,[140][141][142][143], also called stratified sampling [144].…”

Section: Hybrid IImentioning

confidence: 99%

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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Section: Hybrid IImentioning

confidence: 99%

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Kuizinienė,

Krilavičius

2024

Electronics

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“…Dube et al (2021) used ANN to develop prediction models for financial services and manufacturing companies listed on the Johannesburg Stock Exchange for the period 2000-2019. Abid et al (2022) used neural networks combined with 30 financial ratios to predict bankruptcy for 856 French companies from the industrial sector. Other authors who deal with bankruptcy prediction using a neural network include Tsai and Wu (2008), Tsai (2009), Salehi and Davoudi Pour (2016), Chung et al (2016), andKim et al (2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Gavurová

Jenčová

Bačík

et al. 2022

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Research background: In a modern economy, full of complexities, ensuring a business' financial stability, and increasing its financial performance and competitiveness, has become especially difficult. Then, monitoring the company's financial situation and predicting its future development becomes important. Assessing the financial health of business entities using various models is an important area in not only scientific research, but also business practice. Purpose of the article: This study aims to predict the bankruptcy of companies in the engineering and automotive industries of the Slovak Republic using a multilayer neural network and logistic regression. Importantly, we develop a novel an early warning model for the Slovak engineering and automotive industries, which can be applied in countries with undeveloped capital markets. Methods: Data on the financial ratios of 2,384 companies were used. We used a logistic regression to analyse the data for the year 2019 and designed a logistic model. Meanwhile, the data for the years 2018 and 2019 were analysed using the neural network. In the prediction model, we analysed the predictive performance of several combinations of factors based on the industry sector, use of the scaling technique, activation function, and ratio of the sample distribution to the test and training parts. Findings & value added: The financial indicators ROS, QR, NWC/A, and PC/S reduce the likelihood of bankruptcy. Regarding the value of this work, we constructed an optimal network for the automotive and engineering industries using nine financial indicators on the input layer in combination with one hidden layer. Moreover, we developed a novel prediction model for bankruptcy using six of these indicators. Almost all sampled industries are privatised, and most companies are foreign owned. Hence, international companies as well as researchers can apply our models to understand their financial health and sustainability. Moreover, they can conduct comparative analyses of their own model with ours to reveal areas of model improvements.

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Feature Selection in Corporate Bankruptcy Prediction Using ML Techniques: A Systematic Literature Review

Shakeel

Siddiqui

Alam³

2023

Lecture Notes in Electrical Engineering

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A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction

Cited by 12 publications

References 33 publications

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Feature Selection in Corporate Bankruptcy Prediction Using ML Techniques: A Systematic Literature Review

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