Machine-learning models for bankruptcy prediction: do industrial variables matter?

Bragoli, Daniela; Ferretti, Camilla; Ganugi, Piero; Marseguerra, Giovanni; Mezzogori, Davide; Zammori, Francesco

doi:10.1080/17421772.2021.1977377

Cited by 18 publications

(11 citation statements)

References 57 publications

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“…Mai et al [11] use data mining techniques to extract textual disclosure information such as qualitative discussion, managerial discussion, and analysis to predict financial distress for public companies in the US. Bragoli et al [12] study the Italian firms with XGBoost techniques and find that industrial variables correctly matter in classifying insolvent companies. Zhao et al [13] and Zhang et al [14] both propose the warning tools based on kernel extreme learning machine for financial decision-making.…”

Section: Literature Reviewmentioning

confidence: 99%

Security Warning Model of Chinese Corporate Bond Defaults Based on Deep Neural Networks

Zhang

Chen

et al. 2022

Security and Communication Networks

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The Chinese bond market has achieved rapid development over the years. However, since the “rigid payment” in China was broken in 2014, the number and size of bond defaults have climbed up promptly and caused huge volatility in bond and even stock markets. To better manage and control the risk of the Chinese corporate bonds market, deep learning can be used as a helpful tool to predict the corporate default risk. This paper constructs a security warning model based on deep neural networks after a reasonable selection of characteristic indicators. By comparing Multi-Layer Perceptron (MLP) with the logistic regression method, it is found that MLP is more suitable in the security warning model to predict the default risk. And, hyper-parameter analyses and ablation study are conducted to explore the performance and accuracy of the best MLP settings. The experimental results show that the deep learning method accommodating the widely chosen factors in the security warning model is effective in predicting corporate bond defaults in China.

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Section: Literature Reviewmentioning

confidence: 99%

Security Warning Model of Chinese Corporate Bond Defaults Based on Deep Neural Networks

Zhang

Chen

et al. 2022

Security and Communication Networks

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“…Some authors apply the decision trees method (Aoki and Hosonuma 2004;Zibanezhad et al 2011;Begović and Bonić 2020), some others utilize various machine learning techniques such as genetic algorithm (Shin and Lee 2002;Kim and Han 2003;Davalos et al 2014), support vector machine (Shin et al 2005;Härdle et al 2005;Dellepiane et al 2015) and random forest (Joshi et al 2018;Ptak-Chmielewska and Matuszyk 2020;Gurnani et al 2021). Recently, several comparative analyses of machine learning models have been carried out to predict bankruptcy (Narvekar and Guha 2021;Park et al 2021;Bragoli et al 2022;Máté et al 2023;Martono and Ohwada 2023).…”

Section: Related Literaturementioning

confidence: 99%

Artificial Intelligence Techniques for Bankruptcy Prediction of Tunisian Companies: An Application of Machine Learning and Deep Learning-Based Models

Hamdi,

Mestiri,

Arbi

2024

JRFM

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The present paper aims to compare the predictive performance of five models namely the Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Trees (DT), Support Vector Machine (SVM) and Random Forest (RF) to forecast the bankruptcy of Tunisian companies. A Deep Neural Network (DNN) model is also applied to conduct a prediction performance comparison with other statistical and machine learning algorithms. The data used for this empirical investigation covers 25 financial ratios for a large sample of 732 Tunisian companies from 2011–2017. To interpret the prediction results, three performance measures have been employed; the accuracy percentage, the F1 score, and the Area Under Curve (AUC). In conclusion, DNN shows higher accuracy in predicting bankruptcy compared to other conventional models, whereas the random forest performs better than other machine learning and statistical methods.

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“…They also suggested an innovative method capable of detecting emerging risks within particular sectors and industries, thus aiding in the identification of market segments where firms remain undisturbed by disruptions [39]. Bragoli et al also developed a predictive model that classified solvent and bankrupt firms, utilizing the enhanced gradient boosting machine learning algorithm [40].…”

Section: Literature Reviewmentioning

confidence: 99%

Risk Analysis of Bankruptcy in the U.S. Healthcare Industries Based on Financial Ratios: A Machine Learning Analysis

Gholampoor,

Asadi

2024

JTAER

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The prediction of bankruptcy risk poses a formidable challenge in the fields of economics and finance, particularly within the healthcare industry, where it carries significant economic implications. The burgeoning field of healthcare electronic commerce, continuously evolving through technological advancements and changing regulations, introduces additional layers of complexity. We collected financial data from 1265 U.S. healthcare industries to predict bankruptcy based on 40 financial ratios using multi-class classification machine learning models across various industry subsectors and market capitalizations. The exceptionally high post-tuning accuracy rates, exceeding 90%, along with high-performance metrics solidified the robustness and exceptional predictive capability of the gradient boosting model in bankruptcy prediction. The results also demonstrate the power and sensitivity of financial ratios in predicting bankruptcy based on financial ratios. The Altman models highlight the return on investment (ROI) as the most important parameter for predicting bankruptcy risk in healthcare industries. The Ohlson model identifies return on assets (ROA) as an important ratio specifically for predicting bankruptcy risk within industry subsectors. Furthermore, it underscores the significance of both ROA and the enterprise value to earnings before interest and taxes (EV/EBIT) ratios as important parameters for predicting bankruptcy based on market capitalization. Recognizing these ratios enables proactive decision making that enhances resilience. Our findings contribute to informed risk management strategies, allowing for better management of healthcare industries in crises like those experienced in 2022 and even on a global scale.

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Machine-learning models for bankruptcy prediction: do industrial variables matter?

Cited by 18 publications

References 57 publications

Security Warning Model of Chinese Corporate Bond Defaults Based on Deep Neural Networks

Security Warning Model of Chinese Corporate Bond Defaults Based on Deep Neural Networks

Artificial Intelligence Techniques for Bankruptcy Prediction of Tunisian Companies: An Application of Machine Learning and Deep Learning-Based Models

Risk Analysis of Bankruptcy in the U.S. Healthcare Industries Based on Financial Ratios: A Machine Learning Analysis

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