Identifying impact of variables in deep learning models on bankruptcy prediction of construction contractors

Jang, Youjin; Jeong, In-Bae; Cho, Yong

doi:10.1108/ecam-06-2020-0386

Cited by 18 publications

(21 citation statements)

References 33 publications

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“…Siswoyo et al [16] apply a hybrid machine learning model by combining a two-class boosted decision tree and multi-class decision forest to predict financial failure for the Indonesian banking industry. Jang et al [17] identify the impact of input variables by using the long short-term memory recurrent neural network to predict the probability of bankruptcy for the US construction market. Wang [18] uses the combination of an ant colony algorithm and a neural network algorithm to build an early warning system for financial management.…”

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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“…ANNs are sometimes called brain-without-mind models [73]. Recently, machine-learning techniques, especially ANNs, have been widely investigated with respect to bankruptcy prediction, as they have been confirmed as good predictors and classifiers, especially when classifying companies according to their risk of possible bankruptcy [17]. In this research, we applied feedforward ANN with multiple hidden layers.…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, an analysis of the financial health and performance of businesses in the construction industry was performed. This industry is unique because of its high capital intensity, uniqueness of projects, and long-term project periods [16,17]. Companies doing business within it achieve high values of liquidity and have different capital structures [18] compared to businesses from other industries.…”

Section: Introductionmentioning

confidence: 99%

The Application of Machine Learning in Diagnosing the Financial Health and Performance of Companies in the Construction Industry

Horváthová,

Mokrišová,

Schneider

2024

Information

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Diagnosing the financial health of companies and their performance is currently one of the basic questions that attracts the attention of researchers and experts in the field of finance and management. In this study, we focused on the proposal of models for measuring the financial health and performance of businesses. These models were built for companies doing business within the Slovak construction industry. Construction companies are identified by their higher liquidity and different capital structure compared to other industries. Therefore, simple classifiers are not able to effectively predict their financial health. In this paper, we investigated whether boosting ensembles are a suitable alternative for performance analysis. The result of the research is the finding that deep learning is a suitable approach aimed at measuring the financial health and performance of the analyzed sample of companies. The developed models achieved perfect classification accuracy when using the AdaBoost and Gradient-boosting algorithms. The application of a decision tree as a base learner also proved to be very appropriate. The result is a decision tree with adequate depth and very good interpretability.

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“…There are several examples of deep learning in credit risk in recent years, both for consumer default prediction (Addo et al 2018;Dastile and Celik 2021;Gunnarsson et al 2021;Ha et al 2019;Hamori et al 2018;Hjelkrem et al 2022;Kvamme et al 2018;Shen et al 2021;Sirignano et al 2016;Wang et al 2018;Wu et al 2021) and bankruptcy prediction (Hosaka 2019;Jang et al 2021;Mai et al 2019;Shetty et al 2022;Smiti and Soui 2020;Stevenson et al 2021). We observe that most of these studies use a shallow learning approach; e.g., the deep learning algorithms are applied on conventional credit risk data sets where raw data are aggregated (typically by hand by experts) into explanatory variables.…”

Section: A Brief Literature Reviewmentioning

confidence: 99%

The Value of Open Banking Data for Application Credit Scoring: Case Study of a Norwegian Bank

Hjelkrem

Lange

Nesset

2022

JRFM

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Banks generally use credit scoring models to assess the creditworthiness of customers when they apply for loans or credit. These models perform significantly worse when used on potential new customers than existing customers, due to the lack of financial behavioral data for new bank customers. Access to such data could therefore increase banks’ profitability when recruiting new customers. If allowed by the customer, Open Banking APIs can provide access to balances and transactions from the past 90 days before the score date. In this study, we compare the performance of conventional application credit scoring models currently in use by a Norwegian bank with a deep learning model trained solely on transaction data available through Open Banking APIs. We evaluate the performance in terms of the AUC and Brier score and find that the models based on Open Banking data alone are surprisingly effective in predicting default compared to the conventional credit scoring models. Furthermore, an ensemble model trained on both traditional credit scoring data and features extracted from the deep learning model further outperforms the conventional application credit scoring model for new customers and narrows the performance gap between application credit scoring models for existing and new customers. Therefore, we argue that banks can increase their profitability by utilizing data available through Open Banking APIs when recruiting new customers.

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Identifying impact of variables in deep learning models on bankruptcy prediction of construction contractors

Cited by 18 publications

References 33 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

The Application of Machine Learning in Diagnosing the Financial Health and Performance of Companies in the Construction Industry

The Value of Open Banking Data for Application Credit Scoring: Case Study of a Norwegian Bank

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