Credit Risk Evaluation of Power Market Players with Random Forest

Umezawa, Yasushi; Mori, Hiroyuki

doi:10.1541/ieejpes.128.165

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…;Min and Lee 2005; Wang et al 2005;Zhou and Tian 2007;Altman and Sabato 2007;Mori and Umezawa 2007;Angelini et al 2008;Vasiliauskaite and Cvilikas 2008;Zhang and Härdle 2010;Chen and Du 2009;Lin 2009;Min and Jeong 2009;Ryser and Denzler 2009; Bužius et al 2010;Tseng and Hu 2010; Dan ėnas et al 2011;De Andrés et al 2011c;Pacelli and Azzollini 2011;Mileris 2012;Olson et al 2012;Wu and Hsu 2012;Gurný and Gurný 2013;Lorca et al 2014). As a result, our research group…”

mentioning

confidence: 67%

“…In this study, three forecasting methods were used (logistic regression, ANN (more precisely, MLP and RBF neural networks) and MARS). In further research, it will be reasonable to also apply other machine-learning models: random forest (e.g., Mori and Umezawa (2007) and Uddin et al (2022)), gradient boosting (e.g., Papík and Papíková (2023) tested CatBoost, LightGBM and XGBoost algorithms) and support vector machine (e.g., Tserng et al 2011).…”

Section: Conflicts Of Interestmentioning

confidence: 99%

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Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector

Kanapickienė,

Kanapickas,

Nečiūnas

2023

Risks

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Credit-risk models that are designed for general application across sectors may not be suitable for the construction industry, which has unique characteristics and financial risks that require specialised modelling approaches. Moreover, advanced bankruptcy-prediction models are often used to achieve the highest accuracy in large modern datasets. Therefore, the aim of this research is the creation of enterprise-bankruptcy prediction (EBP) models for Lithuanian micro and small enterprises (MiSEs) in the construction sector. This issue is analysed based on classification models and the specific types of variable used. Firstly, four types of variable are proposed. In EBP models, financial variables substantially explain an enterprise’s financial statements and performance from different perspectives. Including enterprises’ non-financial, construction-sector and macroeconomic variables improves the characteristics of EBP models. The inclusion of macroeconomic variables in the model has a particularly significant impact. These findings can be of great significance to investors, creditors, policymakers and practitioners in assessing financial risks and making informed decisions. The second question is related to the classification models used. To develop the EBP models, logistic regression (LR), artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS) were used. In addition, this study developed two-stage hybrid models, i.e., the LR is combined with ANNs. The findings show that two‑stage hybrid models do not improve bankruptcy prediction. It cannot be argued that ANN models are more accurate in predicting bankruptcy. The MARS model demonstrates the best bankruptcy prediction, i.e., this model could be a valuable tool for stakeholders to evaluate enterprises’ financial risk.

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confidence: 67%

Section: Conflicts Of Interestmentioning

confidence: 99%

Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector

Kanapickienė,

Kanapickas,

Nečiūnas

2023

Risks

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“…The digital era has arrived, and technologies such as big data, cloud computing, and blockchain have made the collection and processing of large amounts of data a reality [3] and are widely used in smart banking [4], financial regulation [5], and smart investment [6]. Meanwhile, the financial industry has undergone a continuous evolution in the digital era, and the current focus has shifted from improving traditional delivery to introducing new business opportunities and models for financial service companies with the value of financial inclusion [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Inclusive Finance, Industrial Upgrading and Farmers’ Income Growth

Yu¹,

Feng²

2023

Advances in Social Science, Education and Humanities Research

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The interaction mechanism between digital inclusive finance and farmers' income growth is focused on, and the mediating effect of industrial upgrading in it is analyzed. Using provincial panel data from 2011-2018 in China, a fixedeffects model and a mediating-effects model are developed to empirically test the interaction effects and realization paths of digital inclusive finance, industrial upgrading and farmers' income growth. The results show that digital inclusive finance promotes farmers' income growth, and industrial upgrading has a partial mediating effect in the process of digital inclusive finance promoting farmers' income growth, and the ratio of the mediating effect to the total effect is 14.82%, and it is more significant in the central and western regions.

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Towards Early Detections of the Bad Debt Customers among the Mail Order Industry

Takahashi

Tsuda

2013

Electronic Business and Marketing

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Credit Risk Evaluation of Power Market Players with Random Forest

Cited by 8 publications

References 9 publications

Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector

Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector

Digital Inclusive Finance, Industrial Upgrading and Farmers’ Income Growth

Towards Early Detections of the Bad Debt Customers among the Mail Order Industry

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