What Best Predicts Corporate Bank Loan Defaults? An Analysis of Three Different Variable Domains

Kohv, Keijo; Lukason, Oliver

doi:10.3390/risks9020029

Cited by 7 publications

(5 citation statements)

References 49 publications

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“…While SDEFAULT directly reflects the financial difficulties at the moment a firm should finally have submitted the annual report, LDEFAULT provides a longer retrospective view. In Estonia, no all-inclusive information about defaults to the private sector (e.g., banks, suppliers, workers) is available, but as firms having such private defaults usually witness tax arrears [60], the latter normally before the defaults to the private sector, the lack of such information is not an issue.…”

Section: Methodsmentioning

confidence: 99%

What Best Explains Reporting Delays? A SME Population Level Study of Different Factors

Lukason

Miñano

2021

Sustainability

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The objective of this paper is to find out which factors best explain why SMEs delay their annual reports (DAR). Relying on various theoretical streams, we use three types of variables to explain DAR: past DAR behaviour of managers, corporate governance characteristics and occurrence of financial distress. The study is based on the whole population data from Estonia, with a total 59,294 unique firms. Two types of DAR, i.e., short- and long-term delays, are used as dependent variables in the logistic regression analysis. The paper indicates that both types of today’s DAR are best explained by the previous DAR behaviour of managers, especially in the nearest past. Financial distress has a lower, but still acceptable explanatory power, while it remains weak for the corporate governance characteristics. Firm size and age have an impact on the results. As the paper indicates the prominence of recurrent violation behaviour in explaining DAR, the legal framework and its implementation could be adjusted to take account of this fact. The linkage of DAR and financial distress suggests the inclusion of the former into credit scoring models.

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Section: Methodsmentioning

confidence: 99%

What Best Explains Reporting Delays? A SME Population Level Study of Different Factors

Lukason

Miñano

2021

Sustainability

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“…A significant strand of literature has found that intelligent models in credit default prediction models are efficient in predicting corporate defaulting [20,[26][27][28]. Without the strict assumptions of the traditional statistical models (e.g., independence and normality among predictor variables), intelligent techniques can automatically derive knowledge from training data [28][29][30].…”

Section: Credit Default Prediction Modelsmentioning

confidence: 99%

“…In general, relative to statistic models, the corporate default prediction performance of intelligent techniques is better. For instance, Kim et al [20] found that the neural network model outperforms logit regression. Similarly, Lahmiri [31] documented that SVM is significantly more accurate than a linear discriminant analysis classifier.…”

Section: Credit Default Prediction Modelsmentioning

confidence: 99%

“…To assess the performance of our proposed CT-XGBoost model on credit default prediction for class imbalance problems, we constructed a database of credit defaults sourced from a commercial bank in western China. As in most previous studies [20], we used the financial variables from the financial statements as the predictors to assess whether the corporates (debtors) would default. As for the benchmark models, we select previously commonly used models: logistic regression, support vector machine (SVM), neural network, random forest, and XGBoost.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Algorithm-Level Ensemble Model for Imbalanced Credit Default Prediction in the Energy Industry

Wang

et al. 2022

Energies

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Credit default prediction for the energy industry is essential to promoting the healthy development of the energy industry in China. While previous studies have constructed various credit default prediction models with brilliant performance, the class-imbalance problem in the credit default dataset cannot be ignored, where the numbers of credit default cases are usually much smaller than the number of non-default ones. To address the class-imbalance problem, we proposed a novel CT-XGBoost model, which adds to XGBoost with two algorithm-level methods for class imbalance, including the cost-sensitive strategy and threshold method. Based on the credit default dataset consisting of energy corporates in western China, which suffers from the class-imbalance problem, the CT-XGBoost model achieves better performance than the conventional models. The results indicate that the proposed model can efficiently alleviate the inherent class-imbalance problem in the credit default dataset. Moreover, we analyze how the prediction performance is influenced by different parameter settings in the cost-sensitive strategy and threshold method. This study can help market investors and regulators precisely assess the credit risk in the energy industry and provides theoretical guidance to solving the class-imbalance problem in credit default prediction.

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“…Current threats and risk management systems are key CSR factors for Stakeholders and the country's economy. Polish and EU economic disruptions may also affect energy company performance; fuels, gas, and mining companies; project subcontractors, and customers e.g., [20][21][22][23]. It is thus important to identify new energy company risks.…”

Section: Introductionmentioning

confidence: 99%

A Model of Risk Information Disclosures in Non-Financial Corporate Reports of Socially Responsible Energy Companies in Poland

2022

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Risk management is critical for corporate finance management systems, in addition to corporate social responsibility (CSR) and sustainable development (SD) programs. Stakeholders need risk information to make informed judgments as to their involvement. No studies exist to date concerning disclosure of non-financial and financial risks in corporate annual statements and Polish strategic sector company reports. The authors sought to determine whether energy companies disclosed risks in non-financial annual reports in 2011–2020 (e.g., CSR, integrated, and board activity reports), and whether one can assess threats, including business activity risks and SD, based on these reports. We assessed the reports of all large Polish energy companies on a three- and five-degree scale to develop a model for risk information disclosures. We have three key empirical findings. Only half the analysed companies disclose annual financial data, threats, and risks. Less than half have implemented and operate enterprise risk management systems. The current ‘soft’ regulatory solutions give energy companies appreciable risk disclosure options, which often is counterproductive. We suggest developing a single integrated European Union (EU) regulation (e.g., directives, standards, or official principles) for non-financial risk disclosures. Our model classifies Polish energy company risks to business activity operations and risk management systems. Other sectors can use this universal model. Our results constitute progress in identifying company risks and may encourage continuing studies of other energy companies, especially in Central and Eastern Europe (CEE), which should be intensively developed. Research should also occur in other strategic sectors.

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What Best Predicts Corporate Bank Loan Defaults? An Analysis of Three Different Variable Domains

Cited by 7 publications

References 49 publications

What Best Explains Reporting Delays? A SME Population Level Study of Different Factors

What Best Explains Reporting Delays? A SME Population Level Study of Different Factors

A Hybrid Algorithm-Level Ensemble Model for Imbalanced Credit Default Prediction in the Energy Industry

A Model of Risk Information Disclosures in Non-Financial Corporate Reports of Socially Responsible Energy Companies in Poland

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