A hybrid information approach to predict corporate credit risk

Bu, Di; Kelly, Simone; Liao, Yin; Zhou, Qing

doi:10.1002/fut.21930

Cited by 3 publications

(4 citation statements)

References 37 publications

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“…‫فقط‬ ‫المالية‬ ‫المؤشرات‬ ‫على‬ ‫اعتمد‬ ) Al Janabi, 2016; Bu, et al, 2018;Tavana, et al, Bushman, et al, ( Hull, 2018;El Bouchti, et al, 2018;Hopkin, 2018 FitzPatrick, 1932;Beaver, 1966;1968;Altman, 1968;Deakin, 1972;Altman, et al, 1977;Kida, 1980;Ohlson, 1980;Zavgren, 1985;Gentry, et al, 1985;Laitinen, 1993;Shirata, Gupta, et al, 2016;Su, and Zhang, ;1998Bu, et al, 2017;Balina, 2018;Du Jardin 2018;Halteh, et al, 2018;Khemakhem and Boujelbene, Tang, et 2018;Li, and Quan, 2019;Hussain, et Ansari, et al,2020;Abidin, et al, 2020;0;Wang, and Xie, al., 2020;Teles, et al, 202 2020;Zhao, 2020;Balci, and Ogul, 2021 Du Jardin, 2021;Hanafi, et al, 2021;Jencova, et al, 2021;Uddin, 2021;Wu, et al, 2021;Zhao, 2021 2000FitzPatrick, 1932Beaver, 1966;1968;…”

Section: ‫على‬ ‫السابقة‬ ‫الدراسات‬ ‫من‬ ‫العديد‬ ‫تركيز‬ ‫من‬ ‫بالرغم‬ ‫وتحليل‬ ‫تقييم‬ ‫الدراسات‬ ‫هذه‬ ‫معظم‬ ‫أن‬ ‫إال‬ ‫بها،‬ ‫والتنmentioning

confidence: 99%

“…Gupta, et al, 2016;Laitinen, 1993;Abidin, et al, 2020;Teles, et al, 2020;Charalambous, et FitzPatrick, 1932;Beaver, 1966;1968;Altman, 1968;Deakin, 1972;Altman, et al, 1977;Kida, 1980;Ohlson, 1980;Zavgren, 1985;Gentry, et al, 1985;Laitinen, Su, and Zhang, 2017;Balina, 2018;993;Shirata, 1998;1 Bu, et al, 2018;Du Jardin, 2018;Khemakhem, et al, Tang, et 2018;Halteh, et al, 2018;Li, and Quan, 2019;Hussain, et al, 2020;Ansari, et al,2020;0;Wang, and Xie, 2020;Balci, al., 2020;Teles, et al, 202 and Ogul, 2021;Charalambous, et al, 2021;Du Jardin, 2021;Hanafi, et al, 2021;Jencova, et al, 2021;Uddin, 2021; Bu, et al, 2018FitzPatrick, 1932Beaver, 1968 Gupta, et al, 2016;Ohlson, 1980;Zavgren, 1985;Laitinen, 1993;Balina, 2018;…”

confidence: 99%

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دراسة تحليلية لنماذج التنبؤ بالمخاطر المالية لمنشآت الأعمال

عثمان¹,

اسماعيل²

2021

المجلة الأکاديمية للبحوث التجارية المعاصرة

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This research aims to conduct an analytical study of financial risk prediction models by following the method of systematic literature review to determine the findings of this literature and to conclude research gaps that could represent the ideas of future research trends, and to provide theoretical scientific conclusions from the analysis of the results of previous research and studies And the leading models in the field of forecasting the corporate financial risks. The research concluded several results, the most important of which are; Increasing the level of awareness of the importance of forecasting financial risks in achieving a balance between the investment opportunities with a higher return, and the degree of risk that can be accepted and managed. Adding non-financial indicators to the models that were relied on by previous studies to predict financial risks will increase the accuracy of this prediction, The use of neural network models in forecasting will increase the predictive of financial risks due to the high predictive accuracy of these models compared to statistical models. The research recommends conducting future studies to identify the determinants that affect the accuracy of forecasting financial risks.

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confidence: 99%

دراسة تحليلية لنماذج التنبؤ بالمخاطر المالية لمنشآت الأعمال

عثمان¹,

اسماعيل²

2021

المجلة الأکاديمية للبحوث التجارية المعاصرة

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“…Credit risk prediction (CRP) refers to the process of predicting whether a company will default in the future through the historical data of a company (Bu et al, 2018;Kousenidis et al, 2019;Ma and Lv, 2019). Accurate and timely CRP can help companies avoid losses.…”

Section: Introductionmentioning

confidence: 99%

Using deep learning to interpolate the missing data in time-series for credit risks along supply chain

Zhang¹,

Lim²,

Yang³

et al. 2023

IMDS

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PurposeAs the supply chain is a highly integrated infrastructure in modern business, the risks in supply chain are also becoming highly contagious among the target company. This motivates researchers to continuously add new features to the datasets for the credit risk prediction (CRP). However, adding new features can easily lead to missing of the data.Design/methodology/approachBased on the gaps summarized from the literature in CRP, this study first introduces the approaches to the building of datasets and the framing of the algorithmic models. Then, this study tests the interpolation effects of the algorithmic model in three artificial datasets with different missing rates and compares its predictability before and after the interpolation in a real dataset with the missing data in irregular time-series.FindingsThe algorithmic model of the time-decayed long short-term memory (TD-LSTM) proposed in this study can monitor the missing data in irregular time-series by capturing more and better time-series information, and interpolating the missing data efficiently. Moreover, the algorithmic model of Deep Neural Network can be used in the CRP for the datasets with the missing data in irregular time-series after the interpolation by the TD-LSTM.Originality/valueThis study fully validates the TD-LSTM interpolation effects and demonstrates that the predictability of the dataset after interpolation is improved. Accurate and timely CRP can undoubtedly assist a target company in avoiding losses. Identifying credit risks and taking preventive measures ahead of time, especially in the case of public emergencies, can help the company minimize losses.

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“…For example, Tian et al built a new fuzzy set and the most advanced credit risk assessment algorithm model using the kernel-free QSSVM basic model to deal with information label error [15]. Bu et al created a new mixed information method, using mixed integrated information to predict enterprise credit risk, and demonstrated its advantages in short-term credit assessment [16]. Huang et al improved the robustness of the probabilistic neural network model by determining the dimensions in advance [17].…”

Section: Introductionmentioning

confidence: 99%

Credit Risk Assessment of Banks' Loan Enterprise Customer Based on State-Constraint

Liu

Yang

Dong

et al. 2021

cai

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Commercial banks are facing increasingly complex enterprise loan customers and businesses. It is important for banks' enterprise loan business to efficiently assess credit risks. Our study builds an enterprise credit risk assessment model based on the state and constraint of bank and customer, and get empirical researches with RF, SVM and DT algorithms. The results show that our model has excellent performance with accuracy 99 % and great characteristic importance in the evaluation of enterprise credit risk. The study can provide important decisionmaking reference for bank loan business and enrich the theoretical system of bank credit risk research.

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A hybrid information approach to predict corporate credit risk

Cited by 3 publications

References 37 publications

دراسة تحليلية لنماذج التنبؤ بالمخاطر المالية لمنشآت الأعمال

دراسة تحليلية لنماذج التنبؤ بالمخاطر المالية لمنشآت الأعمال

Using deep learning to interpolate the missing data in time-series for credit risks along supply chain

Credit Risk Assessment of Banks' Loan Enterprise Customer Based on State-Constraint

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