The Development of an Alternative Method for the Sovereign Credit Rating System Based on Adaptive Neuro-Fuzzy Inference System

Pabuçcu, Hakan; Ayan, Tuba Yakici

doi:10.4236/ajor.2017.71003

Cited by 9 publications

(5 citation statements)

References 32 publications

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“…Yin Ming (2019) [8] established a game model between the central and local government and the public and put forward policy suggestions for preventing hidden debt risks from the perspective of improving local debt information disclosure and strengthening public supervision. In terms of using artificial intelligence to study sovereign credit risk, Pabuçcu and Ayan (2016) [9] used artificial neural networks to predict government bond interest rates and determine the credit ratings of different countries, thereby analyzing the country's sovereign credit risk. Sarlin P (2011) [10] used machine learning technology to predict sovereign debt crises, and studied whether the public debt of developed countries exceeds 150% of the GDP as a dependent variable to predict the possibility of sovereign debt crises.…”

Section: Literature Review 21 Hidden Debt Risks Of Local Governmentmentioning

confidence: 99%

Early Warning of Systemic Financial Risk of Local Government Implicit Debt Based on BP Neural Network Model

Zhao

Chen

et al. 2022

Systems

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In recent years, local governments have boosted their local economies by raising large amounts of debt. Even though the state further strictly controls local government debt, the hidden debt formed by the local government borrowing in disguised form can infect systemic financial risks, creating an urgent need to carry out risk warning based on local government hidden debt. The paper uses the macro indicators of local government implicit debt risk at the prefecture-level city level, and introduces the micro indicators of PPP projects, financing platform bank debt, and urban investment debt to establish a BP neural network model. We not only study the contagion effect of local government hidden debt on systemic financial risks, but also predict the systemic financial risks in 2019 and construct an early warning risk system based on the prefecture-level city data from 2015 to 2018. In addition, the early warning effect of local government implicit debt on systemic financial risk under different stress scenarios is investigated. The study found that the implicit debt risk of local governments, the scale of financing platform bank debt, the scale of PPP, and the scale of urban investment bonds have a significant impact on systemic financial risks. The neural network model constructed by introducing these four variables at the same time can better predict the level of systemic financial risk. The model can also accurately predict the changes in systemic financial risks under the stress test of the increase in hidden debt of different local governments, and has a good early warning effect.

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Section: Literature Review 21 Hidden Debt Risks Of Local Governmentmentioning

confidence: 99%

Early Warning of Systemic Financial Risk of Local Government Implicit Debt Based on BP Neural Network Model

Zhao

Chen

et al. 2022

Systems

View full text Add to dashboard Cite

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“…The main purpose of the defuzzification strategies is to be able to produce a non-fuzzy control mechanism that can best represent the probability distribution of a system obtained by fuzzy control mechanisms [23], [24]. While it is not a systematic approach to select the defuzzification strategy, a method should be chosen according to the problem structure [25].…”

Section: Fuzzy Logicmentioning

confidence: 99%

Measuring Poverty Level of Households by Using Fuzzy Logic

Pabuçcu¹

2017

aeb

Self Cite

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Poverty is general scarcity or the state of one who lacks a certain amount of material possessions or money. Poverty is a multifaceted concept, which includes social, economic, and political elements. Poverty level can be calculated for households as well as for countries. United Nations developed a methodology to calculate the advanced poverty index for countries. The main purpose of this study is primarily to determine the determinants of poverty by conducting a strong literature survey and examining the United Nations methodology. By using the determinants of poverty, it is planned to calculate the poverty index and classify the households with an approach based on fuzzy logic. The applied methodology contains the weighting of the determinants of poverty by using fuzzy logic membership functions and the calculation of the poverty index of households. The weighting process of the determinants has recognized by taking the opinions of the expert academicians in the field of poverty. It is also considered to categorize the households, according to the level of poverty. For this purpose, it is determined a sample consisting of 120 households in Bayburt province. As a result of the analysis, it is aimed to examine the effect of factors determined the poverty and also provide suggestions to the researchers and policy makers.

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“…The Subjective approach and Objective approach are the credit-granting methodologies that financial organizations might employ to assess credit applicants' repayment potential (Bougard, 2017). The subjective approach uses judgment by the credit analyst to analyse a borrower's character, collateral and ability to repay (Pabuccu & Ayan, 2017). Also, recommendations from the employer of the borrower or previous lender can be used to make a decision on who is entitled to receive credit.…”

Section: Introductionmentioning

confidence: 99%

Development Of Credit Scoring Model For Borrowers Using Machine Learning Techniques

Ekong

Akintola²,

Kuboye³

2022

j. gov. soc. politicol.

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Financial organizations such as banks have experienced an increase in demand for loans from borrowers over the years. These organizations are highly interested in knowing whether a borrower can pay back if granted the loan requested. Granting loans to defaulters can cripple the business, hence, these financial organizations are compelled to evaluate credit worthiness of clients using the vast volume of historical data related to financial position of borrowers. Like other prediction models, credit scoring is a technique used in predicting the probability that a loan applicant, existing borrower, or counterparty will default. Machine learning technique has ability to solve these challenges faced by credit analyst by automating the processing and extraction of knowledge from data. This research focuses on the development of a credit scoring model using Rough Set Theory (RST) and Multi-Layer Perceptron (MLP) Neural Network. RST was used for feature selection while ANN trained with backpropagation was used for classification. This research used two credit scoring datasets; Australian and German credit dataset. Data pre-processing and machine learning were performed using the Anaconda software. This research compares the result obtained from the RST and MLP with Decision Tree, Logistic Regression, Random Forest, Support Vector Machine, Nayes Bayes, K-Nearest Neighbour and ANN using standard evaluation metric to ascertain its performance on the two datasets and conclude the major findings. This research contributes a credit scoring model with improved performance while saving the computational costs.

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The Development of an Alternative Method for the Sovereign Credit Rating System Based on Adaptive Neuro-Fuzzy Inference System

Cited by 9 publications

References 32 publications

Early Warning of Systemic Financial Risk of Local Government Implicit Debt Based on BP Neural Network Model

Early Warning of Systemic Financial Risk of Local Government Implicit Debt Based on BP Neural Network Model

Measuring Poverty Level of Households by Using Fuzzy Logic

Development Of Credit Scoring Model For Borrowers Using Machine Learning Techniques

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