Credit Rating Forecasting Using Machine Learning Techniques

Wallis, Mark; Kumar, Kuldeep; Gepp, Adrian

doi:10.4018/978-1-5225-7277-0.ch010

Cited by 14 publications

(16 citation statements)

References 19 publications

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“…(Deloitte, 2018) mentioned that the Random Forest for credit risk models-Machine learning and Credit Risk is a suitable marriage. More big entities explored Machine Learning in the prediction of credit ratings, and it was identified that Random Forests models possess good performance results (Wallis, Kumar, & Gepp, 2019), (Morgan, 2017), and(McKinsey, 2017). The highest precision of the Random Forests algorithm for the credit rating estimation was also shown in (Lia, Mirzab, Rahatc, & Xiongd, 2020), where the precision remained robust for all the classes of the ratings.…”

Section: Evolution Of the Credit Risk Modeling And Datamentioning

confidence: 99%

Harnessing Machine Learning Emerging Technology in Financial Investment Industry: Machine Learning Credit Rating Model Implementation

Wang¹,

Sen²,

Yao³

et al. 2021

JFRM

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Credit risk ratings consist of assessing the creditworthiness of the issuer and gauge the risks associated with buying its debt. Any delay in updating the credit risk ratings could have a severe impact on the financial system such as the financial crisis in 2008. This paper discusses a case that leverages emerging technology and breakthrough cognitive analytics in the financial industry. It specifically describes the design and implementation of a predictive modeling case based on the Machine Learning Approach and its application in credit risk forecasting and portfolio management. Using big data and Machine Learning, it is possible to improve credit risk analysis and forecasting by allowing the algorithms to search for patterns using large sets of data.

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Section: Evolution Of the Credit Risk Modeling And Datamentioning

confidence: 99%

Harnessing Machine Learning Emerging Technology in Financial Investment Industry: Machine Learning Credit Rating Model Implementation

Wang¹,

Sen²,

Yao³

et al. 2021

JFRM

View full text Add to dashboard Cite

show abstract

“…Rating agencies use specific financial ratios to assess corporate credit ratings Wallis et al (2019). These features are considered to be good indicators of companies financial situation.…”

Section: Comparing the Performance Of Algorithms When Using A Selecte...mentioning

confidence: 99%

“…These features are considered to be good indicators of companies financial situation. In this section, we choose features that are important for companies profitability and revenue Wallis et al (2019); Kumar (2001). Table 1 provides the list of these features according to the literature provided.…”

Section: Comparing the Performance Of Algorithms When Using A Selecte...mentioning

confidence: 99%

Application of Deep Neural Networks to assess corporate Credit Rating

Golbayani,

Wang,

Florescu

2020

Preprint

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Recent literature implements machine learning techniques to assess corporate credit rating based on financial statement reports. In this work, we analyze the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor's. We analyze companies from the energy, financial and healthcare sectors in US. The goal of the analysis is to improve application of machine learning algorithms to credit assessment. To this end, we focus on three questions. First, we investigate if the algorithms perform better when using a selected subset of features, or if it is better to allow the algorithms to select features themselves. Second, is the temporal aspect inherent in financial data important for the results obtained by a machine learning algorithm? Third, is there a particular neural network architecture that consistently outperforms others with respect to input features, sectors and holdout set? We create several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedure.

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“…They also show that MLP and Decision tree are the best models by providing the highest accuracy and the lowest misclassification cost, respectively. Wallis et al (Wallis et al, 2019) provided a comparative analysis of several most popular machine learning techniques (multinomial logistic regression, linear discriminant analysis, regularized discriminant analysis,artificial neural network, support vector machine, gaussian process classifier, random forest, gradient boosting machine) to predict Moody's lone term credit rating. They studied these models on 308 of the S&P 500 companies from January 2016 to November 2017.…”

Section: Evaluation and Comparisonmentioning

confidence: 99%

A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees

Golbayani¹,

Florescu²,

Chatterjee³

2020

Preprint

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Credit ratings are one of the primary keys that reflect the level of riskiness and reliability of corporations to meet their financial obligations. Rating agencies tend to take extended periods of time to provide new ratings and update older ones. Therefore, credit scoring assessments using artificial intelligence has gained a lot of interest in recent years. Successful machine learning methods can provide rapid analysis of credit scores while updating older ones on a daily time scale. Related studies have shown that neural networks and support vector machines outperform other techniques by providing better prediction accuracy. The purpose of this paper is two fold. First, we provide a survey and a comparative analysis of results from literature applying machine learning techniques to predict credit rating. Second, we apply ourselves four machine learning techniques deemed useful from previous studies (Bagged Decision Trees, Random Forest, Support Vector Machine and Multilayer Perceptron)

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Credit Rating Forecasting Using Machine Learning Techniques

Cited by 14 publications

References 19 publications

Harnessing Machine Learning Emerging Technology in Financial Investment Industry: Machine Learning Credit Rating Model Implementation

Harnessing Machine Learning Emerging Technology in Financial Investment Industry: Machine Learning Credit Rating Model Implementation

Application of Deep Neural Networks to assess corporate Credit Rating

A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees

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