Credit rating analysis with support vector machines and neural networks: a market comparative study

By Zan Huang, Hsinchun Chen, Chia-Jung Hsu, Wun-Hwa Chen, Soushan Wu. 2004

1,075 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Finance

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Citations in the corpus

Deep Learning Stock Volatility with Google Domestic Trends (2015, background): “Overfitting is one of the biggest issues which have plagued highly-parameterized supervised machine learning methods in past decades [5].”
Bayesian dynamic density estimation (2008, neutral): “In the analysis of credit rating scores, Huang et al. (2004) compared credit rating prediction performance between backpropagation neural network (BNN) and support vector machines (SVM), obtaining around 80% of accuracy for both methods in the context of corporate credit rating analysis.”
Time Series Prediction Using Support Vector Machines: A Survey (2009, methodology): “[50] and Hur and Lim [51]), not an SVR.”
Explaining prediction models and individual predictions with feature contributions (2014, background): “Applications range from credit scoring [11] and fraud detection [5] to financial auditing [4] and efficiency analysis [18].”
Artificial neural networks in business: Two decades of research (2016, background): “Throughout the investigated period authors have applied artificial neural networks to various problems including credit scoring [161, 258, 259], financial analysis [52, 186], or stock performance prediction [89, 162, 166].”
A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, methodology): “work, [52] designed a support vector machine (SVM) for credit rating analyses and they compared it with ANN.”
Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation (2015, methodology): “Also, advanced data analysis techniques are currently used to evaluate risk in credit approval (Huang et al., 2004) and fraud detection (Ngai et al.”
Using neural network ensembles for bankruptcy prediction and credit scoring (2008, positive): “…learning techniques are superior to that of traditional (statistical) methods in dealing with bankruptcy prediction and credit scoring problems, especially in nonlinear pattern classification (Huang et al., 2004; Ong, Huang, & Tzeng, 2005; Vellido, Lisboa, & Vaughan, 1999; Wong & Selvi, 1998).”
Support vector machines for credit scoring and discovery of significant features (2009, background): “Huang et al (2004) compare SVMs with a back-propagation neural network to predict corporate credit ratings but find consequential differences in performance.”
Consumer Credit Risk Models Via Machine-Learning Algorithms (2010, result): “Huang et al. (2004) provides an excellent survey of these and other studies.”
Asymmetric benchmarking in bank credit rating (2012, neutral): “Kim (2005), Huang et al. (2004) and Lee (2007) show that artificial intelligence techniques (particularly neural networks and support vector machines) do not provide superior predictions of bond ratings compared with standard ordered-choice methods.”
Financial Crises and Bank Failures: A Review of Prediction Methods (2010, methodology): “Such models are utilized in Vapnik [59], Boyacioglu et al. [2], Chen and Shih [1] and Huang et al. [ 60 ], among others.”
Major Challenges Facing Small and Medium-Sized Enterprises in Asia and Solutions for Mitigating Them (2016, background): “Huang et al (2004) compared support vector machines and back-propagation neural networks to forecast the rating of financial institutions operating in the United States and Taipei,China.”
Financial time series forecasting using independent component analysis and support vector regression (2009, neutral): “Due to the advantages of the generalization capability in obtaining a unique solution, the SVMs have drawn the attention of researchers and have been applied in many applications such as texture classification, image recognition, data mining and bioinformatics [6, 13, 22, 32, 35, 44, 46, 50].”
Modeling wine preferences by data mining from physicochemical properties (2009, background): “…António Cerdeira b, Fernando Almeida b, Telmo Matos b, José Reis a,b a Department of Information Systems/R&D Centre Algoritmi, University of Minho, 4800-058 Guimarães, Portugal b Viticulture Commission of the Vinho Verde Region (CVRVV), 4050-501 Porto, Portugal a b s t r a c ta r t i c l e i n f o”