Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters

By Jae H. Min, Young-Chan Lee. Expert systems with applications, 2005

927 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/3548e1cdb053744ac5790fd09695f2290f0fb357

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Citations in the corpus

Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors (2007, methodology): “SVMs were successfully reported in classifying human cytochrome P450 enzyme (Kriegl, Arnhold, Beck, & Fox, 2005), financial analysis (Min & Lee, 2005), chemical process (Guo, Xie, Wang, & Zhang, 2003), biomedical (Chan & Lee, 2002), image processing and face recognition (Antonini, Popovici, &…”
Artificial neural networks in business: Two decades of research (2016, neutral): “Financial distress & bankruptcy (76) [8, 13, 16, 17, 30, 31, 38, 40, 60, 62, 64, 65, 74, 77, 78, 86, 90, 97, 98, 107, 128, 129, 147, 145, 158, 159, 167, 168, 176, 177, 179, 180, 181, 183, 185, 189, 193, 194, 201, 203, 204, 212, 213, 218, 220, 223, 227, 243, 253, 254, 257, 266, 274, 288, 308, 318, 32”
Financial credit risk assessment: a recent review (2015, background): “Their excellent capability of treating nonlinear data is beneficial to find the intrinsic patterns from complex financial data (Charalambous et al. 2000; Fu-yuan 2008).”
Machine learning models and bankruptcy prediction (2017, methodology): “Meanwhile, although the123 RBF kernel is difficult to analyse, or even discuss, it provides superior predictions in non-separable cases.124 The SVM method is discussed in detail in Cortes and Vapnik (1995); Min and Lee (2005), and Yu et al.125 (2010).126 1.2.”
Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review (2007, background): “[79] Break even point ratio, bonds payable, cash flow/interest expenses, cash flow/(previous years short-term loan), cash flow/shortterm loan, cash flow/total debt, cash flow/total loans, capital stock turnover, depreciation ratio, EBIT/sales, fixed assets/”
Supervised classification and mathematical optimization (2013, background): “Typical business applications are credit scoring [11], bankruptcy [176], fraud detection [87], customer targeting [73], customer loyalty [106] [112], market basket analysis [61], recommender systems [62], revenue management [122], services booking cancellations [203], country risk ratings [114],…”
Stock Market Prediction using Feed-forward Artificial Neural Network (2014, background): “[4] report a comparative study of application of Support Vector Machines (SVM) and Backpropagation Neural Networks (BPNN) for an analysis of corporate credit ratings.”
Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches (2014, methodology): “Shin et al. [107] and Min and Lee [80] both used SVM to predict bankruptcy for South Korean companies, and drew the conclusion that this method outperformed MDA, Logit and NN. Hui and Sun [49] and Ding et al. [31] respectively adopted SVM model to do empirical study on FDP for Chinese listed compani”
Atmospheric Temperature Prediction using Support Vector Machines (2009, methodology): “Various techniques like linear regression, auto regression, Multi Layer Perceptron, Radial Basis Function networks are applied to predict atmospheric parameters like temperature, wind speed, rainfall, meteorological pollution etc. [4], [5], [6], [7], [10], [11].”
Kernel Methods in Finance (2008, background): “CR BP (Fan and Palaniswami 2000) (van Gestel et al. 2003b) (Sânchez et al. 2004) (Shin et al. 2004) (Yun et al. 2004) (Hardle et al. 2005) ( Min and Lee 2005 ) (Shin et al. 2005) (Hui and Sun 2006) – – (Min et al. 2006) 10”
A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy (2007, methodology): “To design an effective SVM model, values of parameters in SVM have to be chosen carefully in advance (Duan, Keerthi, & Poo, 2003; Lin, 2001; Min & Lee, 2005).”