An application of support vector machines in bankruptcy prediction model

By Kyung-shik Shin, Taik Soo Lee, Hyun-jung Kim. Expert systems with applications, 2005

837 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/301789c07c0214f90179f4b1bfe41d52720d3e3f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

In Artificial Intelligence

Citations in the corpus

Soft computing system for bank performance prediction (2008, methodology): “Shin and Lee [46] proposed a genetic algorithm based method for firm bankruptcy prediction.”
Deep belief networks for predicting corporate defaults (2015, background): “There have been several machine learning algorithms proposed regarding the default prediction problem as a classiﬁcation problem, such as Support Vector Machines (SVM) [7], [8] and Artiﬁcial Neural Network (ANN) [9], [10].”
Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches (2014, methodology): “[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.”
Kernel Methods in Finance (2008, background): “In their study (Shin et al., 2005) showed that SVMs outperformed backpropagation neural networks in the problem of corporate bankruptcy pre-”
Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction (2016, methodology): “One of the most successful model was support vector machines (SVM) (Shin et al., 2005).”
Artificial neural networks in business: Two decades of research (2016, neutral): “[344] compared conventional backpropagation neural network with support vector machines in corporate bankruptcy prediction problem, Kuo and Xue [235] proposed fuzzy neural network for the sake of sales forecasting, West et al.”
Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms (2011, neutral): “Following Shin et al. (2005), SVM was tuned by manipulating the complexity parameter C at ﬁve levels: 1, 10, 50, 75, and 100.”
Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models (2014, result): “A study by Shin et al. (2005) and Chevalier et al. (2011) found that the application of ANN models in time series forecasting was comparable to those of SVR models especially as the size of the training set was increased.”