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Financial decision support using neural networks and support vector machines

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Financial Distress and Bankruptcy Prediction Neural Networks and Applications Data Mining and Machine Learning Applications

Abstract

Abstract:Bankruptcy prediction and credit scoring are the two important problems facing financial decision support. The multilayer perceptron (MLP) network has shown its applicability to these problems and its performance is usually superior to those of other traditional statistical models. Support vector machines (SVMs) are the core machine learning techniques and have been used to compare with MLP as the benchmark. However, the performance of SVMs is not fully understood in the literature because an insufficient number of data sets is considered and different kernel functions are used to train the SVMs. In this paper, four public data sets are used. In particular, three different sizes of training and testing data in each of the four data sets are considered (i.e. 3:7, 1:1 and 7:3) in order to examine and fully understand the performance of SVMs. For SVM model construction, the linear, radial basis function and polynomial kernel functions are used to construct the SVMs. Using MLP as the benchmark, the SVM classifier only performs better in one of the four data sets. On the other hand, the prediction results of the MLP and SVM classifiers are not significantly different for the three different sizes of training and testing data.

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References

2000Neural network credit scoring models 2003Credit rating analysis with support vector machines and neural networks: a market comparative study 2005Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters 2004An application of support vector machines in bankruptcy prediction model 2001Bankruptcy prediction for credit risk using neural networks: A survey and new results 2004Mining the customer credit using classification and regression tree and multivariate adaptive regression splines 2002Credit scoring using the hybrid neural discriminant technique 2004Neural network ensemble strategies for financial decision applications

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2016An Effective Computational Model for Bankruptcy Prediction Using Kernel Extreme Learning Machine Approach 2019A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market 2022A Hybrid XGBoost-MLP Model for Credit Risk Assessment on Digital Supply Chain Finance 2011Simple instance selection for bankruptcy prediction 2016

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