Bankruptcy Prediction by Supervised Machine Learning Techniques

Tsai, Chih‐Fong; Lu, Yu-Hsin; Hsu, Yu‐Feng

doi:10.4018/9781616928650.ch007

Cited by 1 publication

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“…They are widely used in the literature (see e.g. Hsieh, 2005;Tsai et al, 2011;West et al, 2005). The information contained in these data sets is shown in Table I.…”

Section: Methodsmentioning

confidence: 99%

“…outliers) to reduce prediction errors during the training of a classifier (Hsieh, 2005). Related work has shown that advanced machine learning techniques outperform single classifiers (West et al, 2005;Tsai et al, 2011). For instance, Hsieh (2005) constructed a hybrid classifier by combining the k-means clustering method and a neural network model, which shows that the hybrid classifier can provide greater prediction accuracy than the single neural network model.…”

Section: Introductionmentioning

confidence: 99%

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Modeling credit scoring using neural network ensembles

Tsai

Hung

2014

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Purpose -Credit scoring is important for financial institutions in order to accurately predict the likelihood of business failure. Related studies have shown that machine learning techniques, such as neural networks, outperform many statistical approaches to solving this type of problem, and advanced machine learning techniques, such as classifier ensembles and hybrid classifiers, provide better prediction performance than single machine learning based classification techniques. However, it is not known which type of advanced classification technique performs better in terms of financial distress prediction. The paper aims to discuss these issues. Design/methodology/approach -This paper compares neural network ensembles and hybrid neural networks over three benchmarking credit scoring related data sets, which are Australian, German, and Japanese data sets. Findings -The experimental results show that hybrid neural networks and neural network ensembles outperform the single neural network. Although hybrid neural networks perform slightly better than neural network ensembles in terms of predication accuracy and errors with two of the data sets, there is no significant difference between the two types of prediction models. Originality/value -The originality of this paper is in comparing two types of advanced classification techniques, i.e. hybrid and ensemble learning techniques, in terms of financial distress prediction.

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“…They are widely used in the literature (see e.g. Hsieh, 2005;Tsai et al, 2011;West et al, 2005). The information contained in these data sets is shown in Table I.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%