Using neural network ensembles for bankruptcy prediction and credit scoring

Tsai, Chih‐Fong; Wu, Jhen-Wei

doi:10.1016/j.eswa.2007.05.019

Cited by 438 publications

(243 citation statements)

References 22 publications

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“…The most typical deficiency is that those works do not check for normality of data (e.g. Ben-David and Frank 2009; Sun 2009, 2013;Martens et al 2007;Tsai and Wu 2008;Lu et al 2013;Sun and Shenoy 2007). Another problem refers to the fact that several works employ this parametric test to compare multiple algorithms (e.g.…”

Section: Statistical Tests Of Significancementioning

confidence: 99%

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An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

2014

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Over the last years, it has been observed an increasing interest of the finance and business communities in any application tool related to the prediction of credit and bankruptcy risk, probably due to the need of more robust decision-making systems capable of managing and analyzing complex data. As a result, plentiful techniques have been developed with the aim of producing accurate prediction models that are able to tackle these issues. However, the design of experiments to assess and compare these models has attracted little attention so far, even though it plays an important role in validating and supporting the theoretical evidence of performance. The experimental design should be done carefully for the results to hold significance; otherwise, it might be a potential source of misleading and contradictory conclusions about the benefits of using a particular prediction system. In this work, we review more than 140 papers published in refereed journals within the period 2000-2013, putting the emphasis on the bases of the experimental design in credit scoring and bankruptcy prediction applications. We provide some caveats and guidelines for the usage of databases, data splitting methods, performance evaluation metrics and hypothesis testing procedures in order to converge on a systematic, consistent validation standard.

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Section: Statistical Tests Of Significancementioning

confidence: 99%

“…Another problem refers to the fact that several works employ this parametric test to compare multiple algorithms (e.g. Ravisankar et al 2010;Tsai and Wu 2008;Ribeiro et al 2012), even though not being suitable to carry out this type of comparisons.…”

Section: Statistical Tests Of Significancementioning

confidence: 99%

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

2014

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“…The Australia, German and Japan credit data sets are publicly available at the UCI Machine Learning repository (http://archive.ics.uci.edu/ml/datasets.html). The Japan data set is commonly mistaken for the Australia data set, for example, by Tsai and Wu (2008) and Nanni and Lumini (2009). The Iran data set is an updated version of a data set that appears in Sabzevari et al (2007).…”

Section: Data Setsmentioning

confidence: 99%

Using semi-supervised classifiers for credit scoring

Kennedy¹,

Namee²,

Delany³

2013

Journal of the Operational Research Society

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“…A neural network is inspired by the way the human brain structured and how it acquires knowledge from its environment through a learning process (Tsai & Wu, 2008). The knowledge acquired in a neural network input is stored by the interneuron connection strengths (Haykin, 2001).…”

Section: Neural Network Overviewmentioning

confidence: 99%