Partial Least Square Discriminant Analysis for bankruptcy prediction

Serrano‐Cinca, Carlos; Nieto, Begoña Gutiérrez

doi:10.1016/j.dss.2012.11.015

Cited by 104 publications

(52 citation statements)

References 45 publications

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“…Corporate and specially banks bankruptcy prediction is an important and widely studied topic in the business intelligence field (Chen, 2011b;Serrano-Cinca & Gutiérrez-Nieto, 2013;Sun, Li, Huang & He, 2014;Zhou, 2013).…”

Section: Review Of Bankruptcy Prediction Modelsmentioning

confidence: 99%

Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks

López‐Iturriaga

Sanz

2015

Expert Systems with Applications

160

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Section: Review Of Bankruptcy Prediction Modelsmentioning

confidence: 99%

Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks

López‐Iturriaga

Sanz

2015

Expert Systems with Applications

160

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“…Examples like K-means or support vector machines (e.g. Serrano et al [30]) are used as algorithms either on scores of a PCA or PLS analysis or directly on the original variables or properties of the present problem [30,31]. Further, an extension of the PLS regression was proposed (see Barker and Rayens [29] or Nocairï et al [29,32]).…”

Section: Choice Of Appropriate Number Of Componentsmentioning

confidence: 99%

NIR and PLS discriminant analysis for predicting the processability of malt during lautering

Krause

Holtz

Gastl

et al. 2014

Eur Food Res Technol

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“…The UCSD data set is a reduced version of a database used in the 2007 Data Mining Contest. The USA database [64] consists of the accounting statements from 8293 banks recorded by the Federal Deposit Insurance Corporation. The common way to assess the performance of financial distress prediction systems when databases are small or medium sized corresponds to k-fold crossvalidation [26].…”

Section: Experimental Set-upmentioning

confidence: 99%

Financial distress prediction using the hybrid associative memory with translation

Cleofas-Sánchez

García

Marqués

et al. 2016

Applied Soft Computing

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This paper presents an alternative technique for financial distress prediction systems. The method is based on a type of neural network, which is called hybrid associative memory with translation. While many different neural network architectures have successfully been used to predict credit risk and corporate failure, the power of associative memories for financial decision-making has not been explored in any depth as yet. The performance of the hybrid associative memory with translation is compared to four traditional neural networks, a support vector machine and a logistic regression model in terms of their prediction capabilities. The experimental results over nine real-life data sets show that the associative memory here proposed constitutes an appropriate solution for bankruptcy and credit risk prediction, performing significantly better than the rest of models under class imbalance and data overlapping conditions in terms of the true positive rate and the geometric mean of true positive and true negative rates.

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Partial Least Square Discriminant Analysis for bankruptcy prediction

Cited by 104 publications

References 45 publications

Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks

Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks

NIR and PLS discriminant analysis for predicting the processability of malt during lautering

Financial distress prediction using the hybrid associative memory with translation

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