The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Partial Least Square Discriminant Analysis for bankruptcy prediction
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“However, none of these variables incorporates non-financial variables to predict bank failure.”
From Bank failure prediction: corporate governance and financial indicators (2023) · cites this paper as Serrano-Cinca and Gutiérrez-Nieto 2013
Citations
Cites
A Caution Regarding Rules of Thumb for Variance Inflation Factors
O’brien, R. M. (2007) · Quality & Quantity · doi:10.1007/s11135-006-9018-6 · Cited by 11,217
Research works printed but not linked: 49
- Adya, 1998, How effective are neural networks at forecasting and prediction? A review and evaluation, International Journal of Forecasting, 17, 48
- Alfaro, 2008, Bankruptcy forecasting: an empirical comparison of AdaBoost and neural networks, Decision Support Systems, 45, 110, 10.1016/j.dss.2007.12.002 · DOI
- Altman, 1968, Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance, 23, 589, 10.1111/j.1540-6261.1968.tb00843.x · DOI · Paper page
- Austin, 2004, Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality, Journal of Clinical Epidemiology, 57, 1138, 10.1016/j.jclinepi.2004.04.003 · DOI
- Barker, 2003, Partial least squares for discrimination, Journal of Chemometrics, 17, 166, 10.1002/cem.785 · DOI
- Beaver, 1966, Financial ratios as predictors of failure, Journal of Accounting Research, 4, 71, 10.2307/2490171 · DOI · Paper page
- Berger, 1997, Efficiency of financial institutions: International survey and directions for future research, European Journal of Operational Research, 98, 175, 10.1016/S0377-2217(96)00342-6 · DOI
- Cassel, 1999, Robustness of partial least-squares method for estimating latent variable quality structures, Journal of Applied Statistics, 26, 435, 10.1080/02664769922322 · DOI
- Chatterjee, 1986, Influential observations, high leverage points, and outliers in linear regression, Statistical Science, 1, 379
- Chen, 2010, Automatic keyword prediction using Google similarity distance, Expert Systems with Applications, 37, 1928, 10.1016/j.eswa.2009.07.016 · DOI
- Curram, 1994, Neural networks, decision tree induction and discriminant analysis: an empirical comparison, Journal of the Operational Research Society, 45, 440, 10.1057/jors.1994.62 · DOI
- Deakin, 1976, Distributions of financial accounting ratios: some empirical evidence, The Accounting Review, 51, 90
- Demyanyk, 2010, Financial crises and bank failures: a review of prediction methods, Omega, 38, 315, 10.1016/j.omega.2009.09.007 · DOI
- Domingos, 1997, On the optimality of the simple Bayesian classifier under zero–one loss, Machine Learning, 29, 103, 10.1023/A:1007413511361 · DOI
- du Jardin, 2011, Predicting corporate bankruptcy using a self-organizing map: an empirical study to improve the forecasting horizon of a financial failure model, Decision Support Systems, 51, 701, 10.1016/j.dss.2011.04.001 · DOI
- Eisenbeis, 1977, Pitfalls in the application of discriminant analysis in business, finance, and economics, Journal of Finance, 32, 875, 10.1111/j.1540-6261.1977.tb01995.x · DOI
- Ezzamel, 1987, On the distributional properties of financial ratios, Journal of Business Finance & Accounting, 14, 463, 10.1111/j.1468-5957.1987.tb00107.x · DOI
- Fan, 2000, Selecting bankruptcy predictors using a support vector machine approach, vol. 6, 354
- Ferri, 2009, An experimental comparison of performance measures for classification, Pattern Recognition Letters, 30, 27, 10.1016/j.patrec.2008.08.010 · DOI · Paper page
- Foster, 2004, Variable selection in data mining: building a predictive model for bankruptcy, Journal of the American Statistical Association, 99, 303, 10.1198/016214504000000287 · DOI
- Garthwaite, 1994, An interpretation of partial least squares, Journal of the American Statistical Association, 89, 122, 10.1080/01621459.1994.10476452 · DOI
- Hendry, 1978, Serial correlation as a convenient simplification, not a nuisance: a comment on a study of the demand for money by the Bank of England, The Economic Journal, 88, 549, 10.2307/2232053 · DOI
- Hornik, 1989, Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359, 10.1016/0893-6080(89)90020-8 · DOI · Paper page
- Hung, 2009, A selective ensemble based on expected probabilities for bankruptcy prediction, Expert Systems with Applications, 36, 5297, 10.1016/j.eswa.2008.06.068 · DOI
- Kjeldahl, 2010, Some common misunderstandings in chemometrics, Journal of Chemometrics, 24, 558, 10.1002/cem.1346 · DOI
- Kuhn, 2008, Building predictive models in R using the caret package, Journal of Statistical Software, 28, 1, 10.18637/jss.v028.i05 · DOI · Paper page
- Lau, 1987, A five-state financial distress prediction model, Journal of Accounting Research, 25, 127, 10.2307/2491262 · DOI
- Lo, 1986, Logit versus discriminant analysis. A specification test and application to corporate bankruptcies, Journal of Econometrics, 31, 151, 10.1016/0304-4076(86)90046-1 · DOI
- Marais, 1984, The experimental design of classification models: an application of recursive partitioning and bootstrapping to commercial bank loan classifications, Journal of Accounting Research, 22, 87, 10.2307/2490861 · DOI
- Martens, 2011, B, Baesens B, Performance of classification models from a user perspective, Decision Support Systems, 51, 782
- Morlini, 2006, On multicollinearity and concurvity in some nonlinear multivariate models, Statistical Methods and Applications, 15, 3, 10.1007/s10260-006-0005-9 · DOI
- Mukkamala, 2006, Computational intelligent techniques for financial distress detection, International Journal of Computational Intelligence Research, 2, 60
- Ohlson, 1980, Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research, 18, 109, 10.2307/2490395 · DOI
- Olden, 2002, Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks, Ecological Modelling, 154, 135, 10.1016/S0304-3800(02)00064-9 · DOI
- Olson, 2012, Comparative analysis of data mining methods for bankruptcy prediction, Decision Support Systems, 52, 464, 10.1016/j.dss.2011.10.007 · DOI
- Park, 2002, A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction, Expert Systems with Applications, 23, 255, 10.1016/S0957-4174(02)00045-3 · DOI
- Pérez-Enciso, 2003, Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach, Human Genetics, 112, 581
- Rakotomamonjy, 2003, Variable selection using SVM-based criteria, Journal of Machine Learning Research, 3, 1357
- Ravi Kumar, 2007, Bankruptcy prediction in banks and firms via statistical and intelligent techniques. A review, European Journal of Operational Research, 180, 1, 10.1016/j.ejor.2006.08.043 · DOI · Paper page
- Sarkar, 2001, Bayesian models for early warning of bank failures, Management Science, 47, 1457, 10.1287/mnsc.47.11.1457.10253 · DOI
- Serrano-Cinca, 1994, Self organizing neural networks for financial diagnosis, Decision Support Systems, 17, 227, 10.1016/0167-9236(95)00033-X · DOI
- Shin, 2005, An application of support vector machines in bankruptcy prediction model, Expert Systems with Applications, 28, 127, 10.1016/j.eswa.2004.08.009 · DOI
- Sun, 2007, Using Bayesian networks for bankruptcy prediction: some methodological issues, European Journal of Operational Research, 180, 738, 10.1016/j.ejor.2006.04.019 · DOI
- Tam, 1992, Managerial applications of neural networks: the case of bank failure predictions, Management Science, 38, 926, 10.1287/mnsc.38.7.926 · DOI
- Westerhuis, 2008, Assessment of PLSDA cross validation, Metabolomics, 4, 81, 10.1007/s11306-007-0099-6 · DOI · Paper page
- Wold, 2001, PLS-regression: a basic tool of chemometrics, Chemometrics and Intelligent Laboratory Systems, 58, 109, 10.1016/S0169-7439(01)00155-1 · DOI · Paper page
- Yang, 2011, Using partial least squares and support vector machines for bankruptcy prediction, Expert Systems with Applications, 38, 8336, 10.1016/j.eswa.2011.01.021 · DOI
- Zavgren, 1983, The prediction of corporate failure: the state of the art, Journal of Accounting Literature, 2, 1
- Zmijewski, 1984, Methodological issues related to the estimation of financial distress prediction models, Journal of Accounting Research, 22, 59, 10.2307/2490859 · DOI
Cited by
Sorted by citations
Bank failure prediction: corporate governance and financial indicators
Alzayed, N., Eskandari, R., & Yazdifar, H. (2023) · Review of Quantitative Finance and Accounting · doi:10.1007/s11156-023-01158-z · Cited by 23
Metadata
- Authors
- Serrano-Cinca, C., & Gutiérrez-Nieto, B.
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 54
- Issue
- 3
- Pages
- 1245-1255
- ISSN
- 0167-9236 (ISSN-L)
- OpenAlex
- W1971141266
- Cited by
- 139
- References
- 1
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 13 · 2023: 12 · 2022: 16 · 2021: 13 · 2020: 15 · 2019: 11 · 2018: 17 · 2017: 9 · 2016: 6 · 2015: 7 · 2014: 6 · 2013: 4
- FWCI
- 19.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Decision Support Systems (publishedVersion)
- Funders
- Ministerio de Educación, Cultura y Deporte
- European Regional Development Fund
- Topics
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Social Sciences › Economics, Econometrics and Finance › Finance › Banking stability, regulation, efficiency
- Keywords
- PLS-DA, bankruptcy prediction, 2008 financial crisis, banking crisis, multicollinearity, bank solvency, linear discriminant analysis, support vector machine, classification accuracy comparison
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review