Whose Balance Sheet is this? Neural Networks for Banks’ Pattern Recognition
Author
Abstract
Suggested Citation
DOI: 10.32468/be.959
Download full text from publisher
Other versions of this item:
- León, C. & Moreno, José Fernando & Cely, Jorge, 2017. "Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition," Discussion Paper 2017-009, Tilburg University, Center for Economic Research.
- León, C. & Moreno, José Fernando & Cely, Jorge, 2017. "Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition," Other publications TiSEM 75d8648e-9855-4c5c-9aa9-0, Tilburg University, School of Economics and Management.
References listed on IDEAS
- Olmeda, Ignacio & Fernandez, Eugenio, 1997. "Hybrid Classifiers for Financial Multicriteria Decision Making: The Case of Bankruptcy Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 10(4), pages 317-335, November.
- Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445.
- Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
- Angelini, Eliana & di Tollo, Giacomo & Roli, Andrea, 2008. "A neural network approach for credit risk evaluation," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(4), pages 733-755, November.
- Rebecca Wu, 1997. "Neural network models: Foundations and applications to an audit decision problem," Annals of Operations Research, Springer, vol. 75(0), pages 291-301, January.
- Fioramanti, Marco, 2008.
"Predicting sovereign debt crises using artificial neural networks: A comparative approach,"
Journal of Financial Stability, Elsevier, vol. 4(2), pages 149-164, June.
- Marco Fioramanti, 2006. "Predicting Sovereign Debt Crises Using Artificial Neural Networks: A Comparative Approach," ISAE Working Papers 72, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
- Markus Holopainen & Peter Sarlin, 2015. "Toward robust early-warning models: A horse race, ensembles and model uncertainty," Papers 1501.04682, arXiv.org, revised Apr 2016.
- Khediri, Karim Ben & Charfeddine, Lanouar & Youssef, Slah Ben, 2015. "Islamic versus conventional banks in the GCC countries: A comparative study using classification techniques," Research in International Business and Finance, Elsevier, vol. 33(C), pages 75-98.
- Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
- repec:zbw:bofrdp:2009_035 is not listed on IDEAS
- Shorouq Fathi Eletter & Saad Ghaleb Yaseen & Ghaleb Awad Elrefae, 2010. "Neuro-Based Artificial Intelligence Model for Loan Decisions," American Journal of Economics and Business Administration, Science Publications, vol. 2(1), pages 27-34, March.
- Sarlin, Peter & Holopainen, Markus, 2016. "Toward robust early-warning models: a horse race, ensembles and model uncertainty," Working Paper Series 1900, European Central Bank.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- León, Carlos & Barucca, Paolo & Acero, Oscar & Gage, Gerardo & Ortega, Fabio, 2020.
"Pattern recognition of financial institutions’ payment behavior,"
Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
- Carlos León & Paolo Barucca & Oscar Acero & Gerardo Gage & Fabio Ortega, 2020. "Pattern recognition of financial institutions’ payment behavior," Borradores de Economia 1130, Banco de la Republica de Colombia.
- Muhammad Nadim Hanif & Khurrum S. Mughal & Javed Iqbal, 2018. "A Thick ANN Model for Forecasting Inflation," SBP Working Paper Series 99, State Bank of Pakistan, Research Department.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- León, Carlos & Barucca, Paolo & Acero, Oscar & Gage, Gerardo & Ortega, Fabio, 2020.
"Pattern recognition of financial institutions’ payment behavior,"
Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
- Carlos León & Paolo Barucca & Oscar Acero & Gerardo Gage & Fabio Ortega, 2020. "Pattern recognition of financial institutions’ payment behavior," Borradores de Economia 1130, Banco de la Republica de Colombia.
- Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
- Carlos León & Fabio Ortega, 2018.
"Nowcasting Economic Activity with Electronic Payments Data: A Predictive Modeling Approach,"
Revista de Economía del Rosario, Universidad del Rosario, vol. 21(2), pages 381-407, December.
- Carlos León & Fabio Ortega, 2018. "Nowcasting economic activity with electronic payments data: A predictive modeling approach," Borradores de Economia 1037, Banco de la Republica de Colombia.
- Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
- Alina Mihaela Dima & Simona Vasilache, 2016. "Credit Risk modeling for Companies Default Prediction using Neural Networks," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 127-143, September.
- Tjeerd M. Boonman & Jan P. A. M. Jacobs & Gerard H. Kuper & Alberto Romero, 2019.
"Early Warning Systems for Currency Crises with Real-Time Data,"
Open Economies Review, Springer, vol. 30(4), pages 813-835, September.
- Tjeerd M. Boonman & Jan P.A.M. Jacobs & Gerard H. Kuper & Alberto Romero, 2017. "Early Warning Systems for Currency Crises with Real-Time Data," CIRANO Working Papers 2017s-18, CIRANO.
- Halil Erdal & Aykut Ekinci, 2013. "A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures," Computational Economics, Springer;Society for Computational Economics, vol. 42(2), pages 199-215, August.
- repec:zbw:bofrdp:2009_035 is not listed on IDEAS
- Iwanicz-Drozdowska Małgorzata & Kurowski Łukasz, 2021. "Keep your friends close and your enemies closer – the case of monetary policy and financial imbalances," German Economic Review, De Gruyter, vol. 22(4), pages 383-414, November.
- Eleftherios Giovanis, 2012.
"Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA,"
Economic Analysis and Policy, Elsevier, vol. 42(1), pages 79-96, March.
- Giovanis, Eleftherios, 2012. "Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA," MPRA Paper 71218, University Library of Munich, Germany.
- Nikita Moiseev & Aleksander Sorokin & Natalya Zvezdina & Alexey Mikhaylov & Lyubov Khomyakova & Mir Sayed Shah Danish, 2021. "Credit Risk Theoretical Model on the Base of DCC-GARCH in Time-Varying Parameters Framework," Mathematics, MDPI, vol. 9(19), pages 1-12, September.
- Christian Menden & Christian R. Proaño, 2017.
"Dissecting the financial cycle with dynamic factor models,"
Quantitative Finance, Taylor & Francis Journals, vol. 17(12), pages 1965-1994, December.
- Christian Menden & Christian R. Proaño, 2017. "Dissecting the financial cycle with dynamic factor models," IMK Working Paper 183-2017, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
- Menden, Christian & Proaño, Christian R., 2017. "Dissecting the financial cycle with dynamic factor models," BERG Working Paper Series 126, Bamberg University, Bamberg Economic Research Group.
- Virág, Miklós & Kristóf, Tamás, 2005. "Az első hazai csődmodell újraszámítása neurális hálók segítségével [Recalculation of the first Hungarian bankruptcy-prediction model using neural networks]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 144-162.
- fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
- Romero Martínez, Mariano & Carmona Ibáñez, Pedro & Pozuelo Campillo, José, 2021. "Utilidad del Deep Learning en la predicción del fracaso empresarial en el ámbito europeo || The usefulness of Deep Learning in the prediction of business failure at the European level," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 32(1), pages 392-414, December.
- Ilyes Abid & Rim Ayadi & Khaled Guesmi & Farid Mkaouar, 2022. "A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction," Annals of Operations Research, Springer, vol. 313(2), pages 605-623, June.
- Söhnke M. Bartram & Jürgen Branke & Mehrshad Motahari, 2020.
"Artificial intelligence in asset management,"
Working Papers
20202001, Cambridge Judge Business School, University of Cambridge.
- Bartram, Söhnke & Branke, Jürgen & Motahari, Mehrshad, 2020. "Artificial Intelligence in Asset Management," CEPR Discussion Papers 14525, C.E.P.R. Discussion Papers.
- Umberto Collodel, 2021. "Finding a needle in a haystack: Do Early Warning Systems for Sudden Stops work?," PSE Working Papers halshs-03185520, HAL.
- Borchert, Philipp & Coussement, Kristof & De Caigny, Arno & De Weerdt, Jochen, 2023. "Extending business failure prediction models with textual website content using deep learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 348-357.
- Jakub Horak & Tomas Krulicky & Zuzana Rowland & Veronika Machova, 2020. "Creating a Comprehensive Method for the Evaluation of a Company," Sustainability, MDPI, vol. 12(21), pages 1-23, November.
- du Plessis, Emile, 2022. "Multinomial modeling methods: Predicting four decades of international banking crises," Economic Systems, Elsevier, vol. 46(2).
More about this item
Keywords
supervised learning; machine learning; artificial neural networks; classification;All these keywords.
JEL classification:
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
- M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ACC-2016-09-18 (Accounting and Auditing)
- NEP-BAN-2016-09-18 (Banking)
- NEP-CMP-2016-09-18 (Computational Economics)
- NEP-SOG-2016-09-18 (Sociology of Economics)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdr:borrec:959. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Clorith Angélica Bahos Olivera (email available below). General contact details of provider: https://edirc.repec.org/data/brcgvco.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.