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Managerial Applications of Neural Networks: The Case of Bank Failure Predictions

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Cited by:

  1. Matthew Smith & Francisco Alvarez, 2022. "Predicting Firm-Level Bankruptcy in the Spanish Economy Using Extreme Gradient Boosting," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 263-295, January.
  2. 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.
  3. José Willer Prado & Valderí Castro Alcântara & Francisval Melo Carvalho & Kelly Carvalho Vieira & Luiz Kennedy Cruz Machado & Dany Flávio Tonelli, 2016. "Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1007-1029, March.
  4. Amarda Cano, 2020. "Evolution of Public Debt in Albania during 1990-2017 and its impact on the Economic Growth," European Journal of Marketing and Economics Articles, Revistia Research and Publishing, vol. 4, January -.
  5. Fosso Wamba, Samuel & Bawack, Ransome Epie & Guthrie, Cameron & Queiroz, Maciel M. & Carillo, Kevin Daniel André, 2021. "Are we preparing for a good AI society? A bibliometric review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
  6. En-Der Su & Shih-Ming Huang, 2010. "Comparing Firm Failure Predictions Between Logit, KMV, and ZPP Models: Evidence from Taiwan’s Electronics Industry," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 17(3), pages 209-239, September.
  7. Pavur, Robert, 2002. "A comparative study of the effect of the position of outliers on classical and nontraditional approaches to the two-group classification problem," European Journal of Operational Research, Elsevier, vol. 136(3), pages 603-615, February.
  8. Philippe Paquet, 1997. "L'utilisation des réseaux de neurones artificiels en finance," Post-Print halshs-02096266, HAL.
  9. Sudhir Nanda & Parag Pendharkar, 2001. "Linear models for minimizing misclassification costs in bankruptcy prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(3), pages 155-168, September.
  10. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
  11. Palocsay, Susan W. & Stevens, Scott P. & Brookshire, Robert G. & Sacco, William J. & Copes, Wayne S. & Buckman, Robert F. & Smith, J. Stanley, 1996. "Using neural networks for trauma outcome evaluation," European Journal of Operational Research, Elsevier, vol. 93(2), pages 369-386, September.
  12. du Jardin, Philippe & Séverin, Eric, 2012. "Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time," European Journal of Operational Research, Elsevier, vol. 221(2), pages 378-396.
  13. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
  14. Musdholifah Musdholifah & Ulil Hartono, 2017. "Assesing Early Warning System Model for Banking Crisis in ASEAN Countries," International Journal of Economics and Financial Issues, Econjournals, vol. 7(4), pages 358-364.
  15. Haider A. Khan, 2004. "General Conclusions: From Crisis to a Global Political Economy of Freedom," Palgrave Macmillan Books, in: Global Markets and Financial Crises in Asia, chapter 9, pages 193-211, Palgrave Macmillan.
  16. du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
  17. Haider A. Khan, 2002. "Can Banks Learn to Be Rational?," CIRJE F-Series CIRJE-F-151, CIRJE, Faculty of Economics, University of Tokyo.
  18. Malhotra, Manoj K. & Sharma, Subhash & Nair, Satish S., 1999. "Decision making using multiple models," European Journal of Operational Research, Elsevier, vol. 114(1), pages 1-14, April.
  19. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
  20. Chiang, W. -C. & Urban, T. L. & Baldridge, G. W., 1996. "A neural network approach to mutual fund net asset value forecasting," Omega, Elsevier, vol. 24(2), pages 205-215, April.
  21. Wu, Desheng(Dash) & Liang, Liang & Yang, Zijiang, 2008. "Analyzing the financial distress of Chinese public companies using probabilistic neural networks and multivariate discriminate analysis," Socio-Economic Planning Sciences, Elsevier, vol. 42(3), pages 206-220, September.
  22. Seyma Caliskan Cavdar & Alev Dilek Aydin, 2015. "An Empirical Analysis for the Prediction of a Financial Crisis in Turkey through the Use of Forecast Error Measures," JRFM, MDPI, vol. 8(3), pages 1-18, August.
  23. Yi-Ting Chen & Edward W. Sun & Yi-Bing Lin, 2020. "Machine learning with parallel neural networks for analyzing and forecasting electricity demand," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 569-597, August.
  24. Kamesh Korangi & Christophe Mues & Cristi'an Bravo, 2021. "A transformer-based model for default prediction in mid-cap corporate markets," Papers 2111.09902, arXiv.org, revised Apr 2023.
  25. Runchi Zhang & Zhiyi Qiu, 2020. "Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-35, June.
  26. Sun, Lili & Shenoy, Prakash P., 2007. "Using Bayesian networks for bankruptcy prediction: Some methodological issues," European Journal of Operational Research, Elsevier, vol. 180(2), pages 738-753, July.
  27. Kulkarni, Uday R. & Kiang, Melody Y., 1995. "Dynamic grouping of parts in flexible manufacturing systems -- a self-organizing neural networks approach," European Journal of Operational Research, Elsevier, vol. 84(1), pages 192-212, July.
  28. Błażej Prusak, 2018. "Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries," IJFS, MDPI, vol. 6(3), pages 1-28, June.
  29. Yu-Shan Chen & Ke-Chiun Chang, 2009. "Using neural network to analyze the influence of the patent performance upon the market value of the US pharmaceutical companies," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(3), pages 637-655, September.
  30. James R. Coakley & Carol E. Brown, 2000. "Artificial neural networks in accounting and finance: modeling issues," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(2), pages 119-144, June.
  31. Davalos, Sergio & Gritta, Richard D. & Adrangi, Bahram, 2007. "Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 46(2).
  32. Wolfgang Karl Härdle & Dedy Dwi Prastyo & Christian Hafner, 2012. "Support Vector Machines with Evolutionary Feature Selection for Default Prediction," SFB 649 Discussion Papers SFB649DP2012-030, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  33. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
  34. Balakrishnan, Nagraj & Chakravarty, Amiya K. & Ghose, Sanjoy, 1997. "Role of design-philosophies in interfacing manufacturing with marketing," European Journal of Operational Research, Elsevier, vol. 103(3), pages 453-469, December.
  35. Laitinen, Erkki K. & Laitinen, Teija, 2000. "Bankruptcy prediction: Application of the Taylor's expansion in logistic regression," International Review of Financial Analysis, Elsevier, vol. 9(4), pages 327-349.
  36. Liviu Tudor & Mădălina Ecaterina Popescu & Marin Andreica, 2015. "A Decision Support System to Predict Financial Distress. The Case Of Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 170-179, December.
  37. Yan Zhang & Peter Trubey, 2019. "Machine Learning and Sampling Scheme: An Empirical Study of Money Laundering Detection," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 1043-1063, October.
  38. Gupta, V. K. & Chen, J. G. & Murtaza, M. B., 1997. "A learning vector quantization neural network model for the classification of industrial construction projects," Omega, Elsevier, vol. 25(6), pages 715-727, December.
  39. Jorge Perez-Rodriguez & Salvador Torra & Julian Andrada-Felix, 2005. "Are Spanish Ibex35 stock future index returns forecasted with non-linear models?," Applied Financial Economics, Taylor & Francis Journals, vol. 15(14), pages 963-975.
  40. Greta Falavigna, 2006. "Models for Default Risk Analysis: Focus on Artificial Neural Networks, Model Comparisons, Hybrid Frameworks," CERIS Working Paper 200610, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
  41. Tonatiuh Peña & Serafín Martínez & Bolanle Abudu, 2011. "Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques," Dynamic Modeling and Econometrics in Economics and Finance, in: Herbert Dawid & Willi Semmler (ed.), Computational Methods in Economic Dynamics, pages 109-131, Springer.
  42. P. Du Jardin & E. Séverin, 2011. "Predicting Corporate Bankruptcy Using Self-Organising map: An empirical study to Improve the Forecasting horizon of financial failure model," Post-Print hal-00801878, HAL.
  43. Selwyn Piramuthu, 1999. "Feature Selection for Financial Credit-Risk Evaluation Decisions," INFORMS Journal on Computing, INFORMS, vol. 11(3), pages 258-266, August.
  44. Yu-Chiang Hu & Jake Ansell, 2009. "Retail default prediction by using sequential minimal optimization technique," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(8), pages 651-666.
  45. Raffaella Calabrese & Johan A. Elkink & Paolo S. Giudici, 2017. "Measuring bank contagion in Europe using binary spatial regression models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(12), pages 1503-1511, December.
  46. Andreas Charitou & Chris Charalambous, 1996. "The Prediction of Earnings Using Financial Statement Information: Empirical Evidence With Logit Models and Artificial Neural Networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 5(4), pages 199-215, December.
  47. Parag Pendharkar & Sudhir Nanda, 2006. "A misclassification cost‐minimizing evolutionary–neural classification approach," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(5), pages 432-447, August.
  48. Beynon, Malcolm J. & Peel, Michael J., 2001. "Variable precision rough set theory and data discretisation: an application to corporate failure prediction," Omega, Elsevier, vol. 29(6), pages 561-576, December.
  49. O' Keefe, Robert M. & Preece, Alun D., 1996. "The development, validation and implementation of knowledge-based systems," European Journal of Operational Research, Elsevier, vol. 92(3), pages 458-473, August.
  50. Donaldson, R. Glen & Kamstra, Mark, 1997. "An artificial neural network-GARCH model for international stock return volatility," Journal of Empirical Finance, Elsevier, vol. 4(1), pages 17-46, January.
  51. 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.
  52. Paul M. Swamidass & Satish S. Nair & Sanjay I. Mistry, 1999. "The Use of a Neural Factory to Investigate the Effect of Product Line Width on Manufacturing Performance," Management Science, INFORMS, vol. 45(11), pages 1524-1538, November.
  53. Qi, Min & Yang, Sha, 2003. "Forecasting consumer credit card adoption: what can we learn about the utility function?," International Journal of Forecasting, Elsevier, vol. 19(1), pages 71-85.
  54. Andrea Bedin & Monica Billio & Michele Costola & Loriana Pelizzon, 2019. "Credit Scoring in SME Asset-Backed Securities: An Italian Case Study," JRFM, MDPI, vol. 12(2), pages 1-28, May.
  55. Setiono, Rudy & Thong, James Y. L., 2004. "An approach to generate rules from neural networks for regression problems," European Journal of Operational Research, Elsevier, vol. 155(1), pages 239-250, May.
  56. du Jardin, Philippe, 2008. "Bankruptcy prediction and neural networks: The contribution of variable selection methods," MPRA Paper 44384, University Library of Munich, Germany.
  57. Sueyoshi, Toshiyuki, 2006. "DEA-Discriminant Analysis: Methodological comparison among eight discriminant analysis approaches," European Journal of Operational Research, Elsevier, vol. 169(1), pages 247-272, February.
  58. Soo Young Kim, 2018. "Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation," Service Business, Springer;Pan-Pacific Business Association, vol. 12(3), pages 483-503, September.
  59. Parag C. Pendharkar, 2011. "Probabilistic Approaches For Credit Screening And Bankruptcy Prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(4), pages 177-193, October.
  60. 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.
  61. Anton Andriyashin, 2008. "Stock Picking via Nonsymmetrically Pruned Binary Decision Trees," SFB 649 Discussion Papers SFB649DP2008-035, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  62. Şaban Çelik & Bora Aktan & Bruce Burton, 2022. "Firm dynamics and bankruptcy processes: A new theoretical model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 567-591, April.
  63. Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2009. "Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(6), pages 512-534.
  64. Chrysovalantis Gaganis & Fotios Pasiouras & Charalambos Spathis & Constantin Zopounidis, 2007. "A comparison of nearest neighbours, discriminant and logit models for auditing decisions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 23-40, January.
  65. Feuerriegel, Stefan & Gordon, Julius, 2019. "News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions," European Journal of Operational Research, Elsevier, vol. 272(1), pages 162-175.
  66. Daniela Carlucci & Paolo Renna & Giovanni Schiuma, 2013. "Evaluating service quality dimensions as antecedents to outpatient satisfaction using back propagation neural network," Health Care Management Science, Springer, vol. 16(1), pages 37-44, March.
  67. Boylu, Fidan & Aytug, Haldun & Koehler, Gary J., 2010. "Induction over constrained strategic agents," European Journal of Operational Research, Elsevier, vol. 203(3), pages 698-705, June.
  68. Daria S. Leonteva, 2022. "Using Market Indicators to Refine Estimates of Corporate Bankruptcy Probabilities," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 74-90, December.
  69. Mark Cecchini & Haldun Aytug & Gary J. Koehler & Praveen Pathak, 2010. "Detecting Management Fraud in Public Companies," Management Science, INFORMS, vol. 56(7), pages 1146-1160, July.
  70. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
  71. Selwyn Piramuthu & Harish Ragavan & Michael J. Shaw, 1998. "Using Feature Construction to Improve the Performance of Neural Networks," Management Science, INFORMS, vol. 44(3), pages 416-430, March.
  72. Mselmi, Nada & Lahiani, Amine & Hamza, Taher, 2017. "Financial distress prediction: The case of French small and medium-sized firms," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 67-80.
  73. Kattan, MW & Cooper, RB, 1998. "The predictive accuracy of computer-based classification decision techniques.A review and research directions," Omega, Elsevier, vol. 26(4), pages 467-482, August.
  74. Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.
  75. Fazelina Sahul Hamid, 2013. "The Effect of Reliance on International Funding on Banking Fragility: Evidence from East Asia," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 7(1), pages 29-60, February.
  76. 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.
  77. Andreas Charitou & Evi Neophytou & Chris Charalambous, 2004. "Predicting corporate failure: empirical evidence for the UK," European Accounting Review, Taylor & Francis Journals, vol. 13(3), pages 465-497.
  78. Sudarshan Kumar & Sobhesh Kumar Agarwalla & Jayanth R. Varma & Vineet Virmani, 2023. "Harvesting the volatility smile in a large emerging market: A Dynamic Nelson–Siegel approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(11), pages 1615-1644, November.
  79. Lin, Fengyi & Yeh, Ching Chiang & Lee, Meng Yuan, 2013. "A Hybrid Business Failure Prediction Model Using Locally Linear Embedding And Support Vector Machines," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 82-97, March.
  80. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
  81. Raveh, Adi, 2000. "The Greek banking system: Reanalysis of performance," European Journal of Operational Research, Elsevier, vol. 120(3), pages 525-534, February.
  82. Tarun K. Sen & Andrew M. Gibbs, 1994. "An Evaluation of the Corporate Takeover Model Using Neural Networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 3(4), pages 279-292, December.
  83. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
  84. Greta Falavigna, 2008. "Nouveaux instruments d’évaluation pour le risque financier d’entreprise," CERIS Working Paper 200801, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
  85. 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.
  86. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
  87. Wolfgang K. Härdle & Rouslan A. Moro & Dorothea Schäfer, 2004. "Rating Companies with Support Vector Machines," Discussion Papers of DIW Berlin 416, DIW Berlin, German Institute for Economic Research.
  88. Palocsay, Susan W. & Wang, Ping & Brookshire, Robert G., 2000. "Predicting criminal recidivism using neural networks," Socio-Economic Planning Sciences, Elsevier, vol. 34(4), pages 271-284, December.
  89. Thomas E. McKee, 2003. "Rough sets bankruptcy prediction models versus auditor signalling rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(8), pages 569-586.
  90. Su-Han Woo & Min-Su Kwon & Kum Fai Yuen, 2021. "Financial determinants of credit risk in the logistics and shipping industries," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 268-290, June.
  91. Barniv, Ran & Mehrez, Abraham & Kline, Douglas M., 2000. "Confidence intervals for controlling the probability of bankruptcy," Omega, Elsevier, vol. 28(5), pages 555-565, October.
  92. Montagno, Ray & Sexton, Randall S. & Smith, Brien N., 2002. "Using neural networks for identifying organizational improvement strategies," European Journal of Operational Research, Elsevier, vol. 142(2), pages 382-395, October.
  93. Fidan Boylu & Haldun Aytug & Gary J. Koehler, 2010. "Induction over Strategic Agents," Information Systems Research, INFORMS, vol. 21(1), pages 170-189, March.
  94. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
  95. Sancho Salcedo‐Sanz & Mario DePrado‐Cumplido & María Jesús Segovia‐Vargas & Fernando Pérez‐Cruz & Carlos Bousoño‐Calzón, 2004. "Feature selection methods involving support vector machines for prediction of insolvency in non‐life insurance companies," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(4), pages 261-281, October.
  96. 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.
  97. Adam Fadlalla & Chien-Hua Lin, 2001. "An Analysis of the Applications of Neural Networks in Finance," Interfaces, INFORMS, vol. 31(4), pages 112-122, August.
  98. Pendharkar, Parag C., 2002. "A computational study on the performance of artificial neural networks under changing structural design and data distribution," European Journal of Operational Research, Elsevier, vol. 138(1), pages 155-177, April.
  99. Bose, Indranil & Pal, Raktim, 2006. "Predicting the survival or failure of click-and-mortar corporations: A knowledge discovery approach," European Journal of Operational Research, Elsevier, vol. 174(2), pages 959-982, October.
  100. Rogelio A. Mancisidor & Kjersti Aas, 2022. "Multimodal Generative Models for Bankruptcy Prediction Using Textual Data," Papers 2211.08405, arXiv.org, revised Feb 2024.
  101. Klein, B. D. & Rossin, D. F., 1999. "Data quality in neural network models: effect of error rate and magnitude of error on predictive accuracy," Omega, Elsevier, vol. 27(5), pages 569-582, October.
  102. Ioannis Anagnostopoulos & Anas Rizeq, 2021. "Conventional and neural network target‐matching methods dynamics: The information technology mergers and acquisitions market in the USA," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(2), pages 97-118, April.
  103. Maria Krambia-Kapardis & Chris Christodoulou & Michalis Agathocleous, 2010. "Neural networks: the panacea in fraud detection?," Managerial Auditing Journal, Emerald Group Publishing, vol. 25(7), pages 659-678, July.
  104. Obeua S. Persons, 1999. "Using Financial Information to Differentiate Failed vs. Surviving Finance Companies in Thailand: An Implication for Emerging Economies," Multinational Finance Journal, Multinational Finance Journal, vol. 3(2), pages 127-145, June.
  105. Zhu, Weidong & Zhang, Tianjiao & Wu, Yong & Li, Shaorong & Li, Zhimin, 2022. "Research on optimization of an enterprise financial risk early warning method based on the DS-RF model," International Review of Financial Analysis, Elsevier, vol. 81(C).
  106. Sexton, Randall S. & McMurtrey, Shannon & Cleavenger, Dean, 2006. "Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem," European Journal of Operational Research, Elsevier, vol. 168(3), pages 1009-1018, February.
  107. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
  108. Thomas E. Mckee, 2000. "Developing a bankruptcy prediction model via rough sets theory," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(3), pages 159-173, September.
  109. D. J. E. Baestaens, 1997. "Comment," The European Journal of Finance, Taylor & Francis Journals, vol. 3(3), pages 203-224.
  110. Puertas Medina, Rosa & Selva, Maria Luisa Martí, 2013. "Análise do credit scoring," RAE - Revista de Administração de Empresas, FGV-EAESP Escola de Administração de Empresas de São Paulo (Brazil), vol. 53(3), May.
  111. Diego Vallarino, 2024. "A Comparative Machine Learning Survival Models Analysis for Predicting Time to Bank Failure in the US (2001-2023)," Journal of Economic Analysis, Anser Press, vol. 3(1), pages 129-144, March.
  112. Sun, Xiaojun & Lei, Yalin, 2021. "Research on financial early warning of mining listed companies based on BP neural network model," Resources Policy, Elsevier, vol. 73(C).
  113. Kurt M. Fanning & Kenneth O. Cogger, 1994. "A Comparative Analysis of Artificial Neural Networks Using Financial Distress Prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 3(4), pages 241-252, December.
  114. Lili Li & Jun Yang & Xin Zou, 2016. "A study of credit risk of Chinese listed companies: ZPP versus KMV," Applied Economics, Taylor & Francis Journals, vol. 48(29), pages 2697-2710, June.
  115. Ahmadian , Azam & Mahsa , Gorji, 2015. "Modeling of Banks Bankruptcy in Iran (Multivariate Statistical Analysis)," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 10(2), pages 1-24, January.
  116. Paola Cerchiello & Paolo Giudici, 2014. "Conditional graphical models for systemic risk measurement," DEM Working Papers Series 087, University of Pavia, Department of Economics and Management.
  117. Eduardo Acosta-González & Fernando Fernández-Rodríguez & Hicham Ganga, 2019. "Predicting Corporate Financial Failure Using Macroeconomic Variables and Accounting Data," Computational Economics, Springer;Society for Computational Economics, vol. 53(1), pages 227-257, January.
  118. Osama Wagdi & Yasmeen Tarek, 2022. "The Integration of Big Data and Artificial Neural Networks for Enhancing Credit Risk Scoring in Emerging Markets: Evidence from Egypt," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 14(2), pages 1-32, February.
  119. Olmedo,E. & Velasco, F. & Valderas, J.M., 2007. "Caracterización no lineal y predicción no paramétrica en el IBEX35/Nonlinear Characterization and Predictions of IBEX 35," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 25, pages 815-842, Diciembre.
  120. Shuofen Hsu & Chaohsin Lin & Yaling Yang, 2008. "Integrating Neural Networks for Risk‐Adjustment Models," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(3), pages 617-642, September.
  121. Adrian Gepp & Kuldeep Kumar & Sukanto Bhattacharya, 2010. "Business failure prediction using decision trees," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(6), pages 536-555.
  122. Hsu-Hao Tsai, 2011. "Research trends analysis by comparing data mining and customer relationship management through bibliometric methodology," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 425-450, June.
  123. Branch, Ben & Wang, Jia & Yang, Taewon, 2008. "A note on takeover success prediction," International Review of Financial Analysis, Elsevier, vol. 17(5), pages 1186-1193, December.
  124. Zhiyong Li & Chen Feng & Ying Tang, 2022. "Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis," Annals of Operations Research, Springer, vol. 315(1), pages 279-315, August.
  125. Fayçal Mraihi & Inane Kanzari, 2019. "Predicting financial distress of companies: Comparison between multivariate discriminant analysis and multilayer perceptron for Tunisian case," Working Papers 1328, Economic Research Forum, revised 21 Aug 2019.
  126. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
  127. Caulkins, Jonathan & Cohen, Jacqueline & Gorr, Wilpen & Wei, Jifa, 1996. "Predicting criminal recidivism: A comparison of neural network models with statistical methods," Journal of Criminal Justice, Elsevier, vol. 24(3), pages 227-240.
  128. Davis, Jefferson T. & Massey, Anne P. & Lovell, Ronald E. R., 1997. "Supporting a complex audit judgment task: An expert network approach," European Journal of Operational Research, Elsevier, vol. 103(2), pages 350-372, December.
  129. Taha, Mahmoud A. & Park, Sang C. & Russell, Jeffrey S., 1995. "Knowledge-based DSS for construction contractor prescreening," European Journal of Operational Research, Elsevier, vol. 84(1), pages 35-46, July.
  130. Sridhar Ramamoorti & Andrew D. Bailey Jr & Richard O. Traver, 1999. "Risk assessment in internal auditing: a neural network approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 8(3), pages 159-180, September.
  131. Kyoung-jae Kim & Kichun Lee & Hyunchul Ahn, 2018. "Predicting Corporate Financial Sustainability Using Novel Business Analytics," Sustainability, MDPI, vol. 11(1), pages 1-17, December.
  132. Denton, James W. & Hung, Ming S., 1996. "A comparison of nonlinear optimization methods for supervised learning in multilayer feedforward neural networks," European Journal of Operational Research, Elsevier, vol. 93(2), pages 358-368, September.
  133. Yijie Peng & Li Xiao & Bernd Heidergott & L. Jeff Hong & Henry Lam, 2022. "A New Likelihood Ratio Method for Training Artificial Neural Networks," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 638-655, January.
  134. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
  135. Sanjeev Mittal & Pankaj Gupta & K. Jain, 2011. "Neural network credit scoring model for micro enterprise financing in India," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 3(3), pages 224-242, October.
  136. Hashemi, R. R. & Le Blanc, L. A. & Rucks, C. T. & Rajaratnam, A., 1998. "A hybrid intelligent system for predicting bank holding structures," European Journal of Operational Research, Elsevier, vol. 109(2), pages 390-402, September.
  137. Kristóf, Tamás & Virág, Miklós, 2022. "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, vol. 61(C).
  138. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
  139. Desai, Vijay S. & Crook, Jonathan N. & Overstreet, George A., 1996. "A comparison of neural networks and linear scoring models in the credit union environment," European Journal of Operational Research, Elsevier, vol. 95(1), pages 24-37, November.
  140. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2018. "Econometrics and Machine Learning," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 505-506, pages 147-169.
  141. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
  142. J.E. Boritz & D.B. Kennedy & Augusto de Miranda e Albuquerque, 1995. "Predicting Corporate Failure Using a Neural Network Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(2), pages 95-111, June.
  143. Beynon, Malcolm J., 2005. "A novel technique of object ranking and classification under ignorance: An application to the corporate failure risk problem," European Journal of Operational Research, Elsevier, vol. 167(2), pages 493-517, December.
  144. Namsik Chang & Olivia R. Liu Sheng, 2008. "Decision-Tree-Based Knowledge Discovery: Single- vs. Multi-Decision-Tree Induction," INFORMS Journal on Computing, INFORMS, vol. 20(1), pages 46-54, February.
  145. Ekaterina Tzvetanova, 2019. "Adaptation of the Altman’s Corporate Insolvency Prediction Model – The Bulgarian Case," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 125-142.
  146. Mohammad Azmi, Nur Syafikah Atirah, 2018. "The Factors Influence Credit Risk in Japan Banking Sector Specific for Kyoto Bank," MPRA Paper 90566, University Library of Munich, Germany.
  147. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
  148. Lubecke, Thomas H. & Nam, Kyung Doo & Markland, Robert E. & Kwok, Chuck C. Y., 1998. "Combining foreign exchange rate forecasts using neural networks," Global Finance Journal, Elsevier, vol. 9(1), pages 5-27.
  149. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
  150. Kattan, Michael W. & Cooper, Randolph B., 2000. "A simulation of factors affecting machine learning techniques: an examination of partitioning and class proportions," Omega, Elsevier, vol. 28(5), pages 501-512, October.
  151. Lean Yu & Lihang Yu & Kaitao Yu, 2021. "A high-dimensionality-trait-driven learning paradigm for high dimensional credit classification," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-20, December.
  152. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2018. "Économétrie & Machine Learning," Working Papers hal-01568851, HAL.
  153. Murugan Anandarajan & Picheng Lee & Asokan Anandarajan, 2001. "Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(2), pages 69-81, June.
  154. Fayçal Mraihi, 2016. "Distressed Company Prediction Using Logistic Regression: Tunisian’s Case," Quarterly Journal of Business Studies, Research Academy of Social Sciences, vol. 2(1), pages 34-54.
  155. Akhil Kumar & Ignacio Olmeda, 1999. "A Study of Composite or Hybrid Classifiers for Knowledge Discovery," INFORMS Journal on Computing, INFORMS, vol. 11(3), pages 267-277, August.
  156. Malhotra, Rashmi & Malhotra, D. K., 2003. "Evaluating consumer loans using neural networks," Omega, Elsevier, vol. 31(2), pages 83-96, April.
  157. Canbas, Serpil & Cabuk, Altan & Kilic, Suleyman Bilgin, 2005. "Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case," European Journal of Operational Research, Elsevier, vol. 166(2), pages 528-546, October.
  158. Carlos Serrano-Cinca, 1997. "Feedforward neural networks in the classification of financial information," The European Journal of Finance, Taylor & Francis Journals, vol. 3(3), pages 183-202.
  159. Malhotra, Rashmi & Malhotra, D. K., 2002. "Differentiating between good credits and bad credits using neuro-fuzzy systems," European Journal of Operational Research, Elsevier, vol. 136(1), pages 190-211, January.
  160. Teija Laitinen & Maria Kankaanpaa, 1999. "Comparative analysis of failure prediction methods: the Finnish case," European Accounting Review, Taylor & Francis Journals, vol. 8(1), pages 67-92.
  161. Gruca, TS & Klemz, BR, 1998. "Using Neural Networks to Identify Competitive Market Structures from Aggregate Market Response Data," Omega, Elsevier, vol. 26(1), pages 49-62, February.
  162. P Pendharkar, 2009. "Misclassification cost minimizing fitness functions for genetic algorithm-based artificial neural network classifiers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1123-1134, August.
  163. Korangi, Kamesh & Mues, Christophe & Bravo, Cristián, 2023. "A transformer-based model for default prediction in mid-cap corporate markets," European Journal of Operational Research, Elsevier, vol. 308(1), pages 306-320.
  164. Wolfgang Karl Härdle & Dedy Dwi Prastyo, 2013. "Default Risk Calculation based on Predictor Selection for the Southeast Asian Industry," SFB 649 Discussion Papers SFB649DP2013-037, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  165. Hu, Yu-Chiang & Ansell, Jake, 2007. "Measuring retail company performance using credit scoring techniques," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1595-1606, December.
  166. Sanjeev Prashar & Harvinder Singh & Chandan Parsad & T. Sai Vijay, 2017. "Predicting Indian Shoppers’ Malls Loyalty Behaviour," Vikalpa: The Journal for Decision Makers, , vol. 42(4), pages 234-250, December.
  167. repec:erf:erfstu:78 is not listed on IDEAS
  168. Hu, Michael Y. & Zhang, G. Peter & Chen, Haiyang, 2004. "Modeling foreign equity control in Sino-foreign joint ventures with neural networks," European Journal of Operational Research, Elsevier, vol. 159(3), pages 729-740, December.
  169. Catalin-Emanuel CIOBOTA & Manuela-Violeta TUREATCA, 2022. "Prediction of Business Bankruptcy with the Help of Extreme Gradient Increase," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 3, pages 178-185.
  170. Piramuthu, Selwyn, 1999. "Financial credit-risk evaluation with neural and neurofuzzy systems," European Journal of Operational Research, Elsevier, vol. 112(2), pages 310-321, January.
  171. Juliana Yim & Heather Mitchell, 2007. "Predicting Financial Distress In The Australian Financial Service Industry," Australian Economic Papers, Wiley Blackwell, vol. 46(4), pages 375-388, December.
  172. Amélia Ferreira da Silva & José Henrique Brito & Mariline Lourenço & José Manuel Pereira, 2023. "Sustainability of Transport Sector Companies: Bankruptcy Prediction Based on Artificial Intelligence," Sustainability, MDPI, vol. 15(23), pages 1-13, December.
  173. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
  174. Mark T. Leung & An-Sing Chen, 2005. "Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 403-420.
  175. Ortíz Arango Francisco & Cabrera Llanos Agustín Ignacio & López Herrera Francisco, 2013. "Pronóstico de los índices accionarios DAX y S&P 500 con redes neuronales diferenciales," Contaduría y Administración, Accounting and Management, vol. 58(3), pages 203-225, julio-sep.
  176. Piramuthu, Selwyn, 2004. "Evaluating feature selection methods for learning in data mining applications," European Journal of Operational Research, Elsevier, vol. 156(2), pages 483-494, July.
  177. Philippe Paquet, 1997. "L'utilisation des réseaux de neurones artificiels en finance," Working Papers 1997-1, Laboratoire Orléanais de Gestion - université d'Orléans.
  178. Fildes, Robert, 2006. "The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion," International Journal of Forecasting, Elsevier, vol. 22(3), pages 415-432.
  179. Lean Yu & Xinxie Li & Ling Tang & Zongyi Zhang & Gang Kou, 2015. "Social credit: a comprehensive literature review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-18, December.
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