A Multi-Layer Perceptron Approach to Financial Distress Prediction with Genetic Algorithm

[1] Wanke P.(2014)Financial distress drivers in Brazilian banks: A dynamic slacks approach Eur. J. Oper. Res. 240 258-268

[2] Barros C.P.(2015)Predicting financial distress in the Hong Kong growth enterprises market from the perspective of financial sustainability Sustainability 7 1186-1200

[3] Faria J.R.(2018)Financial risk measurement and prediction modelling for sustainable development of business entities using regression analysis Sustainability 10 2144-404

[4] Hu Hui(2015)Predicting financial distress: A comparison of survival analysis and decision tree techniques Procedia Comput. Sci. 54 396-614

[5] Sathye Milind(2005)Min and Young-Chan Lee, Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters Expert Syst. Appl. 28 603-609

[6] Valaskova K.(1968)Financial ratios, discriminant analysis and the prediction of corporate bankruptcy J. Finance 23 589-131

[7] Kliestik T.(1980)Financial ratios and the probabilistic prediction of bankruptcy J. Account. Res. 18 109-289

[8] Svabova T.(1991)Credit-scoring by enlarged discriminant models Omega 19 275-14

[9] Adamko P.(2019)Traditional financial distress prediction models predict the early warning signs of financial distress? J. Risk Financ. Manage. 12 55-152

[10] Gepp A.(2017)Hamido Fujita, Peng Chen, and Hui Li, Dynamic financial distress prediction with concept drift based on time weighting combined with adaboost support vector machine ensemble Knowl.-Based Syst. 120 4-831

[1] Wanke P.(2014)Financial distress drivers in Brazilian banks: A dynamic slacks approach Eur. J. Oper. Res. 240 258-268

[2] Barros C.P.(2015)Predicting financial distress in the Hong Kong growth enterprises market from the perspective of financial sustainability Sustainability 7 1186-1200

[3] Faria J.R.(2018)Financial risk measurement and prediction modelling for sustainable development of business entities using regression analysis Sustainability 10 2144-404

[4] Hu Hui(2015)Predicting financial distress: A comparison of survival analysis and decision tree techniques Procedia Comput. Sci. 54 396-614

[5] Sathye Milind(2005)Min and Young-Chan Lee, Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters Expert Syst. Appl. 28 603-609

[6] Valaskova K.(1968)Financial ratios, discriminant analysis and the prediction of corporate bankruptcy J. Finance 23 589-131

[7] Kliestik T.(1980)Financial ratios and the probabilistic prediction of bankruptcy J. Account. Res. 18 109-289

[8] Svabova T.(1991)Credit-scoring by enlarged discriminant models Omega 19 275-14

[9] Adamko P.(2019)Traditional financial distress prediction models predict the early warning signs of financial distress? J. Risk Financ. Manage. 12 55-152

[10] Gepp A.(2017)Hamido Fujita, Peng Chen, and Hui Li, Dynamic financial distress prediction with concept drift based on time weighting combined with adaboost support vector machine ensemble Knowl.-Based Syst. 120 4-831