doi:10.1017/S026988892100014X

Hafiz A Alaka , Lukumon O Oyedele , Hakeem A Owolabi , Vikas Kumar, Saheed O Ajayi , Olugbenga O Akinade , and Muhammad Bilal. Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 94: 164–184, 2018.

Edward I Altman . Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23 (4): 589–609, 1968.

Flavio Barboza, Herbert Kimura, and Edward Altman. Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83: 405–417, 2017.

William H Beaver . Financial ratios as predictors of failure. Journal of accounting research, pages 71–111, 1966.

William H Beaver , Maureen F McNichols , and Jung-Wu Rhie. Have financial statements become less informative? evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting studies, 10 (1): 93–122, 2005.

Jodi L Bellovary , Don E Giacomino , and Michael D Akers . A review of bankruptcy prediction studies: 1930 to present. Journal of Financial education, pages 1–42, 2007.

Girish Chandrashekar and Ferat Sahin. A survey on feature selection methods. Computers & Electrical Engineering, 40 (1): 16–28, 2014.

Davide Chicco and Giuseppe Jurman. The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC genomics, 21 (1): 1–13, 2020.

Jesse Davis and Mark Goadrich. The relationship between precision-recall and roc curves. In Proceedings of the 23rd international conference on Machine learning, pages 233–240, 2006.

Abe De Jong, Rezaul Kabir, and Thuy Thu Nguyen. Capital structure around the world: The roles of firm-and country-specific determinants. Journal of Banking & Finance, 32 (9): 1954–1969, 2008.

S Sarojini Devi and Y Radhika . A survey on machine learning and statistical techniques in bankruptcy prediction. International Journal of Machine Learning and Computing, 8 (2): 133–139, 2018.

Emil Eirola, Andrey Gritsenko, Anton Akusok, Kaj-Mikael BjÖrk, Yoan Miche, DuŠan Sovilj, Rui Nian, Bo He, and Amaury Lendasse. Extreme learning machines for multiclass classification: refining predictions with gaussian mixture models. In International Work-Conference on Artificial Neural Networks, pages 153–164. Springer, 2015.

Daryush Foroghi, Amirhassan Monadjemi, et al. Applying decision tree to predict bankruptcy. In 2011 IEEE International Conference on Computer Science and Automation Engineering, volume 4, pages 165–169. IEEE, 2011.

Haibo He and Edwardo A Garcia. Learning from imbalanced data. IEEE Transactions on knowledge and data engineering, 21 (9): 1263–1284, 2009.

Stephen A Hillegeist , Elizabeth K Keating , Donald P Cram, and Kyle G Lundstedt. Assessing the probability of bankruptcy. Review of accounting studies, 9 (1): 5–34, 2004.

Tadaaki Hosaka. Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert systems with applications, 117: 287–299, 2019.

Chih-Wei Hsu, Chih-Chung Chang, Chih-Jen Lin, et al. A practical guide to support vector classification, 2003.

Win-Bin Huang, Junting Liu, Haodong Bai, and Pengyi Zhang. Value assessment of companies by using an enterprise value assessment system based on their public transfer specification. Information Processing & Management, 57 (5): 102254, 2020.

Sadegh Bafandeh Imandoust and Mohammad Bolandraftar. Application of k-nearest neighbor (knn) approach for predicting economic events: Theoretical background. International Journal of Engineering Research and Applications, 3 (5): 605–610, 2013.

Utkarsh Mahadeo Khaire and R Dhanalakshmi . Stability of feature selection algorithm: A review. Journal of King Saud University-Computer and Information Sciences, 2019.

Hyeongjun Kim, Hoon Cho, and Doojin Ryu. Corporate default predictions using machine learning: Literature review. Sustainability, 12 (16): 6325, 2020.

Emrehan Kutlug Sahin, Cengizhan Ipbuker, and Taskin Kavzoglu. Investigation of automatic feature weighting methods (fisher, chi-square and relief-f) for landslide susceptibility mapping. Geocarto international, 32 (9): 956–977, 2017.

Larry Li and Silvia Z Islam. Firm and industry specific determinants of capital structure: Evidence from the australian market. International Review of Economics & Finance, 59: 425–437, 2019.

Piero Montebruno, Robert J Bennett , Harry Smith, and Carry Van Lieshout. Machine learning classification of entrepreneurs in british historical census data. Information Processing & Management, 57 (3): 102210, 2020.

OECD. Country statistical profile: Spain 2020. OECD ilibrary, 2018. URL https://www.oecd-ilibrary.org/.

James A Ohlson . Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, pages 109–131, 1980.

David L Olson , Dursun Delen, and Yanyan Meng. Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Systems, 52 (2): 464–473, 2012.

J-P Onnela , Anirban Chakraborti, Kimmo Kaski, and Janos Kertesz. Dynamic asset trees and black monday. Physica A: Statistical Mechanics and its Applications, 324 (1-2): 247–252, 2003.

Yi Qu, Pei Quan, Minglong Lei, and Yong Shi. Review of bankruptcy prediction using machine learning and deep learning techniques. Procedia Computer Science, 162: 895–899, 2019.

Mandeep Kaur Saggi and Sushma Jain. A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management, 54 (5): 758–790, 2018.

Takaya Saito and Marc Rehmsmeier. The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets. PloS one, 10 (3), 2015.

M Sharma and Monali Mavani. Development of predictive model in education system: using nave bayes classifier. In Proceedings of the International Conference & Workshop on Emerging Trends in Technology, pages 185–186, 2011.

Tyler Shumway. Forecasting bankruptcy more accurately: A simple hazard model. The journal of business, 74 (1): 101–124, 2001.

Saúl Solorio -Fernández, J Ariel Carrasco -Ochoa, and José Fco Martnez -Trinidad. A review of unsupervised feature selection methods. Artificial Intelligence Review, 53 (2): 907–948, 2020.

David Veganzones and Eric Séverin. An investigation of bankruptcy prediction in imbalanced datasets. Decision Support Systems, 112: 111–124, 2018.

Robert Wade and Frank Veneroso. The asian crisis: the high debt model versus the wall street-treasury-imf complex. New left review, pages 3–24, 1998.

Nanxi Wang et al. Bankruptcy prediction using machine learning. Journal of Mathematical Finance, 7 (04): 908, 2017.

Guoqiu Wen, Xianxian Li, Yonghua Zhu, Linjun Chen, Qimin Luo, and Malong Tan. One-step spectral rotation clustering for imbalanced high-dimensional data. Information Processing & Management, 58 (1): 102388, 2021.

Feng Yang and KZ Mao. Robust feature selection for microarray data based on multicriterion fusion. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8 (4): 1080–1092, 2010.

Wenhao Zhang et al. Machine learning approaches to predicting company bankruptcy. Journal of Financial Risk Management, 6 (04): 364, 2017.

Maciej Zieba, Sebastian K Tomczak, and Jakub M Tomczak. Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Expert systems with applications, 58: 93–101, 2016.