* Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Oyedele, A. A., Akinade, O. O., Bilal, M., & Ajayi, S. O. (2017). Critical factors for insolvency prediction: Towards a theoretical model for the construction industry. International Journal of Construction Management, 17(1), 25-49. https://doi.org/10.1080/15623599.2016.1166546
Article Google Scholar
* Almaskati, N., Bird, R., Yeung, D., & Lu, Y. (2021). A horse race of models and estimation methods for predicting bankruptcy. Advances in Accounting, 52, Article 100513. https://doi.org/10.1016/j.adiac.2021.100513
Article Google Scholar
* Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Article Google Scholar
* Altman, E. I., & Hotchkiss, E. (2010). Corporate financial distress and bankruptcy: Predict and avoid bankruptcy, analyze and invest in distressed debt. John Wiley & Sons.
Google Scholar
* Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2020). A race for long horizon bankruptcy prediction. Applied Economics, 52(37), 4092-4111. https://doi.org/10.1080/00036846.2020.1730762
Article Google Scholar
* Armstrong, C., Foster, G., & Taylor, D. (2016). Abnormal accruals in newly public companies: Opportunistic misreporting or economic activity? Management Science, 62(5), 1316-1338. https://doi.org/10.1287/mnsc.2015.2179
Article Google Scholar
* Balla, E., Mazur, L., & Prescott, E. (2019). A comparison of community bank failures and FDIC losses in the 1986-92 and 2007-13 banking crises. Journal of Banking & Finance, 106, 1-15. https://doi.org/10.1016/j.jbankfin.2019.04.005
Article Google Scholar
* Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405-417. https://doi.org/10.1016/j.eswa.2017.04.006
Article Google Scholar
* Beaver, W. H., Correia, M., & McNichols, M. F. (2012). Do differences in financial reporting attributes impair the predictive ability of financial ratios for bankruptcy? Review of Accounting Studies, 17(4), 969-1010. https://doi.org/10.1007/s11142-012-9186-7
Article Google Scholar
* Behr, A., & Weinblat, J. (2017). Default patterns in seven EU countries: A random forest approach. International. Journal of the Economics of Business, 24(2), 181-222. https://doi.org/10.1080/13571516.2016.1252532
Article Google Scholar
* Ben Jabeur, S., & Fahmi, Y. (2018). Forecasting financial distress for French firms: A comparative study. Empirical. Economics, 54(3), 1173-1186. https://doi.org/10.1007/s00181-017-1246-1
Article Google Scholar
* Ben Jabeur, S., & Serret, V. (2023). Bankruptcy prediction using fuzzy convolutional neural networks. Research in International Business and Finance, 63, Article 101844. https://doi.org/10.1016/j.ribaf.2022.101844
Article Google Scholar
* Ben Jabeur, S., Stef, N., & Carmona, P. (2023). Bankruptcy prediction using the XGBoost algorithm and variable importance feature engineering. Computational Economics, 61(2), 715-741. https://doi.org/10.1007/s10614-021-10227-1
Article Google Scholar
* Bode, C., Wagner, S. M., Petersen, K. J., & Ellram, L. M. (2011). Understanding responses to supply chain disruptions: Insights from information processing and resource dependence perspectives. Academy of Management Journal, 54(4), 833-856. https://doi.org/10.5465/amj.2011.64870145
Article Google Scholar
* Charalambous, C., Martzoukos, S. H., & Taoushianis, Z. (2022). Estimating corporate bankruptcy forecasting models by maximizing discriminatory power. Review of Quantitative Finance and Accounting, 58(1), 297-328. https://doi.org/10.1007/s11156-021-00995-0
Article Google Scholar
* Chava, S., & Jarrow, R. A. (2004). Bankruptcy prediction with industry effects. Review of Finance, 8(4), 537-569. https://doi.org/10.1007/s10679-004-6279-6
Article Google Scholar
* Chen, J., & Sun, B. (2024). Enhancing financial risk prediction using TG-LSTM model: An innovative approach with applications to public health emergencies. Journal of the Knowledge Economy, 16(1), 2979-2999. https://doi.org/10.1007/s13132-024-02081-x
Article Google Scholar
* Chen, S., Yuan, Y., Luo, X., Jian, J., & Wang, Y. (2021). Discovering group-based transnational cyber fraud actives: A polymethodological view. Computers & Security, 104, Article 102217. https://doi.org/10.1016/j.cose.2021.102217
Article Google Scholar
* Chen, T.-K., Liao, H.-H., Chen, G.-D., Kang, W.-H., & Lin, Y.-C. (2023). Bankruptcy prediction using machine learning models with the text-based communicative value of annual reports. Expert Systems with Applications, 233, 120714. https://doi.org/10.1016/j.eswa.2023.120714
Article Google Scholar
* Craighead, C. W., Ketchen, D. J., & Darby, J. L. (2020). Pandemics and supply chain management research: Toward a theoretical toolbox. Decision Sciences, 51(4), 838-866. https://doi.org/10.1111/deci.12468
Article Google Scholar
* Cruz-García, P., & de Fernánz Guevara, J. (2020). Determinants of net interest margin: The effect of capital requirements and deposit insurance scheme. European Journal of Finance, 26(11), 1102-1123. https://doi.org/10.1080/1351847x.2019.1700149
Article Google Scholar
* Davydenko, S. A., & Franks, J. R. (2008). Do bankruptcy codes matter? A study of defaults in France, Germany, and the UK. Journal of Finance, 63(2), 565-608. https://doi.org/10.1111/j.1540-6261.2008.01325.x
Article Google Scholar
* DeYoung, R., & Torna, G. (2013). Nontraditional banking activities and bank failures during the financial crisis. Journal of Financial Intermediation, 22(3), 397-421. https://doi.org/10.1016/j.jfi.2013.01.001
Article Google Scholar
* Dikshit, A., & Pradhan, B. (2021). Interpretable and explainable AI (XAI) model for spatial drought prediction. Science of the Total Environment, 801, Article 149797. https://doi.org/10.1016/j.scitotenv.2021.149797
Article Google Scholar
* du Jardin, P. (2017). Dynamics of firm financial evolution and bankruptcy prediction. Expert Systems with Applications, 75, 25-43. https://doi.org/10.1016/j.eswa.2017.01.016
Article Google Scholar
* du Jardin, P. (2018). Failure pattern-based ensembles applied to bankruptcy forecasting. Decision Support Systems, 107, 64-77. https://doi.org/10.1016/j.dss.2018.01.003
Article Google Scholar
* du Jardin, P., & Séverin, E. (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(3), 701-711. https://doi.org/10.1016/j.dss.2011.04.001
Article Google Scholar
* du Jardin, P., & Séverin, E. (2012). Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time. European Journal of Operational Research, 221(3), 644-659. https://doi.org/10.1016/j.ejor.2012.04.006
Article Google Scholar
* Fan, S., & Liu, G. (2017). Anomaly detection methods for bankruptcy prediction. In 2017 International Conference on Systems and Informatics (ICSAI) (pp. 1558-1563). https://doi.org/10.1109/ICSAI.2017.8248515
* Fan, Y. (2014). Essays on industrial organization, Doctoral dissertation. London School of Economics and Political Science.
Google Scholar
* Fischer, E. O., Heinkel, R., & Zechner, J. (1989). Dynamic capital structure choice: Theory and tests. The Journal of Finance, 44(1), 19-40. https://doi.org/10.1111/j.1540-6261.1989.tb02402.x
Article Google Scholar
* Gao, Y., Zhao, C., Sun, B., & Zhao, W. (2022). Effects of investor sentiment on stock volatility: New evidences from multi-source data in China’s green stock markets. Financial Innovation, 8(1), 77. https://doi.org/10.1186/s40854-022-00381-2
* Gritta, R. D., Adams, B., & Adrangi, B. (2006). An analysis of the effects of operating and financial leverage on the major U.S. air carriers’ rates of return: 1990-2003. Journal of the Transportation Research Forum, 45(1).
* Gupta, V. (2011). Recent trends in text classification techniques. International Journal of Computer Applications, 35(6), 6-11.
Article Google Scholar
* International Monetary Fund. (2020). World economic outlook, April 2020: The great lockdown.
Book Google Scholar
* Johnson, M. S., Levine, D. I., & Toffel, M. W. (2023). Improving regulatory effectiveness through better targeting: Evidence from OSHA. American Economic Journal: Applied Economics, 15(4), 30-67. https://doi.org/10.1257/app.20200659
Article Google Scholar
* Jones, S., Johnstone, D., & Wilson, R. (2017). Predicting corporate bankruptcy: An evaluation of alternative statistical frameworks. Journal of Business Finance & Accounting, 44(1-2), 3-34. https://doi.org/10.1111/jbfa.12218
Article Google Scholar
* Joseph, A. (2019). Shapley regressions: A framework for statistical inference on machine learning models (Staff Working Paper No. 784). Bank of England https://www.bankofengland.co.uk/working-paper/2019/shapley-regressions-a-framework-for-statistical-inference-on-machine-learning-models
Google Scholar
* Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146-3154.
Google Scholar
* Kraus, A., & Litzenberger, R. H. (1973). A state-preference model of optimal financial leverage. The Journal of Finance, 28(4), 911-922.
Google Scholar
* Li, Z., Tian, Y., Li, K., Zhou, F., & Yang, W. (2017). Reject inference in credit scoring using semi-supervised support vector machines. Expert Systems with Applications, 74, 105-114. https://doi.org/10.1016/j.eswa.2017.01.011
Article Google Scholar
* Liu, J., & Dong, J. (2020). A multi-agent simulation of investment choice in the P2P lending market with bankruptcy risk. Journal of Simulation, 14(2), 87-101. https://doi.org/10.1080/17477778.2020.1759386
Article Google Scholar
* Liu, X. (2021). Empirical analysis of financial statement fraud of listed companies based on logistic regression and random forest algorithm. Journal of Mathematics. https://doi.org/10.1155/2021/9241338
Article Google Scholar
* López de Prado, M., & Lewis, M. J. (2019). Detection of false investment strategies using unsupervised learning methods. Quantitative Finance, 19(9), 1555-1565. https://doi.org/10.1080/14697688.2019.1622311
Article Google Scholar
* Lundberg, S. M., & Lee, S.-I. (2017a). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765-4774.
Google Scholar
* Lundberg, S. M., & Lee, S.-I. (2017b). Consistent feature attribution for tree ensembles. arXiv https://arxiv.org/abs/1706.06060
* Mai, F., Tian, S., Lee, C., & Ma, L. (2019). Deep learning models for bankruptcy prediction using textual disclosures. European Journal of Operational Research, 274(2), 743-758. https://doi.org/10.1016/j.ejor.2018.10.024
Article Google Scholar
* Miller, E. M. (1977). Risk, uncertainty, and divergence of opinion. The Journal of Finance, 32(4), 1151-1168. https://doi.org/10.1111/j.1540-6261.1977.tb03317.x
Article Google Scholar
* Modigliani, F., & Miller, M. H. (1958). The cost of capital, corporation finance and the theory of investment. The American Economic Review, 48(3), 261-297.
Google Scholar
* Molnar, C. (2020). Interpretable machine learning: A guide for making black box models explainable. Lulu.com.
Google Scholar
* Moscatelli, M., Narizzano, S., Parlapiano, F., & Viggiano, G. (2020). Corporate default forecasting with machine learning. Expert Systems with Applications, 161, Article 113567. https://doi.org/10.1016/j.eswa.2020.113567
Article Google Scholar
* Myers, S. C. (1984). The capital structure puzzle. The Journal of Finance, 39(3), 575-592. https://doi.org/10.1111/j.1540-6261.1984.tb03646.x
Article Google Scholar
* Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. https://doi.org/10.2307/2490395
Article Google Scholar
* Perboli, G., & Arabnezhad, E. (2021). A machine learning-based DSS for mid and long-term company crisis prediction. Expert Systems with Applications, 174, Article 114758. https://doi.org/10.1016/j.eswa.2021.114758
Article Google Scholar
* Powell, R. J., Dinh, D. V., Vu, N. T., & Vo, D. H. (2023). Accounting-based variables as an early warning indicator of financial distress in crisis and non-crisis periods. International Journal of Finance & Economics. https://doi.org/10.1002/ijfe.2864
Article Google Scholar
* Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 31, 6638-6648.
Google Scholar
* Shapley, L. S. (1953). A value for n-person games. Contributions to the Theory of Games, 2, 307-317.
Google Scholar
* Sigrist, F., & Hirnschall, C. (2019). Grabit: Gradient tree-boosted Tobit models for default prediction. Journal of Banking & Finance, 102, 177-192. https://doi.org/10.1016/j.jbankfin.2019.03.004
Article Google Scholar
* Sigrist, F., & Leuenberger, N. (2023). Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities. European Journal of Operational Research, 305(3), 1390-1406. https://doi.org/10.1016/j.ejor.2022.06.035
Article Google Scholar
* Smith, M., & Alvarez, F. (2022). Predicting firm-level bankruptcy in the Spanish economy using extreme gradient boosting. Computational Economics, 59(1), 263-295. https://doi.org/10.1007/s10614-020-10078-2
Article Google Scholar
* U.S. Census Bureau. (2019). U.S. Census Bureau’s budget fiscal year 2020. https://www2.census.gov/about/budget/FY-2020-Congressional-Budget-Submission.pdf
* U.S. Securities and Exchange Commission. (2023). SEC adopts rules on cybersecurity risk management, strategy, governance, and incident disclosure by public companies [Press release]. https://www.sec.gov/newsroom/press-releases/2023-139
* Veganzones, D., & Séverin, E. (2018). An investigation of bankruptcy prediction in imbalanced datasets. Decision Support Systems, 112, 111-124. https://doi.org/10.1016/j.dss.2018.06.011
Article Google Scholar
* Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924. https://doi.org/10.1108/bpmj-10-2019-0411
Article Google Scholar
* Wang, J., Yang, J., Iverson, B. C., & Kluender, R. (2020). Bankruptcy and the COVID-19 crisis. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3690398
* Wang, S., Hu, H., & Zhang, Z. (2023). Market development strategy and process performance of knowledge-intensive SMEs in the epidemic era: A process-oriented perspective. Business Process Management Journal, 29(4), 1010-1030. https://doi.org/10.1108/bpmj-07-2022-0323
Article Google Scholar
* Yan, X., & Zhao, J. (2019). Application of improved convolution neural network in financial forecasting. In 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (pp. 324-328). https://doi.org/10.1109/icccbda.2019.8725661
Chapter Google Scholar
* Yildirim, E., Koç, T., & Güzel, Y. (2024). Excessive pricing at the Covid-19 pandemic: Big data insights from consumer complaints. SAGE Open. https://doi.org/10.1177/21582440241228699
Article Google Scholar
* Zhang, K., Xu, P., & Zhang, J. (2020). Explainable AI in deep reinforcement learning models: A SHAP method applied in power system emergency control. In 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2) (pp. 771-775). https://doi.org/10.1109/EI250167.2020.9347147
Chapter Google Scholar
* Zhang, T., Zhu, W., Wu, Y., Wu, Z., Zhang, C., & Hu, X. (2023). An explainable financial risk early warning model based on the DS-XGBoost model. Finance Research Letters, 56, Article 104045. https://doi.org/10.1016/j.frl.2023.104045
Article Google Scholar
* Zięba, M., Tomczak, S. K., & Tomczak, J. M. (2016). Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Expert Systems with Applications, 58, 93-101. https://doi.org/10.1016/j.eswa.2016.04.001
Article Google Scholar
