Al‐Hadi, A., Chatterjee, B., Yaftian, A., Taylor, G., & Monzur Hasan, M. (2019). Corporate social responsibility performance, financial distress and firm life cycle: Evidence from Australia. Accounting and Finance, 59, 961–989. https://doi.org/10.1111/acfi.12277.
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23, 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x.
Apergis, N., Bhattacharya, M., & Inekwe, J. (2019). Prediction of financial distress for multinational corporations: Panel estimations across countries. Applied Economics, 51, 4255–4269. https://doi.org/10.1080/00036846.2019.1589646.
Boubaker, S., Cellier, A., Manita, R., & Saeed, A. (2020). Does corporate social responsibility reduce financial distress risk? Economic Modelling, 91, 835–851. https://doi.org/10.1016/j.econmod.2020.05.012.
Chen, C. C., Chen, C. D., & Lien, D. (2020). Financial distress prediction model: The effects of corporate governance indicators. Journal of Forecasting, 39, 1238–1252. https://doi.org/10.1002/for.2684.
- Chen, M.‐Y. (2011). Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, 38, 11261–11272. https://doi.org/10.1016/j.eswa.2011.02.173.
Paper not yet in RePEc: Add citation now
- Choi, H., Son, H., & Kim, C. (2018). Predicting financial distress of contractors in the construction industry using ensemble learning. Expert Systems with Applications, 110, 1–10. https://doi.org/10.1016/j.eswa.2018.05.026.
Paper not yet in RePEc: Add citation now
Choi, J. H., Kim, S., Yang, D.‐H., & Cho, K. (2021). Can corporate social responsibility decrease the negative influence of financial distress on accounting quality? Sustainability, 13, 11124. https://doi.org/10.3390/su131911124.
- Cleofas‐Sánchez, L., García, V., Marqués, A. I., & Sánchez, J. S. (2016). Financial distress prediction using the hybrid associative memory with translation. Applied Soft Computing, 44, 144–152. https://doi.org/10.1016/j.asoc.2016.04.005.
Paper not yet in RePEc: Add citation now
Crone, S. F., & Finlay, S. (2012). Instance sampling in credit scoring: An empirical study of sample size and balancing. International Journal of Forecasting, 28, 224–238. https://doi.org/10.1016/j.ijforecast.2011.07.006.
Dahlsrud, A. (2008). How corporate social responsibility is defined: An analysis of 37 definitions. Corporate Social Responsibility and Environmental Management, 15(1), 1–13. https://doi.org/10.1002/csr.132.
- Ding, Y., Song, X., & Zen, Y. (2008). Forecasting financial condition of Chinese listed companies based on support vector machine. Expert Systems with Applications, 34, 3081–3089. https://doi.org/10.1016/j.eswa.2007.06.037.
Paper not yet in RePEc: Add citation now
Dumitrescu, A., el Hefnawy, M., & Zakriya, M. (2020). Golden geese or black sheep: Are stakeholders the saviors or saboteurs of financial distress? Finance Research Letters, 37, 101371. https://doi.org/10.1016/j.frl.2019.101371.
Gupta, K., & Krishnamurti, C. (2018). Does corporate social responsibility engagement benefit distressed firms? The role of moral and exchange capital. Pacific‐Basin Finance Journal, 50, 249–262. https://doi.org/10.1016/j.pacfin.2016.10.010.
- Haibo, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21, 1263–1284. https://doi.org/10.1109/TKDE.2008.239.
Paper not yet in RePEc: Add citation now
He, Y., Xu, L., & Yang, M. (2021). The impact of tunnelling on financial distress and resolution: Evidence from listed firms in China. International Journal of Finance and Economics, 26, 1773–1792. https://doi.org/10.1002/ijfe.1877.
- Hsieh, M.‐Y., Yan, T.‐M., Huang, C.‐C., & Jane, C.‐J. (2014). Explore the most potential supplier's selection determinants in modern supply chain management. Mathematical Problems in Engineering, 2014, 1–8. https://doi.org/10.1155/2014/390878.
Paper not yet in RePEc: Add citation now
- Huan, L., & Lei, Y. (2005). Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17, 491–502. https://doi.org/10.1109/TKDE.2005.66.
Paper not yet in RePEc: Add citation now
Inekwe, J. N. (2016). Financial distress, Employees' welfare and entrepreneurship among SMEs. Social Indicators Research, 129, 1135–1153. https://doi.org/10.1007/s11205-015-1164-6.
Jiang, Y., & Jones, S. (2018). Corporate distress prediction in China: A machine learning approach. Accounting and Finance, 58(4), 1063–1109. https://doi.org/10.1111/acfi.12432.
Kim, S. Y. (2018). Predicting hospitality financial distress with ensemble models: The case of US hotels, restaurants, and amusement and recreation. Service Business, 12, 483–503. https://doi.org/10.1007/s11628-018-0365-x.
Kim, S. Y., & Upneja, A. (2014). Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models. Economic Modelling, 36, 354–362. https://doi.org/10.1016/j.econmod.2013.10.005.
Kim, Y., Li, H., & Li, S. (2014). Corporate social responsibility and stock price crash risk. Banking and Finance, 43, 1–13. https://doi.org/10.1016/j.jbankfin.2014.02.013.
Li, L., & Faff, R. (2019). Predicting corporate bankruptcy: What matters? International Review of Economics and Finance, 62, 1–19. https://doi.org/10.1016/j.iref.2019.02.016.
- Liang, D., Tsai, C.‐F., & Wu, H.‐T. (2015). The effect of feature selection on financial distress prediction. Knowledge‐Based Systems, 73, 289–297. https://doi.org/10.1016/j.knosys.2014.10.010.
Paper not yet in RePEc: Add citation now
- Liang, D., Tsai, C.‐F., Dai, A.‐J., & Eberle, W. (2018). A novel classifier ensemble approach for financial distress prediction. Knowledge and Information Systems, 54, 437–462. https://doi.org/10.1007/s10115-017-1061-1.
Paper not yet in RePEc: Add citation now
- Liu, J. M., Li, C. Z., Peng, O. Y., Liu, J. J., & Wu, C. (2022). Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach. Journal of Forecasting, 2022, 1–26.
Paper not yet in RePEc: Add citation now
Liu, J., & Wu, C. (2017). Dynamic forecasting of financial distress: The hybrid use of incremental bagging and genetic algorithm—Empirical study of Chinese listed corporations. Risk Management, 19, 32–52. https://doi.org/10.1057/s41283-016-0012-6.
Liu, J., Wu, C., & Li, Y. (2019). Improving financial distress prediction using financial network‐based information and GA‐based gradient boosting method. Computational Economics, 53, 851–872. https://doi.org/10.1007/s10614-017-9768-3.
- Malakauskas, A., & Lakštutienė, A. (2021). Financial distress prediction for small and medium enterprises using machine learning techniques. Engineering Economics, 32, 4–14. https://doi.org/10.5755/j01.ee.32.1.27382.
Paper not yet in RePEc: Add citation now
Manzaneque, M., Priego, A. M., & Merino, E. (2016). Corporategovernance effect on financial distress likelihood: Evidence from Spain. Revista de Contabilidad, 19(1), 111–121. https://doi.org/10.1016/j.rcsar.2015.04.001.
- Matin, R., Hansen, C., Hansen, C., & Mølgaard, P. (2019). Predicting distresses using deep learning of text segments in annual reports. Expert Systems with Applications, 132, 199–208. https://doi.org/10.1016/j.eswa.2019.04.071.
Paper not yet in RePEc: Add citation now
- Merwin, C. L. (1944). Financing small corporations in five manufacturing industries, 1926–36. Journal of the American Statistical Association, 39, 129–130.
Paper not yet in RePEc: Add citation now
Mselmi, N., Lahiani, A., & Hamza, T. (2017). Financial distress prediction: The case of French small and medium‐sized firms. International Review of Financial Analysis, 50, 67–80. https://doi.org/10.1016/j.irfa.2017.02.004.
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal on Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395.
Pavlicko, M., Durica, M., & Mazanec, J. (2021). Ensemble model of the financial distress prediction in Visegrad group countries. Mathematics, 9, 1886. https://doi.org/10.3390/math9161886.
- Santoso, N., & Wibowo, W. (2018). Financial distress prediction using linear discriminant analysis and support vector machine. Journal of Physics: Conference Series, 979, 012089.
Paper not yet in RePEc: Add citation now
Schwaab, B., Koopman, S. J., & Lucas, A. (2014). Nowcasting and forecasting global financial sector stress and credit market dislocation. International Journal of Forecasting, 30, 741–758. https://doi.org/10.1016/j.ijforecast.2013.10.004.
- Shen, F., Liu, Y., Wang, R., & Zhou, W. (2020). A dynamic financial distress forecast model with multiple forecast results under unbalanced data environment. Knowledge‐Based Systems, 192, 105365. https://doi.org/10.1016/j.knosys.2019.105365.
Paper not yet in RePEc: Add citation now
- Shi, X. (2006). Optimal sample pairing and critical value of logistic default risk modeling: The China case. Application of Statistics and Management, 6, 675–682.
Paper not yet in RePEc: Add citation now
- Song, Y., & Peng, Y. (2019). A MCDM‐based evaluation approach for imbalanced classification methods in financial risk prediction. IEEE Access, 7, 84897–84906. https://doi.org/10.1109/ACCESS.2019.2924923.
Paper not yet in RePEc: Add citation now
- Sue, K.‐L., Tsai, C.‐F., & Chiu, A. (2021). The data sampling effect on financial distress prediction by single and ensemble learning techniques. Communications in Statistics ‐ Theory and Methods, 52(1), 1–12.
Paper not yet in RePEc: Add citation now
- Sun, J., & Li, H. (2008). Listed companies' financial distress prediction based on weighted majority voting combination of multiple classifiers. Expert Systems with Applications, 35, 818–827. https://doi.org/10.1016/j.eswa.2007.07.045.
Paper not yet in RePEc: Add citation now
- Sun, J., Fujita, H., Chen, P., & Li, H. (2017). Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble. Knowledge‐Based Systems, 120, 4–14. https://doi.org/10.1016/j.knosys.2016.12.019.
Paper not yet in RePEc: Add citation now
- Sun, J., Li, J., Fujita, H., & Ai, W. G. (2022). Multiclass financial distress prediction based on oneversus‐one decomposition integrated with improved decision‐directed acyclic graph. Journal of Forecasting, 2022, 1–20.
Paper not yet in RePEc: Add citation now
Sun, J., Shang, Z., & Li, H. (2014). Imbalance‐oriented SVM methods for financial distress prediction: A comparative study among the new SB‐SVM‐ensemble method and traditional methods. Journal of the Operational Research Society, 65, 1905–1919. https://doi.org/10.1057/jors.2013.117.
Tang, X., Li, S., Tan, M., & Shi, W. (2020). Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods. Journal of Forecasting, 39, 1–19. https://doi.org/10.1002/for.2661.
- Tao, X., Li, Q., Guo, W., Ren, C., Li, C., Liu, R., & Zou, J. (2019). Self‐adaptive cost weights‐based support vector machine cost‐sensitive ensemble for imbalanced data classification. Information Sciences, 487, 31–56. https://doi.org/10.1016/j.ins.2019.02.062.
Paper not yet in RePEc: Add citation now
- Thomas, E., & Marilyn, G. (2000). Predicting bankruptcy using recursive partitioning and a realistically proportioned data set. Journal of Forecasting, 19(3), 219–230.
Paper not yet in RePEc: Add citation now
- Umar, F., & Muhammad, A. J. Q. (2019). Predicting multistage financial distress: Reflections on sampling, feature and model selection criteria[J]. Journal of Forecasting, 8(7), 632–648.
Paper not yet in RePEc: Add citation now
- Wang, G., Chen, G., & Chu, Y. (2018). A new random subspace method incorporating sentiment and textual information for financial distress prediction. Electronic Commerce Research and Applications, 29, 30–49. https://doi.org/10.1016/j.elerap.2018.03.004.
Paper not yet in RePEc: Add citation now
- Wang, L., & Wu, C. (2020). Dynamic imbalanced business credit evaluation based on Learn++ with sliding time window and weight sampling and FCM with multiple kernels. Information Sciences, 520, 305–323. https://doi.org/10.1016/j.ins.2020.02.011.
Paper not yet in RePEc: Add citation now
- Wang, Z., & Li, H. (2007). Financial distress prediction of Chinese listed companies: A rough set methodology. Chinese Management Studies, 1, 93–110. https://doi.org/10.1108/17506140710758008.
Paper not yet in RePEc: Add citation now
- Wu, L., Shao, Z., Yang, C., Ding, T., & Zhang, W. (2020). The impact of CSR and financial distress on financial performance—Evidence from Chinese listed companies of the manufacturing industry. Sustainability, 12, 6799. https://doi.org/10.3390/su12176799.
Paper not yet in RePEc: Add citation now
Xie, C., Luo, C., & Yu, X. (2011). Financial distress prediction based on SVM and MDA methods: The case of Chinese listed companies. Quality & Quantity, 45, 671–686. https://doi.org/10.1007/s11135-010-9376-y.
- Zeng, S., Li, Y., Yang, W., & Li, Y. (2020). A financial distress prediction model based on sparse algorithm and support vector machine. Mathematical Problems in Engineering, 2020, 1–11. https://doi.org/10.1155/2020/5625271.
Paper not yet in RePEc: Add citation now
- Zhou, L. (2013). Performance of corporate bankruptcy prediction models on imbalanced dataset: The effect of sampling methods. Knowledge‐Based Systems, 41, 16–25. https://doi.org/10.1016/j.knosys.2012.12.007.
Paper not yet in RePEc: Add citation now
- Zhu, F.‐J., Zhou, L.‐J., Zhou, M., & Pei, F. (2021). Financial distress prediction: A novel data segmentation research on Chinese listed companies. Technological and Economic Development of Economy, 27, 1413–1446. https://doi.org/10.3846/tede.2021.15337.
Paper not yet in RePEc: Add citation now
Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59–82. https://doi.org/10.2307/2490859.