Financial distress prediction: a novel data segmentation research on Chinese listed companies
Abstract
In the Chinese stock market, the unique special treatment (ST) warning mechanism can signal financial distress for listed companies. In existing studies, classification model has been developed to differentiate the two general listing states. However, this classification model cannot explain the internal changes of each listing state. Considering that the requirement of the withdrawal of ST in the mechanism is relatively loose, we propose a new segmentation approach for Chinese listed companies, which are divided into negative companies and positive companies according to the number of times being labeled ST. Under the framework of data mining, we use financial indicators, non-financial indicators, and time series to build a financial distress prediction model of distinguishing the long-term development of different Chinese listed companies. Through data segmentation, we find that the negative samples have a huge destructive interference on the prediction effect of the total sample. On the contrary, positive companies improve the prediction accuracy in all aspects and the optimal feature set is also different from all companies. The main contribution of the paper is to analyze the internal impact of the deterioration of financial distress prediction in time series and construct an optimization model for positive companies.
First published online 04 November 2021
Keyword : financial distress prediction, Chinese listed companies, ensemble learning, data mining, data segmentation, special treatment
How to Cite
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(6), 1413-1446. https://doi.org/10.3846/tede.2021.15337
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O., & Bilal, M. (2017). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Application, 94, 164–184. https://doi.org/10.1016/j.eswa.2017.10.040
Alfaro, E., Garcia, N., Gamez, M., & Elizondo, D. (2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems, 45(1), 110–122. https://doi.org/10.1016/j.dss.2007.12.002
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
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
Batmaz, I., Danisoglu, S., Yazici, C., & Kartal-Koc, E. (2017). A data mining application to deposit pricing: Main determinants and prediction mod-els. Applied Soft Computing, 60, 808–819. https://doi.org/10.1016/j.asoc.2017.07.047
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. https://doi.org/10.2307/2490171
Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39, 3446–3453. https://doi.org/10.1016/j.eswa.2011.09.033
Chen, N., Ribeiro, B., Vieira, A. S., Duarte, J., & Neves, J. C. (2011). A genetic algorithm-based approach to cost-sensitive bankruptcy prediction. Expert Systems with Applications, 38, 12939–12945. https://doi.org/10.1016/j.eswa.2011.04.090
Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187–205. https://doi.org/10.1016/j.eswa.2017.04.030
Cleofas-Sánchez, L., García, V., Marqués, A., & Sénchez, J. (2016). Financial distress prediction using the hybrid associative memory with trans-lation. Applied Soft Computing, 44, 144–152. https://doi.org/10.1016/j.asoc.2016.04.005
Danenas, P., & Garsva, G. (2015). Selection of support vector machines based classifiers for credit risk domain. Expert Systems with Applications, 42(6), 3194–3204. https://doi.org/10.1016/j.eswa.2014.12.001
Das, A. K., Das, S., & Ghosh, A. (2017). Ensemble feature selection using bi-objective genetic algorithm. Knowledge-Based Systems, 123, 116–127. https://doi.org/10.1016/j.knosys.2017.02.013
Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning (pp. 233–240). Association for Computing Machinery. https://doi.org/10.1145/1143844.1143874
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, 90(3), 487–513. https://doi.org/10.1016/0377-2217(95)00070-4
Ding, Y. S., Song, X. P., & Zen, Y. M. (2008). Forecasting financial condition of Chinese listed companies based on support vector machine. Expert Systems with Applications, 34(4), 3081–3089. https://doi.org/10.1016/j.eswa.2007.06.037
du Jardin, P. (2016). A two-stage classification technique for bankruptcy prediction. European Journal of Operational Research, 254(1), 236–252. https://doi.org/10.1016/j.ejor.2016.03.008
Dutta, D., Sil, J., & Dutta, P. (2020). A bi-phased multi-objective genetic algorithm based classifier. Expert Systems with Applications, 146, 113163. https://doi.org/10.1016/j.eswa.2019.113163
Espejo, P. G., Ventura, S., & Herrera, F. (2010). A survey on the application of genetic programming to classification. IEEE Transactions on Sys-tems Man and Cybernetics Part C (Applications and Reviews), 40(2), 121–144. https://doi.org/10.1109/TSMCC.2009.2033566
Farisha, H., Hafiza, A. H., & Zalailah, S. (2012). Motivation for earnings management among auditors in Malaysia. Procedia - Social and Behav-ioral Sciences, 65, 239–246. https://doi.org/10.1016/j.sbspro.2012.11.117
Geng, R. B., Bose, I., & Chen, X. (2015). Prediction of financial distress: An empirical study of listed Chinese companies using data mining. Eu-ropean Journal of Operational Research, 241(1), 236–247. https://doi.org/10.1016/j.ejor.2014.08.016
Gorzalczany, M. B., & Rudzinski, F. (2016). A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability. Applied Soft Computing, 40, 206–220. https://doi.org/10.1016/j.asoc.2015.11.037
Heo, J., & Yang, J. Y. (2014). AdaBoost based bankruptcy forecasting of Korean construction companies. Applied Soft Computing, 24, 494–499. https://doi.org/10.1016/j.asoc.2014.08.009
Yu, E. Z., & Cho, S. (2006). Ensemble based on GA wrapper feature selection. Computers & Industrial Engineering, 51(1), 111–116. https://doi.org/10.1016/j.cie.2006.07.004
Kim, H. S., & Sohn, S. Y. (2010). Support vector machines for default prediction of SMEs based on technology credit. European Journal of Op-erational Research, 201, 838–846. https://doi.org/10.1016/j.ejor.2009.03.036
Kim, M. J., Kang, D. K., & Kim, H. B. (2015). Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction. Expert Systems with Applications, 42(3), 1074–1082. https://doi.org/10.1016/j.eswa.2014.08.025
Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering and Sys-tem Safety, 91(9), 992–1007. https://doi.org/10.1016/j.ress.2005.11.018
Krogh, A., & Vedelsby, J. (1995). Neural network ensembles, cross validation, and active learning. Advances in Neural Information Processing Systems, 7, 231–238.
Liang, D., Lu, C. C., Tsai, C. F., & Shih, G. A. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A compre-hensive study. European Journal of Operational Research, 252(2), 561–572. https://doi.org/10.1016/j.ejor.2016.01.012
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
Lin, W. Y., Hu, Y. H., & Tsai, C. F. (2012). Machine learning in financial crisis prediction: A survey. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 42(4), 421–436. https://doi.org/10.1109/TSMCC.2011.2170420
Martin, D. (1977). Early warnings of bank failure: A logit regression approach. Journal of Banking and Finance, 1(3), 249–276. https://doi.org/10.1016/0378-4266(77)90022-X
Miglani, S., Ahmed, K., & Henry, D. (2015). Voluntary corporate governance structure and financial distress: Evidence from Australia. Journal of Contemporary Accounting & Economics, 11(1), 18–30. https://doi.org/10.1016/j.jcae.2014.12.005
Mousavi, M. M., & Lin, J. L. (2020). The application of PROMETHEE multi-criteria decision aid in financial decision making: Case of distress prediction models evaluation. Expert Systems with Applications, 159, 113438. https://doi.org/10.1016/j.eswa.2020.113438
Olson, D. L., Delen, D., & Meng, Y. Y. (2012). Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Sys-tems, 52(2), 464–473. https://doi.org/10.1016/j.dss.2011.10.007
Sanchez-Lasheras, F., de Andres, J., Lorca, P., & de Cos Juez, F. J. (2012). A hybrid device for the solution of sampling bias problems in the forecasting of firms’ bankruptcy. Expert Systems with Applications, 39, 7512–7523. https://doi.org/10.1016/j.eswa.2012.01.135
Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing, 5(4), 3–22.
Sun, J., Lang, J., Fujita, H., & Li, H. (2018). Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates. Information Sciences, 425, 76–91. https://doi.org/10.1016/j.ins.2017.10.017
Sun, J., Li, H., Fujita, H., Fu, B. B., & Ai, W. G. (2020). Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensem-ble combined with SMOTE and time weighting. Information Fusion, 54, 128–144. https://doi.org/10.1016/j.inffus.2019.07.006
Tian, Y., Shi, Y., & Liu, X. (2012). Recent advances on support vector machines research. Technological and Economic Development of Economy, 18(1), 5–33. https://doi.org/10.3846/20294913.2012.661205
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
Wang, G., Ma, J. L., Chen, G., & Yang, Y. (2020). Financial distress prediction: Regularized sparse-based Random Subspace with ER aggregation rule incorporating textual disclosures. Applied Soft Computing, 90, 106152. https://doi.org/10.1016/j.asoc.2020.106152
Wang, G., Ma, J., & Yang, S. (2014). An improved boosting based on feature selection for corporate bankruptcy prediction. Expert Systems with Applications, 41(5), 2353–2361. https://doi.org/10.1016/j.eswa.2013.09.033
West, R. C. (1985). A factor-analytic approach to bank condition. Journal of Banking & Finance, 9(2), 253–266. https://doi.org/10.1016/0378-4266(85)90021-4
Xia, Y. F., Liu, C. Z., Li, Y. Y., & Liu, N. N. (2017). A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Systems with Applications, 78, 225–241. https://doi.org/10.1016/j.eswa.2017.02.017
Zhou, L. G. (2013). Predicting the removal of special treatment or delisting risk warning for listed company in China with Adaboost. Procedia Computer Science, 17, 633–640. https://doi.org/10.1016/j.procs.2013.05.082
Zhou, L. G., Lu, D., & Fujita, H. (2015). The performance of corporate financial distress prediction models with features selection guided by do-main knowledge and data mining approaches. Knowledge-Based Systems, 85, 52–61. https://doi.org/10.1016/j.knosys.2015.04.017
Zhou, L. G., Tam, K. P., & Fujita, H. (2016). Predicting the listing status of Chinese listed companies with multi-class classification models. In-formation Sciences, 328, 222–236. https://doi.org/10.1016/j.ins.2015.08.036
Zieba, M., Tomczak, S. K., & Tomczak, J. M. (2016). Ensemble boosted trees with synthetic features generation in application to bankruptcy pre-diction. Expert Systems with Applications, 58, 93–101. https://doi.org/10.1016/j.eswa.2016.04.001
Alfaro, E., Garcia, N., Gamez, M., & Elizondo, D. (2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems, 45(1), 110–122. https://doi.org/10.1016/j.dss.2007.12.002
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
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
Batmaz, I., Danisoglu, S., Yazici, C., & Kartal-Koc, E. (2017). A data mining application to deposit pricing: Main determinants and prediction mod-els. Applied Soft Computing, 60, 808–819. https://doi.org/10.1016/j.asoc.2017.07.047
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. https://doi.org/10.2307/2490171
Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39, 3446–3453. https://doi.org/10.1016/j.eswa.2011.09.033
Chen, N., Ribeiro, B., Vieira, A. S., Duarte, J., & Neves, J. C. (2011). A genetic algorithm-based approach to cost-sensitive bankruptcy prediction. Expert Systems with Applications, 38, 12939–12945. https://doi.org/10.1016/j.eswa.2011.04.090
Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187–205. https://doi.org/10.1016/j.eswa.2017.04.030
Cleofas-Sánchez, L., García, V., Marqués, A., & Sénchez, J. (2016). Financial distress prediction using the hybrid associative memory with trans-lation. Applied Soft Computing, 44, 144–152. https://doi.org/10.1016/j.asoc.2016.04.005
Danenas, P., & Garsva, G. (2015). Selection of support vector machines based classifiers for credit risk domain. Expert Systems with Applications, 42(6), 3194–3204. https://doi.org/10.1016/j.eswa.2014.12.001
Das, A. K., Das, S., & Ghosh, A. (2017). Ensemble feature selection using bi-objective genetic algorithm. Knowledge-Based Systems, 123, 116–127. https://doi.org/10.1016/j.knosys.2017.02.013
Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning (pp. 233–240). Association for Computing Machinery. https://doi.org/10.1145/1143844.1143874
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, 90(3), 487–513. https://doi.org/10.1016/0377-2217(95)00070-4
Ding, Y. S., Song, X. P., & Zen, Y. M. (2008). Forecasting financial condition of Chinese listed companies based on support vector machine. Expert Systems with Applications, 34(4), 3081–3089. https://doi.org/10.1016/j.eswa.2007.06.037
du Jardin, P. (2016). A two-stage classification technique for bankruptcy prediction. European Journal of Operational Research, 254(1), 236–252. https://doi.org/10.1016/j.ejor.2016.03.008
Dutta, D., Sil, J., & Dutta, P. (2020). A bi-phased multi-objective genetic algorithm based classifier. Expert Systems with Applications, 146, 113163. https://doi.org/10.1016/j.eswa.2019.113163
Espejo, P. G., Ventura, S., & Herrera, F. (2010). A survey on the application of genetic programming to classification. IEEE Transactions on Sys-tems Man and Cybernetics Part C (Applications and Reviews), 40(2), 121–144. https://doi.org/10.1109/TSMCC.2009.2033566
Farisha, H., Hafiza, A. H., & Zalailah, S. (2012). Motivation for earnings management among auditors in Malaysia. Procedia - Social and Behav-ioral Sciences, 65, 239–246. https://doi.org/10.1016/j.sbspro.2012.11.117
Geng, R. B., Bose, I., & Chen, X. (2015). Prediction of financial distress: An empirical study of listed Chinese companies using data mining. Eu-ropean Journal of Operational Research, 241(1), 236–247. https://doi.org/10.1016/j.ejor.2014.08.016
Gorzalczany, M. B., & Rudzinski, F. (2016). A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability. Applied Soft Computing, 40, 206–220. https://doi.org/10.1016/j.asoc.2015.11.037
Heo, J., & Yang, J. Y. (2014). AdaBoost based bankruptcy forecasting of Korean construction companies. Applied Soft Computing, 24, 494–499. https://doi.org/10.1016/j.asoc.2014.08.009
Yu, E. Z., & Cho, S. (2006). Ensemble based on GA wrapper feature selection. Computers & Industrial Engineering, 51(1), 111–116. https://doi.org/10.1016/j.cie.2006.07.004
Kim, H. S., & Sohn, S. Y. (2010). Support vector machines for default prediction of SMEs based on technology credit. European Journal of Op-erational Research, 201, 838–846. https://doi.org/10.1016/j.ejor.2009.03.036
Kim, M. J., Kang, D. K., & Kim, H. B. (2015). Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction. Expert Systems with Applications, 42(3), 1074–1082. https://doi.org/10.1016/j.eswa.2014.08.025
Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering and Sys-tem Safety, 91(9), 992–1007. https://doi.org/10.1016/j.ress.2005.11.018
Krogh, A., & Vedelsby, J. (1995). Neural network ensembles, cross validation, and active learning. Advances in Neural Information Processing Systems, 7, 231–238.
Liang, D., Lu, C. C., Tsai, C. F., & Shih, G. A. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A compre-hensive study. European Journal of Operational Research, 252(2), 561–572. https://doi.org/10.1016/j.ejor.2016.01.012
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
Lin, W. Y., Hu, Y. H., & Tsai, C. F. (2012). Machine learning in financial crisis prediction: A survey. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 42(4), 421–436. https://doi.org/10.1109/TSMCC.2011.2170420
Martin, D. (1977). Early warnings of bank failure: A logit regression approach. Journal of Banking and Finance, 1(3), 249–276. https://doi.org/10.1016/0378-4266(77)90022-X
Miglani, S., Ahmed, K., & Henry, D. (2015). Voluntary corporate governance structure and financial distress: Evidence from Australia. Journal of Contemporary Accounting & Economics, 11(1), 18–30. https://doi.org/10.1016/j.jcae.2014.12.005
Mousavi, M. M., & Lin, J. L. (2020). The application of PROMETHEE multi-criteria decision aid in financial decision making: Case of distress prediction models evaluation. Expert Systems with Applications, 159, 113438. https://doi.org/10.1016/j.eswa.2020.113438
Olson, D. L., Delen, D., & Meng, Y. Y. (2012). Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Sys-tems, 52(2), 464–473. https://doi.org/10.1016/j.dss.2011.10.007
Sanchez-Lasheras, F., de Andres, J., Lorca, P., & de Cos Juez, F. J. (2012). A hybrid device for the solution of sampling bias problems in the forecasting of firms’ bankruptcy. Expert Systems with Applications, 39, 7512–7523. https://doi.org/10.1016/j.eswa.2012.01.135
Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing, 5(4), 3–22.
Sun, J., Lang, J., Fujita, H., & Li, H. (2018). Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates. Information Sciences, 425, 76–91. https://doi.org/10.1016/j.ins.2017.10.017
Sun, J., Li, H., Fujita, H., Fu, B. B., & Ai, W. G. (2020). Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensem-ble combined with SMOTE and time weighting. Information Fusion, 54, 128–144. https://doi.org/10.1016/j.inffus.2019.07.006
Tian, Y., Shi, Y., & Liu, X. (2012). Recent advances on support vector machines research. Technological and Economic Development of Economy, 18(1), 5–33. https://doi.org/10.3846/20294913.2012.661205
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
Wang, G., Ma, J. L., Chen, G., & Yang, Y. (2020). Financial distress prediction: Regularized sparse-based Random Subspace with ER aggregation rule incorporating textual disclosures. Applied Soft Computing, 90, 106152. https://doi.org/10.1016/j.asoc.2020.106152
Wang, G., Ma, J., & Yang, S. (2014). An improved boosting based on feature selection for corporate bankruptcy prediction. Expert Systems with Applications, 41(5), 2353–2361. https://doi.org/10.1016/j.eswa.2013.09.033
West, R. C. (1985). A factor-analytic approach to bank condition. Journal of Banking & Finance, 9(2), 253–266. https://doi.org/10.1016/0378-4266(85)90021-4
Xia, Y. F., Liu, C. Z., Li, Y. Y., & Liu, N. N. (2017). A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Systems with Applications, 78, 225–241. https://doi.org/10.1016/j.eswa.2017.02.017
Zhou, L. G. (2013). Predicting the removal of special treatment or delisting risk warning for listed company in China with Adaboost. Procedia Computer Science, 17, 633–640. https://doi.org/10.1016/j.procs.2013.05.082
Zhou, L. G., Lu, D., & Fujita, H. (2015). The performance of corporate financial distress prediction models with features selection guided by do-main knowledge and data mining approaches. Knowledge-Based Systems, 85, 52–61. https://doi.org/10.1016/j.knosys.2015.04.017
Zhou, L. G., Tam, K. P., & Fujita, H. (2016). Predicting the listing status of Chinese listed companies with multi-class classification models. In-formation Sciences, 328, 222–236. https://doi.org/10.1016/j.ins.2015.08.036
Zieba, M., Tomczak, S. K., & Tomczak, J. M. (2016). Ensemble boosted trees with synthetic features generation in application to bankruptcy pre-diction. Expert Systems with Applications, 58, 93–101. https://doi.org/10.1016/j.eswa.2016.04.001