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Prediction of financial distress: An empirical study of listed Chinese companies using data mining

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Cited by:

  1. Yue Qiu & Jiabei He & Zhensong Chen & Yinhong Yao & Yi Qu, 2024. "A novel semisupervised learning method with textual information for financial distress prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2478-2494, November.
  2. Xiaowei Chen & Cong Zhai, 2023. "Bagging or boosting? Empirical evidence from financial statement fraud detection," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(5), pages 5093-5142, December.
  3. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
  4. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
  5. Yi Jiang & Stewart Jones, 2018. "Corporate distress prediction in China: a machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(4), pages 1063-1109, December.
  6. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
  7. Mattia Pellegrino & Gianfranco Lombardo & George Adosoglou & Stefano Cagnoni & Panos M. Pardalos & Agostino Poggi, 2024. "A Multi-Head LSTM Architecture for Bankruptcy Prediction with Time Series Accounting Data," Future Internet, MDPI, vol. 16(3), pages 1-20, February.
  8. Jiaming Liu & Chong Wu, 2017. "Dynamic forecasting of financial distress: the hybrid use of incremental bagging and genetic algorithm—empirical study of Chinese listed corporations," Risk Management, Palgrave Macmillan, vol. 19(1), pages 32-52, February.
  9. Zhao Wang & Cuiqing Jiang & Huimin Zhao, 2022. "Know Where to Invest: Platform Risk Evaluation in Online Lending," Information Systems Research, INFORMS, vol. 33(3), pages 765-783, September.
  10. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
  11. Oyakhilome Ibhagui, 2020. "Financial Reforms, Capital Investment and Financial Intermediation in China," South Asian Journal of Macroeconomics and Public Finance, , vol. 9(1), pages 58-86, June.
  12. Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2022. "Does board committee independence affect financial distress likelihood? A comparison of China with the UK," Asia Pacific Journal of Management, Springer, vol. 39(2), pages 723-761, June.
  13. Gianfranco Lombardo & Mattia Pellegrino & George Adosoglou & Stefano Cagnoni & Panos M. Pardalos & Agostino Poggi, 2022. "Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks," Future Internet, MDPI, vol. 14(8), pages 1-23, August.
  14. Adriana Csikosova & Maria Janoskova & Katarina Culkova, 2020. "Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure," JRFM, MDPI, vol. 13(10), pages 1-14, September.
  15. Yi Cao & Xiaoquan Liu & Jia Zhai & Shan Hua, 2022. "A two‐stage Bayesian network model for corporate bankruptcy prediction," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 455-472, January.
  16. Yulian Zhang & Shigeyuki Hamori, 2020. "Forecasting Crude Oil Market Crashes Using Machine Learning Technologies," Energies, MDPI, vol. 13(10), pages 1-14, May.
  17. Alberto Tron & Maurizio Dallocchio & Salvatore Ferri & Federico Colantoni, 2023. "Corporate governance and financial distress: lessons learned from an unconventional approach," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 425-456, June.
  18. Dawen Yan & Guotai Chi & Kin Keung Lai, 2020. "Financial Distress Prediction and Feature Selection in Multiple Periods by Lassoing Unconstrained Distributed Lag Non-linear Models," Mathematics, MDPI, vol. 8(8), pages 1-27, August.
  19. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
  20. Ruan, Xuanmin & Zhu, Yuanyang & Li, Jiang & Cheng, Ying, 2020. "Predicting the citation counts of individual papers via a BP neural network," Journal of Informetrics, Elsevier, vol. 14(3).
  21. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
  22. Jiang, Cuiqing & Lyu, Ximei & Yuan, Yufei & Wang, Zhao & Ding, Yong, 2022. "Mining semantic features in current reports for financial distress prediction: Empirical evidence from unlisted public firms in China," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1086-1099.
  23. Ren, Xiaocong & Huang, Zilong & He, Yiqun, 2024. "Financial warning for coal mining investments: Evidence from the fruit fly optimisation algorithm with backpropagation neural networks," Energy Economics, Elsevier, vol. 134(C).
  24. Khaled Halteh & Kuldeep Kumar & Adrian Gepp, 2018. "Using Cutting-Edge Tree-Based Stochastic Models to Predict Credit Risk," Risks, MDPI, vol. 6(2), pages 1-13, May.
  25. Sami Ben Jabeur & Nicolae Stef & Pedro Carmona, 2023. "Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 715-741, February.
  26. Yanfang Zhang & Mushang Lee, 2019. "A Hybrid Model for Addressing the Relationship between Financial Performance and Sustainable Development," Sustainability, MDPI, vol. 11(10), pages 1-15, May.
  27. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
  28. Guanping Zhou, 2019. "Financial distress prevention in China: Does gender of board of directors matter?," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 9(6), pages 1-8.
  29. Barboza, Flavio & Altman, Edward, 2024. "Predicting financial distress in Latin American companies: A comparative analysis of logistic regression and random forest models," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
  30. Li, Zhiyong & Crook, Jonathan & Andreeva, Galina & Tang, Ying, 2021. "Predicting the risk of financial distress using corporate governance measures," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
  31. Mohammad Shamsu Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib & Kunpeng Yuan, 2022. "Modeling credit risk with a multi‐stage hybrid model: An alternative statistical approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1386-1415, November.
  32. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
  33. David Alaminos & Manuel Ángel Fernández, 2019. "Why do football clubs fail financially? A financial distress prediction model for European professional football industry," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-15, December.
  34. Lei Ruan & Heng Liu, 2021. "Financial Distress Prediction Using GA-BP Neural Network Model," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 13(3), pages 1-1, March.
  35. Jiang, Cuiqing & Zhou, Yiru & Chen, Bo, 2023. "Mining semantic features in patent text for financial distress prediction," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
  36. ElBannan, Mona A., 2021. "On the prediction of financial distress in emerging markets: What matters more? Empirical evidence from Arab spring countries," Emerging Markets Review, Elsevier, vol. 47(C).
  37. Mahdiani, Pegah & Ranjbarfard, Mina, 2018. "بررسی کاربردهای داده‌کاوی در مدیریت مشتریان شرکت‌های هواپیمایی [Data mining for managing customers of airline companies]," MPRA Paper 114737, University Library of Munich, Germany.
  38. Kshitij Sharma & Yogesh K. Dwivedi & Bhimaraya Metri, 2024. "Incorporating causality in energy consumption forecasting using deep neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 537-572, August.
  39. Yfanti, Stavroula & Karanasos, Menelaos & Zopounidis, Constantin & Christopoulos, Apostolos, 2023. "Corporate credit risk counter-cyclical interdependence: A systematic analysis of cross-border and cross-sector correlation dynamics," European Journal of Operational Research, Elsevier, vol. 304(2), pages 813-831.
  40. Maria Goreti Kentris Indarti & Jacobus Widiatmoko & Imang Dapit Pamungkas, 2021. "Corporate Governance Structures and Probability of Financial Distress: Evidence From Indonesia Manufacturing Companies," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 12(1), pages 174-183, January.
  41. Fernández-Gámez, Manuel Ángel & Soria, Juan Antonio Campos & Santos, José António C. & Alaminos, David, 2020. "European country heterogeneity in financial distress prediction: An empirical analysis with macroeconomic and regulatory factors," Economic Modelling, Elsevier, vol. 88(C), pages 398-407.
  42. Lifang Zhang & Mohammad Zoynul Abedin & Zhenkun Liu, 2024. "Incorporating media news to predict financial distress: Case study on Chinese listed companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1374-1398, August.
  43. Paras Arora & Suman Saurabh, 2022. "Predicting distress: a post Insolvency and Bankruptcy Code 2016 analysis," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(3), pages 604-622, July.
  44. Ding, Shusheng & Cui, Tianxiang & Bellotti, Anthony Graham & Abedin, Mohammad Zoynul & Lucey, Brian, 2023. "The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 90(C).
  45. Xiaobo Tang & Shixuan Li & Mingliang Tan & Wenxuan Shi, 2020. "Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 769-787, August.
  46. Zhao, Qi & Xu, Weijun & Ji, Yucheng, 2023. "Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?," International Review of Financial Analysis, Elsevier, vol. 89(C).
  47. Kim Long Tran & Hoang Anh Le & Thanh Hien Nguyen & Duc Trung Nguyen, 2022. "Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam," Data, MDPI, vol. 7(11), pages 1-12, November.
  48. Katsafados, Apostolos G. & Androutsopoulos, Ion & Chalkidis, Ilias & Fergadiotis, Manos & Leledakis, George N. & Pyrgiotakis, Emmanouil G., 2020. "Textual Information and IPO Underpricing: A Machine Learning Approach," MPRA Paper 103813, University Library of Munich, Germany.
  49. Apostolos G. Katsafados & Dimitris Anastasiou, 2024. "Short-term prediction of bank deposit flows: do textual features matter?," Annals of Operations Research, Springer, vol. 338(2), pages 947-972, July.
  50. Zhen Jia Liu & Yi Shu Wang, 2016. "Corporate Failure Prediction Models for Advanced Research in China: Identifying the Optimal Cut Off Point," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 6(1), pages 54-65, January.
  51. Jie Sun & Mengjie Zhou & Wenguo Ai & Hui Li, 2019. "Dynamic prediction of relative financial distress based on imbalanced data stream: from the view of one industry," Risk Management, Palgrave Macmillan, vol. 21(4), pages 215-242, December.
  52. Su, Ruixin & Shui, Xiaolong & Zhang, Minhao & Li, Si, 2024. "Too hot to be innovative: Examining the relationship between high temperatures and corporate green innovation," International Journal of Production Economics, Elsevier, vol. 274(C).
  53. Fatima Zahra Azayite & Said Achchab, 2019. "A hybrid neural network model based on improved PSO and SA for bankruptcy prediction," Papers 1907.12179, arXiv.org.
  54. Joanna Wieprow & Agnieszka Gawlik, 2021. "The Use of Discriminant Analysis to Assess the Risk of Bankruptcy of Enterprises in Crisis Conditions Using the Example of the Tourism Sector in Poland," Risks, MDPI, vol. 9(4), pages 1-11, April.
  55. Feuerriegel, Stefan & Gordon, Julius, 2019. "News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions," European Journal of Operational Research, Elsevier, vol. 272(1), pages 162-175.
  56. Kolesnikova, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2019. "Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting," IRTG 1792 Discussion Papers 2019-023, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  57. Borchert, Philipp & Coussement, Kristof & De Caigny, Arno & De Weerdt, Jochen, 2023. "Extending business failure prediction models with textual website content using deep learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 348-357.
  58. Chi-Wei Su & Xu-Yu Cai & Ran Tao, 2020. "Can Stock Investor Sentiment Be Contagious in China?," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
  59. Guo, Hongfeng & Xia, Shengxiang & An, Qiguang & Zhang, Xin & Sun, Weihua & Zhao, Xinyao, 2020. "Empirical study of financial crises based on topological data analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
  60. Michal Pavlicko & Marek Durica & Jaroslav Mazanec, 2021. "Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries," Mathematics, MDPI, vol. 9(16), pages 1-26, August.
  61. Lenka Papíková & Mário Papík, 2022. "Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 254-281, October.
  62. Sebastian Klaudiusz Tomczak, 2021. "Ratio Selection between Six Sectors in the Visegrad Group Using Parametric and Nonparametric ANOVA," Energies, MDPI, vol. 14(21), pages 1-20, November.
  63. Madalina Ecaterina Popescu & Victor Dragotă, 2018. "What Do Post-Communist Countries Have in Common When Predicting Financial Distress?," Prague Economic Papers, Prague University of Economics and Business, vol. 2018(6), pages 637-653.
  64. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2020. "Corporate Default Predictions Using Machine Learning: Literature Review," Sustainability, MDPI, vol. 12(16), pages 1-11, August.
  65. Yang-Chieh Chin & Wen-Zhong Su & Shih-Chih Chen & Jianing Hou & Yu-Chuan Huang, 2018. "Exploring Users’ Self-Disclosure Intention on Social Networking Applying Novel Soft Computing Theories," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
  66. du Jardin, Philippe, 2021. "Forecasting corporate failure using ensemble of self-organizing neural networks," European Journal of Operational Research, Elsevier, vol. 288(3), pages 869-885.
  67. Amal Al Ali & Ahmed M. Khedr & Magdi El Bannany & Sakeena Kanakkayil, 2023. "GALSTM-FDP: A Time-Series Modeling Approach Using Hybrid GA and LSTM for Financial Distress Prediction," IJFS, MDPI, vol. 11(1), pages 1-15, February.
  68. Tang, Wenjin & Bu, Hui & Zuo, Yuan & Wu, Junjie, 2024. "Unlocking the power of the topic content in news headlines: BERTopic for predicting Chinese corporate bond defaults," Finance Research Letters, Elsevier, vol. 62(PA).
  69. Chi, Guotai & Dong, Bingjie & Zhou, Ying & Jin, Peng, 2024. "Long-horizon predictions of credit default with inconsistent customers," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
  70. Diego Vallarino, 2024. "A Comparative Machine Learning Survival Models Analysis for Predicting Time to Bank Failure in the US (2001-2023)," Journal of Economic Analysis, Anser Press, vol. 3(1), pages 129-144, March.
  71. Jiaming Liu & Chong Wu & Yongli Li, 2019. "Improving Financial Distress Prediction Using Financial Network-Based Information and GA-Based Gradient Boosting Method," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 851-872, February.
  72. Sun, Xiaojun & Lei, Yalin, 2021. "Research on financial early warning of mining listed companies based on BP neural network model," Resources Policy, Elsevier, vol. 73(C).
  73. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
  74. Sermpinis, Georgios & Stasinakis, Charalampos & Theofilatos, Konstantinos & Karathanasopoulos, Andreas, 2015. "Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations," European Journal of Operational Research, Elsevier, vol. 247(3), pages 831-846.
  75. Marcin Hernes & Adrianna Kozierkiewicz & Marcin Maleszka & Artur Rot & Agata Kozina & Karolina Matenczuk & Jakub Janus & Ewelina Wrobel, 2021. "Deep Learning for Repayment Prediction in Leasing Companies," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 1134-1148.
  76. Katsafados, Apostolos G. & Leledakis, George N. & Pyrgiotakis, Emmanouil G. & Androutsopoulos, Ion & Fergadiotis, Manos, 2024. "Machine learning in bank merger prediction: A text-based approach," European Journal of Operational Research, Elsevier, vol. 312(2), pages 783-797.
  77. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
  78. Sermpinis, Georgios & Stasinakis, Charalampos & Rosillo, Rafael & de la Fuente, David, 2017. "European Exchange Trading Funds Trading with Locally Weighted Support Vector Regression," European Journal of Operational Research, Elsevier, vol. 258(1), pages 372-384.
  79. Deng, Shangkun & Luo, Qunfang & Zhu, Yingke & Ning, Hong & Shimada, Tatsuro, 2024. "Financial risk forewarning with an interpretable ensemble learning approach: An empirical analysis based on Chinese listed companies," Pacific-Basin Finance Journal, Elsevier, vol. 85(C).
  80. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
  81. Daniel Ogachi & Richard Ndege & Peter Gaturu & Zeman Zoltan, 2020. "Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya," JRFM, MDPI, vol. 13(3), pages 1-14, March.
  82. Yuan, Kunpeng & Chi, Guotai & Zhou, Ying & Yin, Hailei, 2022. "A novel two-stage hybrid default prediction model with k-means clustering and support vector domain description," Research in International Business and Finance, Elsevier, vol. 59(C).
  83. Jiaming Liu & Chengzhang Li & Peng Ouyang & Jiajia Liu & Chong Wu, 2023. "Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1112-1137, August.
  84. Ayoola Tajudeen John & Obokoh Lawrence Ogechukwu, 2018. "Corporate Governance and Financial Distress in the Banking Industry: Nigerian Experience," Journal of Economics and Behavioral Studies, AMH International, vol. 10(1), pages 182-193.
  85. Yong Sun & Hui Liu & Jiwei Liu & Mingyu Sun & Qun Li, 2023. "Analysis of Factors Influencing the Corporate Performance of Listed Companies in China’s Agriculture and Forestry Sector Based on a Panel Threshold Model," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
  86. repec:fst:rfsisf:v:8:y:2023:i:special-june_2023:p:45-56 is not listed on IDEAS
  87. repec:prg:jnlpep:v:preprint:id:664:p:1-17 is not listed on IDEAS
  88. Muhammad Khafid & Tusyanah Tusyanah & Tejo Suryanto, 2019. "Analyzing the Determinants of Financial Distress in Indonesian Mining Companies," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(4), pages 353-368.
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