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Financial distress prediction based on SVM and MDA methods: the case of Chinese listed companies

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  • Chi Xie
  • Changqing Luo
  • Xiang Yu

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  • Chi Xie & Changqing Luo & Xiang Yu, 2011. "Financial distress prediction based on SVM and MDA methods: the case of Chinese listed companies," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(3), pages 671-686, April.
    • Handle: RePEc:spr:qualqt:v:45:y:2011:i:3:p:671-686
    DOI: 10.1007/s11135-010-9376-y
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    References listed on IDEAS

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    Cited by:

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    2. Feng Shen & Yunwen Ma & Run Wang & Ningning Pan & Zhiyi Meng, 2019. "Does environmental performance affect financial performance? Evidence from Chinese listed companies in heavily polluting industries," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 1941-1958, July.
    3. Vladislav V. Afanasev & Yulia A. Tarasova, 2022. "Default Prediction for Housing and Utilities Management Firms Using Non-Financial Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 91-110, December.
    4. Ming-Fu Hsu & Ping-Feng Pai, 2013. "Incorporating support vector machines with multiple criteria decision making for financial crisis analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(6), pages 3481-3492, October.
    5. Pawan Kumar Singh & Anushka Chouhan & Rajiv Kumar Bhatt & Ravi Kiran & Ansari Saleh Ahmar, 2022. "Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 2023-2033, August.
    6. 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.
    7. Mostafaei, Kamran & maleki, Shaho & Zamani Ahmad Mahmoudi, Mohammad & Knez, Dariusz, 2022. "Risk management prediction of mining and industrial projects by support vector machine," Resources Policy, Elsevier, vol. 78(C).
    8. Mahtani, Umesh S. & Garg, Chandra Prakash, 2018. "An analysis of key factors of financial distress in airline companies in India using fuzzy AHP framework," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 87-102.
    9. 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.
    10. Sabek Amine, 2023. "Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 9(1), pages 16-32, July.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Fereshteh Taromideh & Ramin Fazloula & Bahram Choubin & Alireza Emadi & Ronny Berndtsson, 2022. "Urban Flood-Risk Assessment: Integration of Decision-Making and Machine Learning," Sustainability, MDPI, vol. 14(8), pages 1-22, April.

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