Reconstruction of Z-score Model Based on Chinese Financial Data
DOI: 10.23977/ferm.2020.030108 | Downloads: 59 | Views: 2019
Author(s)
Liang Hou 1
Affiliation(s)
1 School of Management, Shanghai University, Shanghai, 200444, China
Corresponding Author
Liang HouABSTRACT
The Z-score model is widely used. However, due to the short development period of China's capital market and its great difference with western countries, the traditional Z-score model needs to be adjusted according to the actual situation of China's capital market. This paper reconstructs the model by using Chinese financial data of listed companies in 2017, and tests the identification and prediction abilities of the reconstruction model. This paper finds that the accuracy of the re-construction model has been greatly improved, but the abilities to identify and predict enterprises with financial failure are weaken than the original model.
KEYWORDS
Z-score model, Reconstruction, Logistic, Chinese capital marketCITE THIS PAPER
Liang Hou. Reconstruction of Z-score Model Based on Chinese Financial Data. Financial Engineering and Risk Management (2020) 3: 60-65. DOI: http://dx.doi.org/10.23977/ferm.2020.030108.
REFERENCES
[1] Altman E I, Haldeman R G, Narayanan P. ZETATM analysis: A new model to identify bankruptcy risk of corporations [J]. Journal of Banking & Finance,1977, 1 (1): 29-54.
[2] Wu Shinong, Lu Xianyi. A Study of Models for Predicting Financial Distress in China' s Listed Companies [J]. Economic Research Journal, 2001, (6): 46-55+96.
[3] Wang Yao. Corporate Governance Variables on Financial Distress Forecast Research [J]. East China Economic Management, 2009, 23 (4): 78-82.
[4] Liang Qi, Guo Xinwei, Shi Ning. Modeling Financial Distress Risk for Listed SMEs in China—Based on both Financial and Corporate Governance Information [J]. Economic Management Journal, 2012, 34 (03): 123-132.
[5] Liang Qi, Guo Xinwei, Shi Ning. Modeling Financial Distress Risks for SMEs Based on Random Effects Logistic Model [J]. Journal of Industrial Engineering and Engineering Management, 2014, 28 (3): 126-134.
[6] Guo Xinwei, Hu Xiao. Corporate Governance, Macroeconomic Condition and Financial Distress Prediction: Application of the Discrete Time Hazard Model [J]. Shanghai Journal of Economics, 2012, 24 (5): 85-97.
[7] Zhang Dong, Yao Qiaoqian, Wang Le, et al. A Kalman Filtering-based prediction of financial distress and its application: a case study of power enterprises [J]. Journal of Southeast University (Philosophy and Social Science), 2017, 19 (5): 132-140+148.
[8] Sun Jie, Li Hui, Han Jianguang. Dynamic Financial Distress Prediction Modeling Based on Rolling Time Window Support Vector Machine [J]. Journal of Industrial Engineering and Engineering Management, 2010, 24 (4): 174-180+92.
[9] Zhuang Qian, Chen Lianghua. Research on Dynamics of Financial Distress Theory and its Prediction [J]. Journal of Audit & Economics, 2014, 29 (5): 69-76.
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