The predictive validity of distance-to-default on financial distress: Evidence from Chinese-listed companies

2018 ◽  
pp. 633-639
Keyword(s):  
Predictive Validity ◽  
Financial Distress ◽  
Listed Companies ◽  
Chinese Listed Companies ◽  
Distance To Default

scholarly journals The Impact of CSR and Financial Distress on Financial Performance—Evidence from Chinese Listed Companies of the Manufacturing Industry

2020 ◽  
Vol 12 (17) ◽  
pp. 6799 ◽  
Author(s):  
Liu Wu ◽  
Zhen Shao ◽  
Changhui Yang ◽  
Tao Ding ◽  
Wan Zhang
Keyword(s):  
Financial Performance ◽  
Financial Distress ◽  
Manufacturing Industry ◽  
Positive Impact ◽  
Listed Companies ◽  
Research Database ◽  
Listed Firms ◽  
Chinese Listed Companies ◽  
Corporate Social ◽  
The Impact

This paper explores the impact of corporate social responsibility (CSR) and financial distress on corporate financial performance (CFP) in Chinese listed companies of the manufacturing industry. Covering a total of 1445 manufacturing observations from 2013 to 2018 by matching the China Stock Market & Accounting Research Database (CSMAR) and Ranking CSR Ratings (RKS) database and regression models, we find that CSR has a significant positive impact on CFP, and the relationship is more pronounced for firms that are more stable. Further, the win-win relationship of CSR and CFP is also stronger in state-owned enterprises (SOEs). These empirical results suggest that enterprises should actively embrace CSR in response to the call of the country. At the same time, corporate stability should be increased to enhance the role of CSR in promoting CFP. We provide a quantitative analysis of the CSR, CFP, and financial distress of listed firms, and help to alleviate managers’ concern of CSR fulfillment and risk control.

PREDICTING FINANCIAL DISTRESS OF CHINESE LISTED COMPANIES USING ROUGH SET THEORY AND SUPPORT VECTOR MACHINE

2011 ◽  
Vol 28 (01) ◽  
pp. 95-109 ◽  
Author(s):  
YU CAO ◽  
GUANGYU WAN ◽  
FUQIANG WANG
Keyword(s):  
Support Vector Machine ◽  
Set Theory ◽  
Rough Set ◽  
Financial Distress ◽  
Rough Set Theory ◽  
Listed Companies ◽  
Support Vector ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Chinese Listed Companies

Effectively predicting corporate financial distress is an important and challenging issue for companies. The research aims at predicting financial distress using the integrated model of rough set theory (RST) and support vector machine (SVM), in order to find a better early warning method and enhance the prediction accuracy. After several comparative experiments with the dataset of Chinese listed companies, rough set theory is proved to be an effective approach for reducing redundant information. Our results indicate that the SVM performs better than the BPNN when they are used for corporate financial distress prediction.

Financial Distress Prediction of Chinese-Listed Companies Based on PCA and WNNs

2013 ◽  
pp. 212-220
Author(s):  
Xiu Xin ◽  
Xiaoyi Xiong
Keyword(s):  
Financial Distress ◽  
Transfer Functions ◽  
Back Propagation ◽  
Principal Component ◽  
Listed Companies ◽  
Quantitative Prediction ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Chinese Listed Companies ◽  
Base Functions

The operating status of an enterprise is disclosed periodically in a financial statement. Financial distress prediction is important for business bankruptcy prevention, and various quantitative prediction methods based on financial ratios have been proposed. This paper presents a financial distress prediction model based on wavelet neural networks (WNNs). The transfer functions of the neurons in WNNs are wavelet base functions which are determined by dilation and translation factors. Back propagation algorithm was used to train the WNNs. Principal component analysis (PCA) method was used to reduce the dimension of the inputs of the WNNs. Multiple discriminate analysis (MDA), Logit, Probit, and WNNs were employed to a dataset selected from Chinese-listed companies. The results demonstrate that the proposed WNNs-based model performs well in comparison with the other three models.

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