Enterprise Financial Distress Prediction Based on BPNN: A Case Study of Chinese Listed Companies

2013 ◽  
Vol 12 (23) ◽  
pp. 7684-7690
Author(s):  
Ying Zhang ◽  
Chong Wu ◽  
Xin-ying Zhang
Keyword(s):  
Financial Distress ◽  
Listed Companies ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Chinese Listed Companies

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.

scholarly journals FINANCIAL DISTRESS PREDICTION: A NOVEL DATA SEGMENTATION RESEARCH ON CHINESE LISTED COMPANIES

2021 ◽  
Vol 0 (0) ◽  
pp. 1-34
Author(s):  
Fang-Jun Zhu ◽  
Lu-Juan Zhou ◽  
Mi Zhou ◽  
Feng Pei
Keyword(s):  
Time Series ◽  
Financial Distress ◽  
Listed Companies ◽  
Classification Model ◽  
Destructive Interference ◽  
Financial Indicators ◽  
Financial Distress Prediction ◽  
Data Segmentation ◽  
Distress Prediction ◽  
Chinese Listed Companies

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.

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

2011 ◽  
Vol 3 (4) ◽  
pp. 6-14
Author(s):  
Xiu Xin ◽  
Xiaoyi Xiong
Keyword(s):  
Financial Distress ◽  
Transfer Functions ◽  
Back Propagation ◽  
Principal Component ◽  
Listed Companies ◽  
Quantitative Prediction ◽  
Back Propagation Algorithm ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Chinese Listed Companies

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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