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Establishment and Research on the Model of the Company's Financial Risk Warning Based on Principal Component Analysis and Logistic Regression
| Content Provider | Semantic Scholar |
|---|---|
| Author | Fu, Jingjing |
| Copyright Year | 2015 |
| Abstract | In this paper, we use the modern management theory to build a corporate financial risk early warning indicator system, while using the statistical method of principal component analysis and logistic regression analysis. Through the empirical research on listed companies' financial crisis situation, we established the company's financial early warning model to provide a scientific and feasible prediction method for enterprises to predict the financial crisis. Introduction With the development of global economic integration, enterprises in access to economic resources, it is also under great pressure of fierce market competition. Uncertainties due to the risk of a variety of transactions increases, companies are facing the business and financial risks have increased. In order to be in an invincible position in the competition, the enterprise healthy and orderly development, effectively avoid, prevent and control risks to become a top priority for enterprise development. Establish and improve financial risk early warning system is a powerful tool for the prevention of financial risks. An effective financial risk early warning system can predict signs of financial crisis, and to discover the cause of the deterioration of the financial situation, enabling operators to take preventive and control measures before the crisis hit, the crisis in the bud. It can be seen that financial risk warning plays a very important role for the development of enterprises. Therefore, the establishment of financial early warning model, the early diagnosis of the financial crisis and take appropriate measures to maintain health and safety and sustainable development of enterprises, it is very important [1]. The Establishment of Financial Early Warning Index System Modern financial management theory holds that measure a company's financial condition, depends on the company's solvency, asset management, profitability and ability to grow. Accordingly, the financial early warning indicators of enterprises can be divided into four categories: financial benefit status indicators, asset operating status indicators, solvency status indicators and development capacity status indicators. Including a number of financial ratios in each category indicators, these indicators reflect the financial position and operating results of the enterprise. The financial early warning system for corporate debt risk by asset management risk, earnings risk and growth monitoring of risk indicators constitute four categories [2]. The Establishment of Corporate Financial Risk Early Warning Model The Selection of the Study Sample. Sample data is mainly from CCER generally listed companies' financial database system. Limit the reliability of sources of information and data by the study sample all from listed companies. In this paper, the standard sample selection: (1)ST sample groups: the company between 2011 and 2012 since the financial situation but had to be specially treated as an exception ST sample group, and asked for two years before it was ST information. International Conference on Education Technology, Management and Humanities Science (ETMHS 2015) © 2015. The authors Published by Atlantis Press 1 (2)Non-ST sample group: According be "ST" industry classification and the corresponding year corresponding to the selected control sample, that sample group of non-ST (healthy companies). In order to meet the needs of this paper, we study a random sample of 100 listed companies as samples to build the model. Select the Model Variables. Based on the characteristics of listed companies selected eight variables X1: ROE (operating profit) X2: ROE X3: net profit growth X4: Current Ratio X5: cash flow debt ratio X6: asset-liability ratio X7: accounts receivable turnover ratio X8 turnover. The Establishment of Early Warning Models. Using SPSS statistical software, this 100 sample companies, according to the eight study variables identified above principal component analysis. The following results [3]: The information on the eight indicators, when selected five main cause of midnight, the information reaches 94.302%, which preserves the information of the original indicators. From Table 4, the rotated factor loading matrix, the first principal component FAC1_1 and X4: current ratio, X5: cash flow debt ratio, X6: the amount of factor loadings of these three indicators of asset-liability ratio is much larger than several other indicators, by these three variables to explain, reflecting the company's solvency. Therefore FAC1_1 represents corporate solvency main ingredient. FAC2_1 primarily by the variable X1: ROE (operating profit), X2: ROE two indicators to explain, these two indicators are all indicators of the profitability of the enterprise, so FAC2_1 on behalf of corporate profitability main ingredient. FAC3_1 main X8 by variables: turnover to explain, this indicator belongs operational capacity index, so FAC3_1 companies operating capacity on behalf of the main ingredients. FAC4_1 mainly by variable X7: accounts receivable turnover to explain this indicator belongs operational capacity indicators, it also represents the same FAC4_1 with FAC3_1 companies operating capacity main ingredient. FAC5_1 primarily by the variable X4: net profit growth to explain, this indicator also belong development capacity index, so FAC5_1 also represents the main component of business development capability. From the factor score coefficient matrix (Component Score Coef-ficient Matrix), the main component of the rotated factor expression can be written as: FAC1_1=X1*(-0.099)-0.079*X2+X3*0.062+0.472*X6+0.46*X7-0.216*X8-0.028*X9-0.051*X 10 FAC2_1=0.869*X1+0.386*X2-0.173*X3-0.133*X4-0.14*X5-0.301*X6-0.032*X7+0.163*X8 FAC3_1=-0.441*X1+0.262*X2-0.065*X3-0.05*X4-0.03*X5+0.132*X6-0.044*X7-0.015*X8 FAC4_1=-0.071*X1-0.062*X2+0.071*X3-0.02*X4-0.032*X5-0.026*X6-0.009*X7-0.009*X8 FAC5_1=-0.067*X1-0.13*X2+1.099*X3+0.112*X4+0.063*X5+0.148*X6-0.133*X7-0.046*X8 Table 1 Variables in the equation B S.E. Wald df Sig. Exp(B) |
| File Format | PDF HTM / HTML |
| DOI | 10.2991/etmhs-15.2015.1 |
| Alternate Webpage(s) | https://download.atlantis-press.com/article/19197.pdf |
| Alternate Webpage(s) | https://doi.org/10.2991/etmhs-15.2015.1 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
