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- W3206889898 abstract "In recent years, with the rapid development of the new energy industry, the probability of enterprises facing financial risks is also increasing. Therefore, an effective early warning model is needed to help enterprises timely detect crises and adjust their marketing strategies. In this paper, 20 listed companies in the new energy industry were selected for comparison through principal component analysis and logistic regression model. KMO and Bartlett tests were used to determine whether the selected indexes were suitable for principal component analysis and then 5 principal components were extracted according to the cumulative contribution rate of the factors, therefore corresponding expressions were obtained. In logistic regression analysis, the regression model was obtained through -2 times logarithmic likelihood function value and Nagekerke test to know whether the model had goodness of fit. Finally, samples are selected to test the model. The results show that the logistic regression model has a better financial warning effect on new energy enterprises." @default.
- W3206889898 created "2021-10-25" @default.
- W3206889898 creator A5061975859 @default.
- W3206889898 date "2021-04-16" @default.
- W3206889898 modified "2023-09-23" @default.
- W3206889898 title "New Energy Enterprises Financial Risk Early Warning Model Based on the Comparison of Principal Component and Logistic Regression Model Analysis" @default.
- W3206889898 doi "https://doi.org/10.1145/3470716.3470737" @default.
- W3206889898 hasPublicationYear "2021" @default.
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