A corporate bankruptcy early warning framework based on principal component analysis to optimize the construction of financial index system in big data environment

Abstract

If we can find and take effective measures when the financial situation of the enterprise is just in crisis, we can make many enterprises avoid suffering greater losses and bankruptcy. This paper uses principal component analysis to construct and standardize the data matrix of enterprise financial early warning indicators, optimizes the indicator system and inputs it into the BP neural network model, and determines the risk of enterprise bankruptcy early warning according to the output of the model. The research results show that the financial early warning indicators selected in this paper can significantly distinguish between enterprises with and without bankruptcy risk, and the average accuracy of the model in early warning of enterprise bankruptcy risk is 92.5%. At the same time, the validity and reliability of the model are found to be high in individual cases. The model in this paper has good predictive ability, has the significance of guiding practice, and the results of the study well support the effectiveness of principal component analysis in the enterprise bankruptcy early warning model.