Research on Financial Risk Crisis Prediction of Listed Companies Based on IWOA-BP Neural Network

Sha Li,
Xuan Chen,

Abstract


To avoid the risk brought by the financial crisis, the Improved Whale Optimization Algorithm-Back Propagation neural network (IWOA-BP) financial crisis early warning model is proposed. This paper selects the data from financial statements of some of the listed Chinese manufacturing companies from 2015-2019 as the research sample. First, the financial data of enterprises are screened by principal component analysis, and the early warning model is constructed from the financial and nonfinancial factors of six indicators: solvency, operating capacity, profitability, development capacity, cash flow and risk level factors. Second, the Whale Optimization Algorithm is optimized by the chaos strategy, as well as by the dynamic weight and sine cosine algorithm. Finally, the improved Whale Algorithm is optimized for BP neural network parameters. In the simulation experiments, the performance of the improved whale optimization algorithm is substantially improved. In addition, in the empirical analysis, compared to the prediction model with other algorithms, the prediction model of this paper has better results in terms of prediction accuracy.

Keywords


Financial risk, Crisis prediction, Whale optimization algorithm, BP neural network

Citation Format:
Sha Li, Xuan Chen, "Research on Financial Risk Crisis Prediction of Listed Companies Based on IWOA-BP Neural Network," Journal of Internet Technology, vol. 23, no. 5 , pp. 955-965, Sep. 2022.

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