Title: Research on e-commerce neural network financial accounting crisis early warning model combined with partial least squares

Authors: Xiaoyang Meng

Addresses: Accounting Institute, Jiaozuo University, Jiaozuo, 454000, China

Abstract: The study establishes a variable system based on the financial accounting crisis early warning theory, and uses partial least squares method to screen variables in order to accurately predict various incentives for the financial crisis in the actual operation of enterprises in the e-commerce industry. According to the findings of the experiment, when the quantity of hidden layer nodes in L-1~3 years is 9, 10 and 11 respectively, the convergence rate of the model can reach the best state. In the prediction of 2020 and 2021, the accuracy rate of L-2 and L-3 is less than 90%, and L-1 has an accuracy rate of more than 90%. In conclusion, the PLS-BP financial crisis early warning model developed and studied can be highly accurate and useful, and it can quickly identify financial crisis signals for businesses in the e-commerce industry and develop efficient measures.

Keywords: partial least squares; PLS; BP neural network; financial crisis; logistic regression; online retailers; variable indicators.

DOI: 10.1504/IJCSYSE.2023.132913

International Journal of Computational Systems Engineering, 2023 Vol.7 No.2/3/4, pp.96 - 105

Received: 30 Nov 2022
Accepted: 13 Feb 2023
Published online: 16 Aug 2023 *

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