English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In the fierce market competition, companies are constantly facing the threat of falling into GFC. A global financial crisis refers to a crisis in global financial assets or financial institutions or financial markets. However, the threat of a global financial crisis (GFC) is not helpless, but can be predicted in advance. Therefore, building a GFC prediction model is of great significance to the development of the company. This article mainly studies the GFC prediction model of listed companies based on statistics and AI methods. This paper chooses to determine the number of training samples and test samples as 40 and 16 respectively, that is, 8 companies are randomly selected as test samples from financial health companies and GFC companies respectively, and the remaining 40 become training samples. According to the primary selection of characteristic indicators, this paper adopts the frequency statistics method, that is, the higher frequency is selected through the previous research, and the indicator selection is made on this basis. This article will use the Kolmogorov–Smimov (K-S test) goodness-of-fit test method. Each of the early warning indicators selected in this article should be able to distinguish between GFC and non-GFC companies, so the selection should be made by indicators one by one. Bring the indicators of each year into the factor function formula obtained by factor analysis, and get a new variable group. Then SPSS16.0 was used for binomial logistic regression analysis for each year. This article uses KMO and Bartlett identification. The assumption of the sphericity test of the Bartlett test is that the correlation coefficient matrix is an identity matrix, and statistics are obtained according to the matrix formula of the correlation coefficient matrix. The prediction accuracy of the nonlinear combination discriminant method has been improved in the first three years of the GFC, and in the year (t − 3), which is a little far away from the crisis time, the accuracy rate has reached 83%. The results show that the combination of statistics and AI has a significant effect on improving the prediction accuracy of the GFC prediction model of listed companies.

Financial Crisis Prediction Model of Listed Companies Based on Statistics and AI