Application of Sinusoidal Function in Financial Crisis Early Warning and Detection System

• Published in: Journal of applied mathematics Vol. 2025; no. 1
• Main Author: Wang, Xueyin
• Format: Journal Article
• Language: English
• Published: New York John Wiley & Sons, Inc 01.01.2025; Wiley
• Subjects: Algorithms; Artificial intelligence; Bank failures; Computer simulation; Deep learning; Detectors; Early warning systems; Economic crisis; Economic policy; Financial institutions; Financial systems; Globalization; International finance; Liquidity; Machine learning; Neural networks; Phase transitions; Prevention; Rating services; Securities markets; China
• ISSN: 1110-757X; 1687-0042
• DOI: 10.1155/jama/7790305

With the deepening of economic and financial globalization and integration, the possibility of financial crisis in a country or a region has obviously increased. Therefore, it is particularly important to prevent and resolve the financial crisis, especially the regional financial crisis. Therefore, this paper studies the application of sine signal function in financial crisis early warning and detection system. According to the principle of “different frequencies are uncorrelated,” the derivation process of a single sinusoidal signal with noise in the crisis warning period is also applicable to the case of multiple sinusoidal signals, so the program can also be used to detect multiple sinusoidal signals in the crisis warning period at the same time. The research shows that the approximate frequency of the frequency to be measured is estimated before measurement, the initial driving force frequency is set in the range of 1–2 times of the estimated value, and the program is executed to search the extreme value of the variance of the output. The frequency value of the driving force corresponding to the extreme value of variance is the frequency value of the signal to be measured, and the simulation results show that the accuracy is about 2%. Therefore, through the application of sine signal function in the financial crisis early warning and detection system, it can provide forward‐looking suggestions for the economic policymakers of various countries, so as to take effective preventive measures in advance for the possible international financial crisis in the future. While the prediction accuracy is not absolute, the findings provide meaningful insights.