The analysis of financial market risk based on machine learning and particle swarm optimization algorithm

• Published in: EURASIP journal on wireless communications and networking Vol. 2022; no. 1; pp. 1 - 17
• Main Authors: Liu, Tao, Yu, Zhongyang
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 02.04.2022; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Artificial intelligence; Blockchain; Clustering; Clustering method; Communications Engineering; Cryptography; Decision trees; Engineering; Financial market; Information Systems Applications (incl.Internet); Interdisciplinary subjects; International finance; Machine learning; Machine Learning for Trust; Mathematical models; Networks; Optimization algorithms; Particle swarm optimization; Random forest; Regional development; Securities markets; Security and Privacy in Computing and Communications; Signal,Image and Speech Processing; Supply chains; Wireless networks; Random forest; Financial market; Clustering method; Machine learning; Blockchain
• ISSN: 1687-1499; 1687-1472; 1687-1499
• DOI: 10.1186/s13638-022-02117-3

The financial industry is a key to promoting the development of the national economy, and the risk it takes is also the largest hidden risk in the financial market. Therefore, the risk existing in the current financial market should be deeply explored under blockchain technology (BT) to ensure the functions of financial markets. The risk of financial markets is analyzed using machine learning (ML) and random forest (RF). First, the clustering method is introduced, and an example is given to illustrate the RF classification model. The collected data sets are divided into test sets and training sets, the corresponding rules are formulated and generated, and the branches of the decision tree (DT) are constructed according to the optimization principle. Finally, the steps of constructing the branches of DT are repeated until they are not continued. The results show that the three major industries of the regional economy account for 3.5%, 51.8%, 3.2%, 3.4%, and 3.8% of the regional GDP, respectively, the secondary industry makes up 44.5%, 43%, 45.1%, 44.8%, and 43.6%, respectively, and the tertiary industry occupies 20%, 3.7%, 52.3%, 52.9%, 54%, and 54.6%, respectively. This shows that with the development of the industrial structure under BT, the economic subject gradually shifts from the primary industry to the tertiary industry; BT can improve the efficiency of the financial industry and reduce operating costs and dependence on media. Meanwhile, the financial features of BT can provide a good platform for business expansion. The application of BT to the supply chain gives a theoretical reference for promoting the synergy between companies.