Author(s)

Yuxin Sun, Shuhan Fan, Tianbo Zhu, Zhiyang Wang, Yi Zhang, Xingyu Ma, Yuqi Wu, Depei Zhang

Corresponding Author

Yuxin Sun

ABSTRACT

Finance is the core of the economy. Under the current complex economic structure, how the financial industry develops has become a hot issue that urgently needs to be resolved. Therefore, this article characterizes the development speed and quality of the financial industry through the change rate of total bank assets and the rate of non-performing loans of commercial banks. Therefore, this article uses the change rate of total bank assets and the rate of non-performing loans of commercial banks to characterize the development speed and quality of the financial industry, and the forecast of the development trend of the financial industry is studied. Based on this, this paper uses particle swarm algorithm and BP neural network to predict the development trend of the financial industry, and optimizes the weights and thresholds of BP neural network through particle swarm algorithm, and the prediction model of BP neural network optimized by particle swarm optimization is established. The simulation results show that the prediction accuracy of the prediction model optimized by the particle swarm algorithm has been greatly improved to meet the requirements of prediction accuracy. This method has very important reference value for my country's financial industry.

KEYWORDS

Particle swarm algorithm, BP neural network, financial industry development trend, prediction model