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In recent years, with the continuous advancement of global financial integration, the Internet financial market has gained huge development space; however, the Internet financial market itself is unstable, and the advancement of globalization has caused increasingly frequent fluctuations (Song et al., 2020). Both companies and financial institutions, as well as ordinary investors, are threatened by financial risks in the financial market (Zhang, 2020). The financial risks will have a great impact on the development and operation of both companies and financial institutions, and affect the survival of individuals. Besides, the increasing financial risks and the ultimate explosion will also bring an impact on the national and even the global financial market, thereby seriously endangering the stability and health of economic growth (Tang et al., 2019). what’s more, the diverse investment portfolios in the financial market can diversify the financial risk management, leading to the decline in financial risks.
Internationally, there are many researches on the risk management of Internet financial market. Some scholars described VaR, a new indicator to describe the risk of financial market, and applied it to the risk management of China’s financial market. The feasibility of VaR was verified according to the actual situation (Almahdi & Yang, 2019). Also, some scholars compared VaR and CVaR, deeming that CVaR can more fully reveal financial risks; applied CVaR to optimize the investment portfolio (Gao & Su, 2020). As a financial risk management tool, the CVaR-based portfolio model has difficulty in solving when its dimension is relatively high (Zhou et al., 2019). Therefore, some scholars applied artificial intelligence (AI) algorithms such as genetic algorithm to solve this model, and this global random search algorithm shows great advantages in dealing with such problems (Fischer & Krauss, 2018). Some scholars applied Particle Swarm Optimization (PSO) to solve the model based on deep learning, so as to establish a more optimized investment portfolio. In order to obtain higher solving efficiency, some scholars enhanced the intelligent algorithm based on deep learning (Bai et al., 2021). Although these researches put forward the portfolio model to deal with the financial risks, the problem of solving the model still needs to be addressed accordingly.
To sum up, firstly, a method for establishing a portfolio model was proposed based on the risk management theory of the financial market in this paper. Then, a prediction model for CVaR was established based on the CNN, and the improved particle swarm algorithm was employed to solve the model. The actual data analysis was implemented to prove the feasibility of CVaR prediction model based on deep learning and PSO algorithm in the financial risk management. Moreover, the proposed algorithm was compared with the traditional particle swarm algorithm. Therefore, this research provides an important reference for the adoption of deep learning methods in the financial risk management.