Quantitative Analysis Model of Financial Risk Value Based on Neural Network Algorithm

Traditional methods regard risk as static and ignore the influence of time factors. However, the risks of financial markets are dynamic and affected by market conditions and the economic environment. This leads to inaccurate estimates and forecasts of risks, and the inability to capture the dynamic evolution of risks. In this study, a quantitative analysis model of financial risk value based on neural network algorithm is used. Neural network algorithms have strong nonlinear modeling and pattern recognition capabilities, and can capture complex relationships and timing dependencies in financial markets. In the research, the article selects the appropriate neural network structure and parameter settings, and uses historical financial data for model training and verification. The experimental results show that the prediction accuracy rate of this method is as high as 95%, and the quantitative analysis model of financial risk value based on neural network algorithm performs well in terms of prediction accuracy.