Financial Evaluation Algorithm Based on Enhanced Time Series Prediction

Volatility prediction is one of the important research directions in the field of asset pricing, its accuracy directly affects the effectiveness of investment strategy, and plays a key role in financial risk management. Therefore, the prediction of financial market volatility can help investors better understand market risks and opportunities, to formulate more effective investment strategies. Based on the current research situation at home and abroad, this paper analyzes relevant research questions and makes use of WIND real financial data and deep learning technology to forecast the volatility of Shanghai 50ETF. The effectiveness of the proposed method is verified by experimental design and comparison results.