Financial Market Sentiment Prediction Technology and Application Based on Deep Learning Model.

Area of Science:

• Artificial Intelligence
• Computational Finance
• Natural Language Processing

Background:

• Reinforcement learning (RL) is crucial for studying decision-making strategies.
• Combining RL with sentiment analysis is a promising research area but faces accuracy challenges.
• Existing methods lack effectiveness in real-world applications.

Purpose of the Study:

• To address the limitations of current RL and sentiment analysis integration.
• To develop an optimized deep reinforcement learning (DRL) investment trading strategy.
• To enhance accuracy and application effects in financial markets.

Main Methods:

• Developed a sentiment analysis method incorporating knowledge graphs tailored for the stock market.
• Implemented a DRL algorithm combining sentiment analysis and knowledge graphs.
• Simulated stock market data for experimental analysis and comparison.

Main Results:

• The proposed DRL system demonstrated superior performance over traditional RL algorithms.
• The integration of sentiment analysis and knowledge graphs led to improved trading gains.
• The algorithm showed significant practical application value in the stock trading domain.

Conclusions:

• The novel DRL approach effectively enhances investment strategies.
• Integrating knowledge graphs with sentiment analysis in DRL offers a robust solution for financial trading.
• This research provides a foundation for more advanced AI-driven trading systems.