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Course Outline
Introduction to LLMs in Finance
- The role of AI and LLMs in financial analysis
- Overview of LLMs and their capabilities in text analysis
- Case studies: LLMs in financial forecasting and risk assessment
LLMs for Financial Data Processing
- Extracting financial indicators from unstructured data with LLMs
- Training LLMs on financial texts for sentiment analysis
- Correlating news sentiment with market movements
Building Predictive Models with LLMs
- Designing LLM-based models for stock price prediction
- Forecasting economic trends using LLM-generated insights
- Backtesting models with historical financial data
Integrating LLMs into Investment Strategies
- Incorporating LLM analytics into quantitative trading
- LLMs for portfolio optimization and risk management
- Communicating AI-driven insights to stakeholders
Hands-on Lab: Financial Market Prediction Project
- Setting up a financial data analysis environment with LLMs
- Developing a market prediction model using LLMs
- Evaluating model performance and making improvements
Summary and Next Steps
Requirements
- A basic understanding of financial markets and instruments
- Experience with Python programming and data analysis
- Familiarity with machine learning concepts and statistical models
Audience
- Financial analysts
- Data scientists
- Investment professionals
14 Hours
