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Duration 14 hours
Course Outline
Introduction to LLMs in Finance
- The impact of AI and LLMs on modern financial analysis
- An overview of LLM architectures and their text processing capabilities
- Case studies demonstrating LLMs in financial forecasting and risk assessment
Processing Financial Data with LLMs
- Extracting key financial indicators from unstructured data using LLMs
- Training LLMs on financial texts to perform accurate sentiment analysis
- Analyzing the correlation between news sentiment and market movements
Constructing Predictive Models with LLMs
- Designing LLM-enhanced models for stock price forecasting
- Projecting economic trends using insights generated by LLMs
- Backtesting models against historical financial datasets
Embedding LLMs in Investment Strategies
- Integrating LLM analytics into quantitative trading frameworks
- Leveraging LLMs for portfolio optimization and risk management
- Effectively communicating AI-driven insights to stakeholders
Practical Lab: Financial Market Prediction Project
- Configuring a financial data analysis environment with LLM support
- Building a functional market prediction model using LLMs
- Evaluating model performance and implementing iterative improvements
Requirements
- Foundational knowledge of financial markets and instruments
- Proficiency in Python programming and data analysis
- Working familiarity with machine learning principles and statistical modeling
Target Audience
- Financial analysts
- Data scientists
- Investment professionals
