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Duration 14 hours (2 days)
Course Outline
Introduction to LLMs in Finance
- The impact of AI and LLMs on financial analysis
- An overview of LLMs and their text analysis capabilities
- Case studies: Applying LLMs to financial forecasting and risk assessment
Processing Financial Data with LLMs
- Extracting financial indicators from unstructured data using LLMs
- Training LLMs on financial texts to perform sentiment analysis
- Correlating news sentiment with market movements
Developing Predictive Models with LLMs
- Architecting LLM-based models for stock price prediction
- Forecasting economic trends based on LLM-generated insights
- Backtesting models against historical financial data
Integrating LLMs into Investment Strategies
- Incorporating LLM analytics into quantitative trading workflows
- Using LLMs for portfolio optimization and risk management
- Communicating AI-driven insights to stakeholders
Hands-on Lab: Financial Market Prediction Project
- Configuring a financial data analysis environment with LLMs
- Building a market prediction model utilizing LLMs
- Evaluating model performance and iterating on improvements
Requirements
- A foundational understanding of financial markets and instruments
- Proficiency in Python programming and data analysis techniques
- Working knowledge of machine learning principles and statistical modeling
Target Audience
- Financial Analysts
- Data Scientists
- Investment Professionals
