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Duration 14 hours
Course Outline
Introduction to LLMs in Finance
- The evolving role of AI and LLMs in financial analysis.
- An overview of LLM architectures and their text analysis capabilities.
- Case studies exploring LLMs in financial forecasting and risk assessment.
LLMs for Financial Data Processing
- 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 volatility.
Building Predictive Models with LLMs
- Designing LLM-based frameworks for stock price prediction.
- Forecasting economic trends by leveraging LLM-generated insights.
- Backtesting model efficacy against historical financial datasets.
Integrating LLMs into Investment Strategies
- Embedding LLM analytics into quantitative trading strategies.
- Applying LLMs for portfolio optimization and comprehensive risk management.
- Effectively communicating AI-driven insights to key stakeholders.
Hands-on Lab: Financial Market Prediction Project
- Establishing a robust financial data analysis environment using LLMs.
- Developing a functional market prediction model with LLM support.
- Evaluating model performance and implementing iterative improvements.
Requirements
- A solid foundational understanding of financial markets and instruments.
- Practical experience with Python programming and data analysis techniques.
- Familiarity with core machine learning concepts and statistical modeling.
Target Audience
- Financial analysts.
- Data scientists.
- Investment professionals.
