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Duration 14 hours
Course Outline
Introduction to LLMs in the Financial Sector
- The impact of AI and LLMs on financial analysis
- An overview of LLM capabilities, specifically in text analytics
- Case studies: Applying LLMs to financial forecasting and risk evaluation
Processing Financial Data with LLMs
- Extracting key financial metrics from unstructured data using LLMs
- Training LLMs on financial texts to perform sentiment analysis
- Analyzing the correlation between news sentiment and market activity
Creating Predictive Models Using LLMs
- Architecting LLM-based models for stock price forecasting
- Predicting economic trends based on insights generated by LLMs
- Conducting backtesting using historical financial datasets
Embedding LLMs into Investment Strategies
- Incorporating LLM analytics into quantitative trading frameworks
- Utilizing 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 LLMs
- Developing a market prediction model leveraging LLMs
- Assessing model performance and implementing refinements
Requirements
- Fundamental knowledge of financial markets and instruments
- Proficiency in Python programming and data analysis
- Working knowledge of machine learning principles and statistical modeling
Target Audience
- Financial analysts
- Data scientists
- Investment professionals
