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Duration 14 hours
Course Outline
Introduction to LLMs in Finance
- The strategic role of AI and LLMs in modern financial analysis
- An overview of LLMs and their specific capabilities in text analysis
- Case studies: Applying LLMs to financial forecasting and risk assessment
Processing Financial Data with LLMs
- Extracting key financial indicators from unstructured data using LLMs
- Training LLMs on financial texts for advanced sentiment analysis
- Analyzing the correlation between news sentiment and market movements
Developing Predictive Models with LLMs
- Architecting LLM-based models for stock price prediction
- Forecasting economic trends by leveraging LLM-generated insights
- Backtesting models using historical financial data
Integrating LLMs into Investment Strategies
- Embedding LLM analytics into quantitative trading strategies
- Utilizing LLMs for portfolio optimization and robust risk management
- Effectively communicating AI-driven insights to stakeholders
Hands-on Lab: Financial Market Prediction Project
- Setting up a comprehensive financial data analysis environment with LLMs
- Developing a functional market prediction model using LLMs
- Evaluating model performance and implementing iterative improvements
Requirements
- Fundamental knowledge of financial markets and financial instruments
- Proficiency in Python programming and data analysis
- Working familiarity with machine learning concepts and statistical modeling
Target Audience
- Financial analysts
- Data scientists
- Investment professionals
