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Course Outline
Introduction to LLMs in Finance
- The role of AI and LLMs in modern financial analysis
- An overview of LLM capabilities in text analysis
- 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 observed market movements
Developing Predictive Models with LLMs
- Designing LLM-based architectures for stock price prediction
- Forecasting economic trends using insights generated by LLMs
- Backtesting models against historical financial data
Integrating LLMs into Investment Strategies
- Incorporating LLM analytics into quantitative trading strategies
- Utilising LLMs for portfolio optimisation and risk management
- Communicating AI-driven insights effectively to stakeholders
Hands-on Lab: Financial Market Prediction Project
- Setting up a financial data analysis environment using LLMs
- Developing a comprehensive market prediction model with LLMs
- Evaluating model performance and implementing refinements
Requirements
- A foundational understanding of financial markets and financial instruments
- Proficiency in Python programming and data analysis techniques
- Knowledge of machine learning concepts and statistical modelling
Target Audience
- Financial analysts
- Data scientists
- Investment professionals
14 Hours
