Course Outline

Introduction to LLMs in Finance

  • The role of AI and LLMs in financial analysis
  • Overview of LLMs and their capabilities in text analysis
  • Case studies: LLMs in financial forecasting and risk assessment

LLMs for Financial Data Processing

  • Extracting financial indicators from unstructured data with LLMs
  • Training LLMs on financial texts for sentiment analysis
  • Correlating news sentiment with market movements

Building Predictive Models with LLMs

  • Designing LLM-based models for stock price prediction
  • Forecasting economic trends using LLM-generated insights
  • Backtesting models with historical financial data

Integrating LLMs into Investment Strategies

  • Incorporating LLM analytics into quantitative trading
  • LLMs for portfolio optimization and risk management
  • Communicating AI-driven insights to stakeholders

Hands-on Lab: Financial Market Prediction Project

  • Setting up a financial data analysis environment with LLMs
  • Developing a market prediction model using LLMs
  • Evaluating model performance and making improvements

Summary and Next Steps

Requirements

  • A basic understanding of financial markets and instruments
  • Experience with Python programming and data analysis
  • Familiarity with machine learning concepts and statistical models

Audience

  • Financial analysts
  • Data scientists
  • Investment professionals
 14 Hours

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