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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 using 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 insights generated by LLMs.
  • Backtesting models with historical financial data.

Integrating LLMs into Investment Strategies

  • Incorporating LLM analytics into quantitative trading.
  • Using 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 implementing improvements.

Summary and Next Steps

Requirements

  • A fundamental understanding of financial markets and instruments.
  • Proficiency in Python programming and data analysis.
  • Familiarity with machine learning concepts and statistical models.

Target Audience

  • Financial analysts.
  • Data scientists.
  • Investment professionals.
 14 Hours

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