AI & Data in Investment Trading

This unit examines how machine learning and emerging data sources are applied to stock trading and other financial services tasks, and it introduces practical generative-AI applications. Students build on foundational financial concepts to evaluate model capabilities and limitations, and to practice deploying AI tools for research and client-facing workflows.

Learning Objectives

• Analyze algorithmic stock trading strategies and interpret trade outcomes and chapter review materials (Chapters 6–10) to identify key drivers of performance
• Apply core machine learning and deep learning concepts to design a basic financial prediction pipeline that incorporates traditional and new alternative data sources
• Evaluate limitations, biases, and ethical/regulatory implications of AI models and novel data in investment and financial planning contexts
• Demonstrate practical use of generative AI tools (e.g., ChatGPT) to support investment research, client communication, and automation, documenting methodology and validation steps