AI and Predictive Trading

This unit explores the application of machine learning and deep learning to financial markets, focusing on stock trading strategies, new data sources, and practical AI tools. Students examine model performance and limitations, practice building prototype predictive models, and use generative AI to automate analysis and reporting, building on foundational finance concepts and preparing for advanced model deployment and evaluation.

Learning Objectives

• Analyze historical stock market data and model outputs to assess the performance and risk-adjusted returns of predictive trading strategies using appropriate metrics.
• Apply machine learning and deep learning techniques to financial datasets, including feature engineering from alternative and real-time data sources, to develop prototype predictive models.
• Evaluate limitations, biases, and ethical considerations of AI models in financial analysis and recommend mitigation strategies to improve model robustness and regulatory compliance.
• Demonstrate the use of generative AI tools and data platforms (e.g., ChatGPT, alternative data feeds) to automate analysis, synthesize findings, and produce professional insights and reports.