TOPICS IN QUANTATIVE FINANCE

The course examines how large language models are transforming investment management practice. It opens with a grounding in core AI techniques (prompting strategies, Retrieval-Augmented Generation, and agentic frameworks) before mapping these onto institutional investment and wealth management workflows.
The bulk of the course explores specific applications: extracting and monitoring risk factors from equity data and unstructured text using AI, building real-time news monitoring systems that score sentiment and flag portfolio-relevant events, and using LLMs to generate stress-test scenarios. Later sessions cover multi-agent systems for automated research and trading, including agent roles, guardrails, and human oversight, and how these techniques can enhance classical portfolio construction methods (risk parity, mean-variance optimization, Black-Litterman). The course includes guest lectures by industry practitioners discussing the industry's AI adoption trends.