AI-Enabled Investment Research and Risk Management

This culminating unit teaches students to synthesize research on ETFs, mutual funds, real estate investments, and related hard assets while integrating AI tools responsibly. Students will evaluate sources, test and select generative AI workflows, apply tax- and inflation-aware risk management techniques, and produce professional documentation and privacy safeguards for investment decisions.

Learning Objectives

• Analyze and compare at least five information sources (e.g., government agencies, brokerage robo-advisors, AI tool directories, user groups, and online courses) for credibility, bias, and applicability to ETF, mutual fund, and real estate investing, producing a ranked justification for each source chosen.
• Evaluate and select appropriate generative AI tools and prompting strategies by testing three tools (including at least one language variant), measuring accuracy, transparency of sourcing, and privacy protections, and documenting a recommended AI workflow.
• Apply tax-aware and inflation-sensitive risk-management strategies—such as stop-loss limits, diversification into ETFs/real estate ETFs/savings bonds/hard assets, and tax resource guidance—to create a hypothetical 12-month portfolio adjustment plan and model its projected impact under at least two Fed/political scenarios.
• Demonstrate professional compliance and recordkeeping by producing a documented paper trail that includes research notes, second-opinion summaries, cited regulatory/tax references, and a privacy checklist for AI use in investment decision-making.