This unit examines the intersection of portfolio management, trading strategies, and generative AI tools, focusing on prompt design, personalization, and business deployment. Students will explore practical uses of ChatGPT in financial workflows, assess regulatory and implementation challenges, and consider future prospects for AI in forecasting and investment analytics.
Learning Objectives
- Analyze the strengths and limitations of traditional versus AI-enhanced portfolio management and trading strategies by comparing risk-adjusted performance metrics and scenario outcomes
- Apply prompt engineering and personalization techniques to design, test, and refine AI prompts for financial forecasting models, documenting input variations and resulting performance changes
- Demonstrate integration of ChatGPT or similar conversational AI into a business application (e.g., research synthesis, client reporting, trade idea generation) via a working prototype or documented workflow
- Evaluate regulatory, ethical, and operational challenges associated with deploying AI in financial services and propose concrete mitigation strategies aligned with compliance and risk-management practices
Curriculum
- 1 Section
- 10 Lessons
- 30 hours
Expand all sectionsCollapse all sections
- Unit 3 — AI-Driven Portfolio Strategies10
- 1.1Portfolio Management and Trading Strategies20 mins
- 1.2What Is a Prompt?20 mins
- 1.3What We Learned20 mins
- 1.4Personalization20 mins
- 1.5The Future of AI in Financial Forecasting20 mins
- 1.6Summary20 mins
- 1.7ChatGPT in Business Applications20 mins
- 1.8AI Regulatory and Implementation Challenges20 mins
- 1.9The Prospects of AI in Finance20 mins
- 1.10Coming Up Next20 mins
Instructor
Free
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