This unit explores how generative AI, especially conversational models like ChatGPT, can be applied to portfolio management and financial forecasting through effective prompt design and personalization. Students will investigate trading strategies, business use cases, regulatory and implementation challenges, and the future prospects of AI in finance to prepare for a culminating applied project.
Learning Objectives
- Analyze portfolio management and trading strategies using AI-driven forecasting, identifying how model outputs and prompt design affect risk-adjusted performance
- Apply prompt-engineering techniques to create reproducible financial analyses and trading signals, documenting inputs, prompt variants, and evaluation metrics
- Demonstrate personalization of AI-generated recommendations by adjusting prompts and parameters to align with specific client risk profiles, investment goals, and compliance requirements
- Evaluate regulatory, ethical, and implementation challenges of deploying AI in financial forecasting and recommend governance, validation, and mitigation strategies for enterprise adoption
Curriculum
- 1 Section
- 10 Lessons
- 30 hours
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- Unit 3 — AI-Driven Portfolio Strategies and Prompts10
- 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
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