Associate Professor of Technology and Operations, Michael R. and Mary Kay Hallman Fellow
Innovations in Investment Technology: Artificial Intelligence
What You'll Learn
- Recognize the strengths and weaknesses of human financial advisors and investors.
- Explain the business model of robo/AI-advisors.
- Identify the relationship between identifiable firm characteristics and average returns.
- Build a diversified portfolio based on attitudes toward risk.
4 Modules
12 Hours
3 hrs per module (approx.)
Rating
About Innovations in Investment Technology: Artificial Intelligence
Explore the evolution of AI investing and online wealth management.
Investing and managing your wealth online has never been easier, but how does AI investing work and what are the challenges?
On this course, you’ll explore how technology has changed the way we invest money. You’ll consider the evolution of AI-driven online wealth management platforms, robo-advisors, and learn how they work and why they’re successful.
Moving from human-based data-driven investing strategies to neural networks, you’ll assess the ability of artificial intelligence to make investment decisions and discover the role of AI and machine learning in making trading decisions.
Skills You'll Gain
- AI Innovation
- Algorithmic Trading
- Artificial Intelligence
- Responsible AI
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to Innovations in Investment Technology: Artificial Intelligence, a course exploring how AI and machine learning are transforming investing and wealth management. You will examine robo-advisors, data-driven and neural network–based strategies, and the shift from human judgment to automated decision-making. Through practical frameworks, you’ll assess the strengths and limitations of AI investing, understand modern fintech business models, and build diversified portfolios aligned with risk preferences. This is the final course in the Financial Technology (Fintech) Innovations Specialization.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Introduction
- Video: Fintech Innovations: Series Map and Learning Goals
- Video: Fintech Innovations: Series Introduction
- Reading: Course Syllabus
- Reading: Help Us Learn More About You!
- Reading: Learning from a Case
- Discussion Prompt: Introductions
Module 2: Robo-Advising
- Reading: Case Reading: Wealth Management - A Case for Innovation?
- Video: Expected Returns, Standard Deviations, and Correlation
- Video: Building an Efficient Portfolio
- Video: Diversified Investments
- Video: Exchange Traded Funds
- Reading: Case Reading: Wealth Management and Robo-Advising
- Video: Robo-Advisors
- Video: How do Pure Advisors & Robo-Advisors actually work?
- Video: RoboAdvising: The Value Add of Robo Advisors
- Discussion Prompt: Case Activity: Create a Retirement Plan
Module 3: Stock Selection & Asset Management
- Reading: Case Reading: Is Active Management Right for Us?
- Video: Stock Selection: Fundamental Analysis: The Passive Benchmark
- Video: Stock Selection: Fundamental Analysis: Manager Performance
- Reading: Case Reading: Benchmarks vs. Signals
- Video: Stock Selection Screening
- Video: Stock Selection Screening: Discovering Signals and Data Issues
- Video: Stock Selection: Neural Networks
- Reading: Case Reading: Diving into Smart Betas
- Video: Stock Selection: Smart Beta: What is Smart Beta?
- Video: Stock Selection Smart Beta: Creating a Smart Beta Index
- Video: Stock Selection: Smart Beta Tilts
- Discussion Prompt: Case Activity: Evaluate Portfolio Performance
Module 4: Big Data
- Reading: Case Reading: The AI Revolution Comes to BAM
- Video: Demystifying the business of AI, machine learning, big data…
- Reading: Case Reading: Robots in the Stock Market
- Video: AI/ML in investment management: Current applications
- Video: AI/ML in investment management: Key value drivers and pitfalls
- Discussion Prompt: Case Discussion
- Reading: Course Feedback
- Reading: Keep Learning with Michigan Online
Grading Policy
Course materials are self-paced and remain open throughout the course. Learners must earn an overall grade of 80% to pass and receive a certificate. The course grade is based on three module quizzes worth 30% each (90%), and three discussion participations worth 3.33% each (10% total).
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Beginner Level
No prior experience required
