Course 2026-2027 a.y.

Classes: 26 (I sem.)

Instructors:
Class 26: HANNES WAGNER

This course introduces students to the sustainability issues and ethical challenges facing today's financial industry. It leverages the joint expertise of the class to help understand the entire sustainable finance universe, from shareholder engagement to constructing innovation-driven ESG portfolios. Through lectures, data-driven workshops, hands-on team projects, and real case studies of innovative financial players, students are exposed to the methods, contexts, and insights that enable providers and seekers of financing to create value. Given the rapid evolution of artificial intelligence as a working tool in finance and sustainability practice, the course is structured to develop genuine analytical capability under conditions where AI assistance is freely available. Students learn to use AI productively as a thinking partner while building skills that AI alone cannot deliver.

Module 1 — Introduction to sustainable finance: Principles of sustainable finance; measuring sustainability; ESG data and its challenges.

Module 2 — Finance and sustainability frameworks: Investment tools; portfolio construction; climate change and environmental engineering; regulation and disclosure.

Module 3 — Governance and activism: Corporate governance; stewardship, activism and engagement; environmental activism.

Module 4 — Public equity markets: ESG portfolios and performance; pricing of sustainability risks; can investors do well by doing good?

Module 5 — Private equity and impact investing: Private equity and measuring impact; workshops and live presentations of team projects.

Module 6 — Fixed income: Green lending and green bonds: Fixed income markets and sustainability; the green bond market; green lending

 

Content Summary – detailed session schedule

Module 1 — Introduction to Sustainable Finance

Class 1 — Kick-off and introduction. Course logistics, team formation, live auction for case seats. AI policy framing.

Class 2 — Sustainability and profitability: the Fishbanks simulation. Multiplayer simulation exploring the sustainability–profitability tension.

Class 3 — Case: Driving Sustainability at Bloomberg L.P., 2012.

Class 4 — Measuring sustainability. Introduction to ESG data, LSEG/WRDS access, contrasting database output and large-language-model output.

Class 5 — ESG data and analytics bootcamp. Hands-on data work.

Module 2 — Finance and Sustainable Frameworks

Class 6 — Understanding investments. Portfolio tools and concepts.

Class 7 — Case: Asset Allocation at the Cook County Pension Fund, 2021. Run laptops-closed: students prepare their numerical analysis in advance and bring it on paper.

Class 8 — Climate change and environmental engineering. Climate science foundations for finance.

Class 9 — Regulation and disclosure. The regulatory architecture for sustainability disclosure.

Module 3 — Governance and Activism

Class 10 — Corporate governance.

Class 11 — Stewardship, activism, and engagement.

Class 12 — Case: Genzyme and Relational Investors, 2012.

Class 13 — Case: Engine No. 1 and ExxonMobil, 2023.

Module 4 — Public Equity Markets

Class 14 — ESG portfolios. Construction and pricing of ESG portfolios.

Class 15 — Performance of ESG. Structured debate on the empirical evidence for and against ESG outperformance.

Class 16 — Case: The Norwegian Government Pension Fund — Divestiture of Wal-Mart Stores Inc., 2011.

Module 5 — Private Equity and Impact Investing

Class 17 — Private equity and measuring impact. Impact measurement frameworks and the structure of impact funds.

Class 18 — Project workshop with structured milestone check-ins.

Classes 19, 20, 21 — Team presentations.

Module 6 — Fixed Income: Green Lending and Green Bonds

Class 22 — Fixed income. Fixed income markets and sustainability.

Class 23 — Green bonds. Guest speaker from the sustainable-finance industry.

Class 24 — Case: Does Sustainability Pay? Barry Callebaut's Sustainability Improvement Loan, 2020. Closing session of the course.

At the end of the course student will be able to...

•         Describe the principal building blocks of sustainable finance, including ESG data, ESG portfolio construction, sustainability-linked debt, green bonds, and impact investing.

•         Explain the institutional roles of asset owners, asset managers, activists, and regulators in shaping sustainability outcomes through capital markets.

•         Identify the regulatory architecture governing sustainability disclosure in the EU, UK, and US and characterise its evolution.

At the end of the course student will be able to...

•         Apply portfolio construction and asset allocation tools to evaluate ESG investment decisions and quantify their financial and sustainability trade-offs.

•         Use ESG data sources (LSEG, MSCI, others) to construct, compare, and critique ESG profiles of real companies and portfolios.

•         Evaluate sustainability-linked financial instruments and impact-investing measurement frameworks against the criteria of credibility, additionality, and greenwashing risk.

•         Take and defend reasoned positions on contested questions in sustainable finance under live discussion conditions.

•         Distinguish AI-assisted analytical work that genuinely advances understanding from AI output that substitutes for it.

•         Communicate financial and sustainability analysis effectively in live, time-constrained settings to mixed audiences of finance and non-finance peers.

•         Use AI tools productively as a thinking partner while retaining intellectual ownership of analytical conclusions.

• Lectures
• Guest speaker's talks (in class or in distance)
• Practical Exercises
• Individual works / Assignments
• Collaborative Works / Assignments
• Interaction/Gamification

The course adopts multiple learning methods, deliberately mixed across the 24 sessions:

• Synchronous lectures.

Used selectively for foundational content. Lecture share is deliberately constrained in favour of active learning.

• Teams

Teams. Students form their own teams of 4–5 in Class 1 and keep them for the semester. Team composition is supported by a class Padlet where students post their background and skills before the course starts.

• Case discussions.

Each of the six case sessions is built around three team seats, allocated through a live auction in Class 1. First Chair presents the recommendation and the supporting analysis. Challenge prepares a structured rebuttal and puts one question to the First Chair. The third seat is named for each case — The Verdict, The Counterparty, What Happened Next, The Live Analogue — and either judges between the two arguments or brings outside evidence to test them. The remainder of the session is open discussion led by the instructor.

All students prepare every case, whether or not their team holds a seat. Individual cold-calls, drawn from the preparation questions, happen in every case session.

Class 7 (Cook County Pension Fund) is run laptops-closed: students perform their numerical analysis in advance, with any tools including AI, and bring their work to class on paper, reproducing the discipline of a live investment committee.

• Simulations and live data exercises

The Fishbanks multiplayer simulation; live ESG data work in LSEG/WRDS with an AI-comparison exercise; live portfolio construction in Excel; and structured live exercises.

• Structured project workshops

Class 18 is organised as a milestone review. The instructor team conducts a structured check-in with each team.

• Live team presentations

Classes 19, 20 and 21. Each team presents with Q&A from the audience and the instructor. A structured peer evaluation runs during these sessions.

 

• Guest speaker.

Classes 9 and 23 — Guest speakers from sustainable finance or capital markets.

AI policy.

• The use of AI tools (large language models, code assistants, data tools) is encouraged throughout the course for preparation, learning, data work, and project research. AI tools may not be used during in-class quizzes, the final exam, or as substitutes for the live presentations in case sessions and team project presentations. The AI policy is presented in detail in Class 1 and is reproduced in the course materials on Blackboard.

 

	Continuous assessment	Partial exams	General exam
Written individual exam (traditional/online)			x
Collaborative Works / Assignment (report, exercise, presentation, project work etc.)	x
Active class participation (virtual, attendance)	x
Peer evaluation	x

Course assessment is identical for attending and non-attending students. The grade reflects performance across four components, with floating weights determined as follows:

• 0–10% Participation
• 0–40% Team cases (15%), quizzes (10%) and project (15%)
• 50–100% Final exam

Participation.

Participation is measured by the contribution each student makes to the learning of others. Peers score one another from 1 to 5 across three rounds — two mid-semester rounds for feedback only, and a final round that counts — with each student's scores constrained to average 3 across the class, so no one can lift or sink everyone at once. Peer scores are an input, not the grade itself: the instructor assigns participation grades. Individual scores stay confidential and only aggregate results are published. Full guidelines are distributed in class. There is one participation grade. The weight is 10% if better than the final exam grade, and 0% otherwise.

Cases.

Cases are designed to give students a sense of solving allegedly real problems facing a manager. Each team is graded on the case seat it is allocated. Seats are allocated in a bidding process in the first class. There is one case grade per team. The weight is 15% if better than the final exam grade, and 0% otherwise.

Quizzes.

Quizzes synthesize the course material and assess students' ability to apply analytical tools and institutional knowledge. They are delivered in class, on paper, and worked on in teams. There is one quiz grade per team. The weight is 10% if better than the final exam grade, and 0% otherwise.

Project.

Teams work on self-chosen impact investing projects. Projects require instructor approval. Teams present during dedicated sessions in Module 5. There is one project grade per team. The weight is 15% if better than the final exam grade, and 0% otherwise.

Final exam.

The final exam covers all course material and is held in person under proctored conditions. The precise format is discussed during the course. There is one final exam grade. The weight of the exam grade depends on the quality of the other components and ranges from 50% to 100%.

Your non-exam grades — participation, case, quizzes and project — are held until you register for the exam, and are then applied to that sitting. They can be used once. If you withdraw from that exam, do not appear, or do not pass, those grades are used up and do not carry over to a later attempt. In practice this means one thing: register for the sitting you intend to take.

The course pack contains for each class a customized set of teaching materials. A set of 22 Launchpads covers for each class the i) Required materials ii) Deliverables iii) Suggested Pre-Class Preparation iv) Preparation Questions.

• Required materials include academic articles, lecture notes, case studies and case supplements, data files, code samples, and other electronic resources.
• Required materials are delivered either digitally via Blackboard Ultra, Microsoft Teams, WRDS or LSEG, or in paper format during class.