Course 2026-2027 a.y.

Classes: 31 (I sem.)

Instructors:
Class 31: HANNES WAGNER

Familiarity with basic finance concepts is useful but not required. The course is moderately quantitative, with most analytical tools introduced during the course itself. For students with a limited finance background: a Finance Foundations On-Ramp guide is distributed in Class 1. The guide covers ten foundational concepts — portfolio expected return and volatility, the Sharpe ratio, correlation, CAPM beta, pension fund liability structure, bond yield, present value, ESG score construction, and index methodology — with one suggested AI prompt per concept and a pointer to the session in which the concept becomes relevant. The guide is a navigation tool, not remedial material, and is not graded.

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.

The course is organised in six modules across 24 sessions:

• 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; social outcomes, regulation and disclosure.
• Module 3 — Governance and activism: Corporate governance; stewardship, activism, and engagement; investor demand for sustainability.
• Module 4 — Public equity markets: ESG portfolios and performance; pricing of sustainability risks; can investors do well by doing good?
• Module 5 — Team projects: Self-chosen project topics, workshops, and live presentations.
• Module 6 — Green lending, green bonds, impact investing: Sustainability-linked loans; the green bond market; impact investing and measurement.

 

Detailed session-by-session structure:

Module 1 — Introduction to sustainable finance

•         Class 01 — Kick-off and introduction. Course logistics, team formation, live auction for case roles and team project topics. AI policy framing.

•         Class 02 — Sustainability and profitability: the Fishbanks simulation. Multiplayer simulation exploring the sustainability–profitability tension.

•         Class 03 — Case: Driving Sustainability at Bloomberg L.P., 2012. Opener / discussant format.

•         Class 04 — Measuring sustainability. Introduction to ESG data, LSEG/WRDS access, contrasting database output and large-language-model output.

•         Class 05 — ESG data and analytics bootcamp. Hands-on data work.

Module 2 — Finance and sustainability frameworks

•         Class 06 — Understanding investments. Portfolio tools and concepts.

•         Class 07 — Case: Asset Allocation at the Cook County Pension Fund, 2021. Opener / discussant format.

•         Class 08 — Climate change and environmental engineering. Climate science foundations for finance.

•         Class 09 — Social outcomes, regulation, and disclosure. Guest speaker from the sustainable-finance industry.

Module 3 — Governance and activism

•         Class 10 — Corporate governance. Live ESG-table debate exercise on governance quality.

•         Class 11 — Case: Alibaba Goes Public (A), 2014. Opener / discussant format.

•         Class 12 — Stewardship, activism, and engagement. Live in-class activist-campaign mapping.

•         Class 13 — Case: Genzyme and Relational Investors, 2012. Opener / discussant format.

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. Opener / discussant format.

•         Class 17 — Case: MSCI Low Carbon Indices, 2020. Opener / discussant format.

Module 5 — Team projects

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

•         Class 19 — Project workshop with presentation-draft check-ins.

•         Class 20 — Team presentations.

•         Class 21 — Team presentations. (a third presentation slot may be added inside Module 6)

Module 6 — Green lending, green bonds, impact investing

•         Class 22 — Case: Does Sustainability Pay? Barry Callebaut's Sustainability Improvement Loan, 2020. Opener / discussant format.

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

•         Class 24 — Case: Acumen Fund Measurement in Impact Investing (A), 2009. Opener / discussant format; ceremonial close to 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.

Case discussions — opener / discussant format.

Each of the eight case sessions is structured around two team roles, both allocated through a live auction in Class 1:

•         Opener: presents the case recommendation and supporting analysis

•         Discussant: prepares a structured challenge to the opener's recommendation

The remainder of the session is structured as open discussion led by the instructor

Most case sessions follow this format with full devices permitted. Class 7 (Cook County Pension Fund) is run laptops-closed: students perform their numerical analysis at home (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 (Class 2); live ESG data work in LSEG/WRDS with an AI-comparison exercise (Classes 4–5); live portfolio construction in Excel; and structured live exercises in Classes 9 (regulation mapping), 10 (governance debate), 12 (activism mapping), and 15 (ESG performance debate).

Structured project workshops.

Classes 18 and 19 are organised as milestone reviews. The instructor team conducts a structured check-in with each team.

Live team presentations.

Classes 20–21 (and where needed Class 21 plus an additional slot in Module 6). Each team presents with Q&A from the audience and the instructor. 

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

•         0–20% Quizzes

•         0–30% Team cases and project

•         40–100% Final exam

Participation.

Measured by the contribution each student makes to the learning of others in the course. The contribution is assessed through an elaborate and refined mechanism that draws on structured peer evaluation. There is one participation grade. The weight is 10% 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 team quiz grade per team. The weight is 20% if better than the final exam grade, and 0% otherwise.

Team cases and project.

This component combines live performance in two case roles and a live team project presentation. There is one team cases and project grade. The weight is 30% if better than the final exam grade, and 0% otherwise. The component breaks down as follows:

•         Case opener role (7.5%): Each team plays the opener role on one case allocated through the Class 1 auction.

•         Case discussant role (7.5%): Each team plays the discussant role on a second case, also allocated in the Class 1 auction.

•         Team project (15%): Topics are self-chosen by teams and allocated through the Class 1 auction. Teams present in Classes 20–21 (plus one additional session if needed). 

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 40% to 100%.

Non-exam grade components have expiration conditions, the details of which are covered in class.

1.     Course note packet: The online course pack contains video materials, syllabus, class lecture notes, technical documents, journal articles, and data files [On Blackboard]

2.     Cases: a set of cases we work through in class  [On Blackboard]

3.     FAQ: An up-to-date guide to Frequently Asked Questions about the course  [On Blackboard]

4.     Required readings: The required readings in this course are as limited as possible to avoid implicit workload. The few per session indicated required readings however are indeed required [On Blackboard]

5.     Pre-readings: See the course notes for pre-reading to be done before the course formally begins [On Blackboard]