This course combines a deep analysis of traditional concepts, with an open-ended reflection upon the recent transition to a knowledge economy across most developed countries. Each week, you will first engage in a comprehensive examination of a specific topic in traditional corporate finance, analysing both foundational theories and salient empirical evidence. You will then engage in an exploratory analysis of how the traditional topic applies to intangible assets and human capital.

This course explores market microstructure; how trading works and prices form in securities markets. Learn about trading mechanisms deployed in modern securities markets, the role of market makers, and techniques to measure market liquidity empirically. You will discuss theories of market liquidity and price discovery, and further examine how liquidity affects asset prices.

In this course, you apply various statistical techniques to make causal inferences and predictions. You will learn how to work with panel datasets, state-of-the-art statistical techniques for addressing endogeneity and omitted variables problems, and apply econometrics to time-series data analysis. Particular attention will be paid to common failures of the exogeneity assumption and how to solve these through appropriate empirical design, as well as to making predictions with econometric forecasting models.

In this course you learn the Python programming language and how to apply it to a wide range of quantitative modelling techniques in finance. You will first learn the basics of data handling in Python. You will then write computer programmes that compute risk-management measures. The course also introduces students to machine learning techniques.

Choose 1 out of 2 courses: Advanced Investments or Derivatives.

In this course, you will learn statistical methodologies used on the current frontier of empirical analysis. You will also learn to implement these methodologies through weekly case assignments, completed using the statistical program STATA or computing language R. Discover the university’s databases and how to prepare for thesis research.

This course provides you with a solid foundation for your Master's thesis. You will start with a thesis proposal, which will receive multiple rounds of feedback from classmates. The guidance you receive in the course will help you to polish your proposal and prepare for further independent thesis work. Also, you will learn how to provide critical opinions of your classmates’ thesis proposals, and how to give effective presentations.

Choose 2 out of 3 courses: Quantitative Finance and Algorithmic Trading, Advanced Risk Management, or Behavioural Finance.

Choose 1 out of 2 courses: Ethics and Professional Skills in Finance or Sustainable Finance.

The Master’s thesis is the final requirement for your graduation. It is your chance to dive deep into a topic in your chosen track, and to learn how to independently complete a comprehensive research project. A professor with expertise on your topic will supervise your effort. You will also receive guidance on how to manage your time, break down a long-term project into a series of manageable steps, and communicate in a clear and professional manner.