Quantitative Finance Master's | Autumn 2024

Background

In September 2024 I started my master’s degree in Quantitative Finance which is offered jointly by ETH Zurich and University of Zurich. As I will have my last exam on the 28th I think it’s a good time to stand still and look back (before having all my grades!). When I have some more time I will write something regarding the application and admission to this programme, even though this comes a bit after the application period.

The programme is built in 90 credits:

• 36 credits in core courses, finance or mathematical finance
• 24 credits in elective courses, where you kind of specialize
• 30 credits master’s thesis

There is not really a rule in which order you do your courses, but I believe you cannot frontload the master’s thesis though. As I studied computer science before, I had a slight knowledge gap in finance and economics, since I actually never took any courses related to that. Due to that I decided to mostly take core courses with a slight focus on finance. I took the following courses:

• Corporate Finance (3 ects, UZH)
• Asset Management (3 ects, UZH)
• Statistical Foundations for Finance (6 ects, UZH)
• Mathematical Foundations for Finance (4 ects, ETH)
• Introduction to Matlab (3 ects, UZH)
• Portfolio Management Theory I (3 ects, UZH)

The last two courses were mandatory courses I had to take because of the Portfolio Management Programme, where you get to learn everything about portfolio management and in the second year you get the manage a portfolio with real money of around 2 million euros.

Course Review

Corporate Finance

In this course you learn everything like the same says about Corporate Finance. You learn how to value a company from a more fundamental perspective, where you analyze the cashflows, earnings, growth and compare it to other similar companies. Before I took this course, I was afraid I would like it less than other courses since it would be less theoretical but more of a wet finger approach. However, I have to say I was pleasantly surprised. Professor Sautner is great at teaching, the first part of the course are real life examples where he shows you how companies have done their acquisitions and what went wrong with some of them. His teaching style is very engaging and tries to build up a a good understanding before moving onto the theory. The course grade is based on a case study (20%) and a final written exam (80%). After the initial classes, I felt like I was quite prepared for the take home since I actually understood what I am doing rather than randomly filling out Excel sheets.

I do have one slight complaint, but maybe that’s because I am more theoretical, some of the questions you might encounter do not have clear answers. Especially in corporate finance, there is always an exception to the rule, I find it hard to distinguish if the professor then wants the most inclusive answer or wants you to actually look for those crazy loopholes.

Asset Management

I think this course serves as an introduction to asset management, where you learn about portfolio allocation, strategy design, risk premia, Fama-French factors and other key concepts in finance. After doing this course, I have to say it’s definitely not an introduction, especially if you consider the workload for the 3 ects. The whole course has around 400 slides, a great summary of probably over 100 papers, but it’s dense. The slides are really great and to be honest if you study this course you learn a lot. Similar to the corporate finance course I felt like it was missing the theoretical aspect sometimes. You get to see a lot of formulas, but you are never really provided with the derivation e.g. Mean Variance Portfolio and CAPM. To me that is also what made the exam so hard, since you do have to write down all your answers like a machine, but I am more of someone who derives something in case I forgot let’s say a formula. If you do that you will definitely run out of the time during the exam.

Statistical Foundations for Finance

This course offers you an introduction to bootstrapping, niche probability distributions, names of famous statisticians and two projects. In one project you implement bootstrapping and in the other you fit distributions more elegantly. These two projects count for 50% of the grade and the other 50% are two exams.

Mathematical Foundations for Finance

My favourite course so far, but I haven’t written the exam yet (maybe I will change my mind!). Jokes aside, you get an introduction to stochastic calculus and derivatives pricing. The best part is you will be able to derive the Black-Scholes model. This course is generally seen as one of the hardest courses, but professor Possamaï does a great job at explaining the concepts and gives you a good practical understanding. I have to admit, this is also probably the course I spent the most of my time on during the semester. I never did any measure theory nor proper probability theory, which are however assumed for this course. Luckily I wasn’t the only person who struggled with this course, others who have a mathematical background would agree with me that it is a challenging course. Despite this, I would recommend everyone to take course, especially if you want to get into pricing or trading.

Introduction to Matlab & Portfolio Management Theory I

I will just combine these two, since they are closely related and requirements of the Portfolio Management Programme. In the Matlab course you learn how to backtest using Matlab and in the other course you have to backtest two academic papers and do a presentation on each of them. You get to learn about common pitfalls and how to do a proper backtest without any look-ahead bias.

Summary

As the semester is about to end, I think it’s now fair to say I am quite happy with this programme. It’s a smaller programme with around 50 students joining each year, which makes it incredible more personal compared to when I did computer science before (we had around 500 starting in my year). I know I want to work within Hedge Funds and the flexibility of courses makes it a very good choice. In the next semester I will try to do more courses like stochastic finance and machine learning, while at the same time working on building some other stuff ;)