Master Meetings

Have you decided on which Master programme to study? Would you like more information on the contents and teaching methods at USI? Register at our Master Meetings to attend courses.
The various Master Meetings offer you the opportunity to follow lectures together with the current master students. Guided by a USI student, you can visit the campus and make up your mind as to whether the contents correspond to your study ambitions.

Registration is compulsory. Please register online.


MSc in Finance

A-11, Red Building


Corporate Finance

Prof. Laurent Frésard


This course teaches the logic underlying the firm’s financial decisions: investment, financing, and payout policies.
The main themes of the course are:

  • What is corporate finance?
  • The concept of value
  • The discounted cash-flow (DCF) method in theory and practice
  • Capital budgeting decisions
  • Market efficiency and financial policy
  • Financial structure and the Modigliani-Miller theorem
  • Payout policies: dividends and share repurchases.

A-22, Red Building

Structured Products
(Minor in Quantitative Finance)

Prof. Nicola Carcano


Apply derivatives know-how in order to analyze and invest in structured products.

In an extensive interpretation, we may speak of structured products as soon as we combine two or more elementary financial products into a new structure displaying original characteristics. Structured products represented one of the most rapidly growing areas of finance in the last two decades, reaching a nearly unlimited variety of forms. The goal of this course is to provide an overview about the process of developing, marketing, and managing structured products. To this purpose, the course will analyze a certain number of popular structured products, like: autocallable notes and other barrier reverse convertibles, principal protected notes, participation certificates, convertible and callable bonds, mortgage-backed securities, and CDOs.

A-32, Red Building


Financial Econometrics

Prof. Loriano Mancini


The aim of this course is to familiarize the student with some of the most popular econometric methods encountered in applied work in finance.

Building on the material acquired in a basic introductory course in econometrics, the aim of this course is to familiarize the student with some of the most popular econometric methods encountered in applied work in finance. After a brief review of the classical linear model, three major topics are considered:

  • The Linear Factor Pricing Model
  • Likelihood Methods, with an application to ARCH and GARCH models
  • Ultra high frequency data

Emphasis is placed on the basic understanding of each approach, together with computer applications on real data.

A-22, Red Building

Data Analytics for Finance
(Minor in Digital Finance)

Prof. Peter Gruber



Tukey (1962) defines Data Analysis to be “Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.”

The goal of this course is to provide the students with the tools and thinking framework to accomplish these tasks.


1. The nature of financial data

Random variables, data generation, data types

2. Managing data

ETL, encodings, data bases, quality checks, relations, data at scale, approximate data analysis, the time dimension

3. Standard data sets in finance

Computstat, CRSP, Optionmetrics, Markit, Factset plus data access Bloomberg

4. Alternative and historic data

Microblogging, search engines, trade, blockchain, satellite images, 19th century stocks

5. Exploratory data analysis

Robust and non-robust descriptive statistics, hypothesis generation, verification of assumptions

6. Advanced statistical methods

Dealing with non-rectangular and non-numerical data

7. Data visualization

Perception and aesthetics; exploratory, illustrational and statistical visualizations, interactive visualization

8. Additional topics

  • The data economy: data as product and raw material, licensing, open data
  • Data in research: sharing and publishing data sets, case studies in the value of (new) datasets
  • Managing a data science project
  • Storytelling with data
  • Copyright, privacy