Introduction to Financial Industry

Gareth Witten

Data Science

Charles Tsang

Risk Management

Multiple Instructors

Machine Learning for Finance

Alik Sokolov

Climate Risk Management

Luis Seco

Communication

Luis Seco

Internship

Financial Markets & Corporate Policy

Rudi Zagst

Workshop in Mathematical Finance

TBA

Asset Management

TBA

Financial Modelling

Naveen Kalia

Risk Management Laboratory

Norbert Fogarasi

Blockchain Fundamentals

Romeo Ware

Innovation and Entrepreneurship

TBA

Introduction to Financial Industry

This course will introduce you to fundamental concepts inherent in financial systems. In order to discuss some of the most important concepts currently debated in finance, for example, “Main Street vs Wall Street”, Central Bank Digital Currencies etc. The course will discover the connections between financial institutions and economic well-being; examine the critical role of healthy banks, efficient asset markets and monetary policy. The course will also examine how commercial banks operate, examine the sources from which banks acquire their funds and how they use the funds they acquire, as well as how assets and liabilities function within banks. Commercial banks facilitate borrowing and lending, which provide valuable services to each party. Once these fundamentals are introduced, the course will explore stock market bubble and how asymmetric information affects financial markets. All of these topics will allow the student to understand and explore the impact of the recent global pandemic on markets and other critical discussion topics. (4 Lectures)

Information Technology

This course will use real data for analysis of time series and teach students how to capture and analyze data. These are skills students will find immediately useful in industry. By the end of the course students should have an introductory knowledge of Excel VBA and Matlab programming, sufficient to use them as tools for the remainder of the MMF program, as well as an introduction to R. Additionally, students will be introduced to financial data sets and data providers, the challenges of managing large financial data sets from differing sources, the principles and challenges of back-testing strategies with historical data sets, and an overview of the modern markets from which the data is derived. (4 Lectures)

Data Science

Over the past decade, data science and machine learning have gained immense popularity in many scientific disciplines. The reason for the emergence is due to theoretical advances in machine learning, availability of big data, and surges in computational capabilities. This course provides an introductory overview of data science methods in finance, investments, and risk management. The course covers a review of foundational probability and statistics, brief introduction to machine learning (supervised learning, unsupervised learning) and big data tools. (8 Lectures)