The MCF approach to learning is experiential. Participants learn by doing things Interactive lecture notebooks delivered, primarily, via Jupyter Notebooks and
RMarkdown technologies are combined with practical programming exercises in
Python and R, real financial and economic analysis, and hands-on modeling
Our goal is to raise the level of education and readiness to solve the tasks that 21st century businesses and finance industry require.
Only academic knowledge is not enough. Therefore, practical work, real time and real business tasks are incorporated into the MCF program.
Visiting practicioners from all around the world provide valuable insights in the global finance and investment best practices.
My company delegated me to the MCF’s module Financial Computing and Quantitative Investments. This amazing course provides the full scope of the investment problematic. A lot of useful and practical concepts were covered. Be ready to “get your hands dirty” and work on numbers of team assessments and projects, which of course, will lead to your better understanding. Another pleasant bonus in this course was introduction to web scrapping and Machine Learning Algorithms to analyze texts.
This course is highly recommended mainly to those, who have some basic knowledge of Python, Linear Algebra and Statistics.
Consultant in Market Risk team
Ernst & Young (EY), Czech Republic
REAL STUFF FOR THE REAL WORLD
“We get to trade with different classess of assets using a very realistic trading simulator. Our trading strategies are presented to our professors. Sometimes we win, sometimes we lose. But, it is great fun!”
LEARN FROM INDUSTRY EXPERTS
A number of distinguished industry experts are guest lecturers on particular
topics of their expertise. In 2022, e.g., Tomas Sobotka (Ernst & Young
Prague) has taught a class on practical applications on stochastic volatility
models within the Financial Derivatives module. In addition, experts from the
MVP Workshop, one of the world’s leading Web3 and blockchain developers
will teach a sub-module on Blockchain Development and Decentralized
PROGRAMMING AT MCF
You will be encouraged to create your own trading algorithms, trading bots
and roboadvisors as well as blockchain applications within the Ethereum
All your questions about MCF will be answered promptly.
You just have to ask.
Professor of Economics and Finance at the School of Computing (Raf) and Fellow, CESIfo, Munich. Holds a PhD in Physics from Brown University and a PhD in Finance from U.C. Berkeley. He created and directed the IMQF program at the University of Belgrade until 2020. From 2021, he directs and teaches at the MCF program. Taught at the Frankfurt School of Finance and Management, ICEF (Moscow), and Pompeu Fabra (Barcelona). Worked in McKinsey & Co and KPMG (Chicago). His recent research is in quantitative investment strategies and risk management.
Branko Urošević, PhD
Full Professor, MCF Program Director
Assistant Professor in Economics and Finance at the School of Computing (Raf). Managing Director, Oxquant Consulting, Oxford, UK. PhD in Engineering from Imperial College (UK). A hedge fund professional and an EU expert, he taught at London Business School and Queen Mary University London. He is teaching and launching fintech solutions based on data science and machine learning.
Drago Inđić, PhD
Assistant Professor in Algorithmic Trading, Operating Systems, Computer Networking, Natural Language Processing, Speech Recognition, Visual Systems at the School of Computing (Raf) in Belgrade. He has an extensive experience in manual and algorithmic, retail trading and developed 500+ automatic trading systems since 2000. His research interests are in Algorithmic Trading, Artificial Intelligence, Machine Learning, Knowledge Representation, Natural Language Processing and Visual Systems.
Mladen Stanojević, PhD
Assistant Professor in Economics and Finance at the School of Computing (Raf). Received a PhD in Economics (Banking and Finance) from the University of Zurich. External Lecturer at the Department of Banking and Finance at the University of Zurich. Previously, he worked for seven years as an investment strategist for International Wealth Management division of Credit Suisse in Zurich. Main research interests: asset pricing, asset allocation, financial engineering, and machine learning.
Nikola Vasiljević, PhD
Finance lecturer and banking consultant. Holds a PhD in Finance from Technical University in Dortmund and BSc and MSc in Math from University of Belgrade. Specialized in market risk, he worked for Deloitte and PwC, where he consulted most of the top 10 German banks in the last decade. Voted top lecturer at TU Dortmund in 2019. His areas of expertise include investments, financial risk management, and banking transformation.