2 Mitacs Globalink (GRI) research projects for Summer 2027.
1. Contagion in financial markets: the application of network analysis in finance
The 2008 financial crisis highlighted the connectedness of financial institutions around the world. This networking can have important consequences, both positive and negative, for global and national financial systems. In this context, network theory and social network analysis (SNA) can provide helpful tools and measures to better understand the effects of networks or any other phenomena of finance or, to quote Allen and Babus (2009): “Mapping the networks between financial institutions is a first step towards gaining a better understanding of modern financial systems.”
The objective of this project is to study and analyse the topology and structure of different financial markets or networks, such as the mutual and pension funds networks, the syndicated loan market network and all the main securities markets. To do so, a comprehensive network theory approach that includes novel financial network analysis tools and metrics will offer an essential perspective on the underlying connections in the networks. Basic network structure metrics will be estimated, such as small-world statistic, scale-free parameters, etc. Further, novel network measures, which are typically developed in other fields, will be applied to the financial networks identified above: herd behavior, sources of diffusion, influential spreaders, bridges, adoption probabilities, mobility traces, etc. Because different networks can emerge from the same connections (e.g., dual-mode network, correlation network, etc.), the metrics will be estimated for a variety of networks.
The expected contributions of the project are both scientific and practical.
Research area, student roles & skills
Research area: International financial markets; risk management; systemic risk; financial institutions management; responsible finance
Student roles:
The student will work on the following tasks:
Managing data:
- Collecting mutual and pension fund data
- Organize data into tables that can be used for tests
Programming and analysis of network measures and tests:
- Programming of basic and novel network metrics in R (or Matlab)
* In some cases, the student will be asked to read an article from the network literature (chosen by professor) and program the measure suggested in the paper in order to apply it to the financial network.
- Analysis of network measures and results
Skills required:
1. Intermediate programming skills (R)
2. Basic / intermediate understanding of financial markets and securities
3. Strong reading, understanding and analysis skills
The student will either have a background in finance (or economics) with complementary programming skills or have a background in other fields (e.g. computers, physics, mathematics, sociology, etc.) with a knowledge of financial markets and securities. A basic knowledge of network theory is as asset.
The student must be very autonomous and show initiatives.
The student must speak and write in english or french
2. Development of Finacial Engineering Algorithms
This project aims to develop financial engineering methods for effective trading strategies. This is a new area for our research group, but given our longstanding expertise in digital signal processing, big data and machine learning we have a strong foundation to excel in this space. We have large sets of data, including 5 ms, 1 sec, and 1 min resolution for different levels of high-frequency trading. This data includes both equity and forex information. We are targeting the development of methods with high Sharpe ratios, minimal drawdowns and drawdown durations, with relatively fast turnover. We develop methods with backtests in Python, and have a platform for fast execution on C++ for viable methods.
The project tasks will include data curation and setting up for backtests. Development of back-testing methods, using advanced statistics and plotting. Conducting backtesting on the UCalgary high-performance compute (HPC) infrastructure. Working closely with the professor to come up with performance improvements and complexity reductions. Machine learning methods are welcomed.
In this project, the student will have the opportunity to work with high-frequency market data. This is a transdisciplinary project merging engineering and business.
Research area, student roles & skills
Research area: My research area is focused on the exciting aspects of big data and machine learning. I have extensive experience with medical imaging technologies; and as an electrical engineer. I use advanced data analytics, supercomputing and machine learning regularly in my research; and my trainees tend to be very skilled in these areas. I am actively expanding my research interests in AI Agentic programming and financial engineering applications, something I have been working on for over a decade and a half outside of the academy.
Student roles:
Please describe the required role of the student:
These internships are oriented towards the development of software that can be used towards the research program aims. The trainee will be required to develop and software and commit their changes to a code repository. The student is expected to meet for research team meetings, and one-to-one meetings with the supervisor weekly. We work in a dynamic team environment on many projects, so interacting with the other lab members is usually helpful.
The students have the opportunity to work with the more senior graduate students, and this will help them to get a better sense of what a research career might have for them. There are many successful graduate students at the University of Calgary who have previously come on the Mitacs GlobalLink Program. It is a really great chance to make a small contribution to research, while learning a lot and visiting a new place.
Our lab is fun and good spirited. Calgary is a city of greater than one Million people on the south-western edge of the Canadian Prairie, it is located in the foothills of the Canadian Rocky Mountains, which includes vast National Parks and Wilderness. These regions are home to world class Skiing and Hiking. Calgary is a diverse city with many activities. Although known for wealth from the oil industry, Calgary is focused on remaking itself as a high-tech hub, with many emerging bio and tech companies. Calgary has beautiful rivers and pathways throughout. Calgary is the sunniest city in Canada with an average of 333 days per year! Canada is known for its inclusive and diverse society, and we aim for our lab to have a similarly kind atmosphere. I strongly encourage applicants who might have a research interest in this area to seize this internship
Skills required:
An ideal student should have a demonstrated interest in a technical domain, such as: Electrical, Computer, or Software Engineering; applications from candidates in Physics, Math, Business, Finance, and Computer Science can also be a good fit for this research program and will be considered. Experience in computing and programming is desirable. Some experience in machine learning and time series processes would be an asset. Motivation, a good attitude, and the ability to work with others are required. Primarily, we are looking for students who have an interest in graduate studies.