In Finance, the use of Automated Trading Systems (ATS) on markets is growing every year and the trades generated by an algorithm now account for the majority of orders that arrive at stock exchanges. Historically, these systems were based on advanced statistical methods and signal processing able to extract trading signals from financial data. However, the recent successes of Machine Learning have attracted the interest of the financial community.
Quick Response Research has long allowed social, behavioural and economic science researchers to collect and integrate valuable first-response data in time-sensitive environments. This type of research is conducted during or shortly after an extreme event and allows social science researchers to collect perishable data that wouldn't be accessible otherwise. While quick response research has been used as an approach for collecting data for decades, important gaps remain in educating and training researchers in this particular form of research.
Borealis AI has access to a huge amount of financial data related to the stock market and is interested in leveraging recent developments in machine learning to better understand this data. Some potential questions emerging from this data are: (1) Given the closing price of a stock in the recent months, can we predict the stock returns within the next month? (2) If a stock crisis occurs, can we predict and control the spread of the crisis? (3) Given the current stock’s history, can we help reduce the risk of investment?.
In this research project, we will partner with the Financial Services Regulatory Authority of Ontario (FSRA) to enhance its default prediction model for private companies administering pension plans in Ontario. Our goal is to enhance the current model’s timeliness in predicting default of private companies by addressing the lack of publicly accessible information from these private entities.
This project targets development of applied methods and practical solutions to risk management problems where only partial observation of a system is possible. Such settings are commonplace in financial and other context but can be challenging to address due to a limited number of production-grade ready-to-use solutions. The scientific component of the project employs approaches from a quickly developing and active area in machine learning. More extensive use of these approaches by Canadian banking institutions will lead to a more robust financial system and better service.
This project is the first step to providing Thinking North’s Purple Squirrel recruitment platform to go beyond traditional matching with a novel, data-backed holistic candidate matching process. To provide a robust system, Thinking North is collaborating with Seneca’s School of Software Design and Data Science to use advanced artificial intelligence and gamification techniques to combine “psychology” and “gamification,” known as “psychification,” to enhance the screening aspect for a recruitment process.
The Embedding Project is a public-benefit research project that relies on strong social science research methods to bring together a global network of business sustainability change agents and harness their collective knowledge to develop rigorous and practical guidance that benefits everyone. This internship will offer an MBA student the opportunity to gain experience in both practice and research, while learning from leaders in the field.
The project aims to use state-of-the-art machine learning techniques to perform model validation. In particular, the intern will validate outcomes from risk assessment models for loan portfolios. The results will be employed to further the efficiency of ATB's internal stress testing models. The benefit for ATB financial will be the possibility to detect subsamples for which model fit might be poor, which will yield insights and, hopefully, improvement to stress testing.
One of the side effects of the COVI D-19 pandemic is that older people in institutional care tend to be more socially isolated and get less physical exercise. This is likely to increase falls risk both directly through reduced strength and balance because of insufficient exercise, and indirectly due to effects of depression leading to reduced awareness of obstacles and trip hazards in the environment. In this project we will test an innovative exercise technology (2RaceWithMe, developed by AGEWELL NCE researchers) to see if it can reduce falls risk in institutional care environments.
Motor vehicle-involved accident is the leading cause of death among teenagers around the world. Death and injuries due to motor vehicle accidents among young drivers brings tremendous societal burden and economic cost to the Canadian society. To better address this major public health concern, better assessment tools are required to evaluate potential risky driving among teenagers and young adults for tailored intervention and insurance purpose.