Personalization algorithms for behavioural inclusion in trading strategies

This project is to further Finliti’s proprietary behavioral science-backed trading algorithms by creating and analyzing data models. This will also provide the basis for quantifying the value proposition of specialized financial advice and education at scale for the wealth management landscape in Canada to direct Finliti’s growth and competitive advantage in the market place. Finliti intends to assess to what extent its behavioural science framework allows it to significantly improve on the quality of the recommended portfolios and trading strategy parameters by incorporating the desired parameters into a conditional portfolio optimization framework.

Faculty Supervisor:

Alexander Schied

Student:

Partner:

Finliti

Discipline:

Mathematics

Sector:

Finance and Insurance; Professional, scientific and technical services

University:

University of Waterloo

Program: