Advancing NLP-based Techniques and Quantitative Models at Picton Mahoney Asset Management

Picton Mahoney Asset Management (“PMAM”) was founded in 2004 to provide unique investment solutions to institutional, retail and high net worth investors in Canada and around the world. They are 100% employee-owned and manage approximately $10.8 billion in sub-advisory, pension plan and hedge fund assets on behalf of their clients.

The Quantitative Research and Risk team at PMAM is dedicated to developing and maintaining models that support investment decisions and risk assessments. However, with the evolving complexity of financial markets and the increasing demand for data-driven insights, the team faces challenges in optimizing model accuracy, enhancing operational efficiency and improving the predictive power of risk assessment tools.

By advancing NLP-based techniques and quantitative models, this project aims to address these specific stakeholder needs, enabling the team to generate actionable insights from both structured and unstructured data, and improve the precision and robustness of predictive analytics, offering a competitive edge in client portfolio management and risk mitigation.

This project requires expertise in NLP, machine learning, statistical analysis and finance. Familiarity with R, Python, SQL and machine learning libraries, like TensorFlow or PyTorch, will enable effective model development and backtesting. Knowledge of financial risk modeling and familiarity with specialized tools for portfolio management and sentiment analysis is essential.

University of Toronto’s Master of Mathematical Finance (MMF) students bring technical and financial expertise that meets PMAM’s Quantitative Research & Risk team’s needs. They’ll contribute advanced programming skills in R and Python to PMAM’s quantitative investment platform by researching and maintaining alpha-generating strategies. Their hands-on experience in quantitative analysis and model development enable them to enhance PMAM’s model accuracy, operational efficiency and predictive power.

Through this internship opportunity, PMAM can support their transition from academia to industry while benefiting from their fresh perspectives, technical innovation and data-driven methodologies to strengthen PMAM’s quantitative investment strategies.

Faculty Supervisor:

Luis Seco;Tracy Barber

Student:

Partner:

PICTON Investments

Discipline:

Mathematics

Sector:

Finance and Insurance

University:

University of Toronto

Program: