A finance professor from Canada and a data science student from France took the top prize at the 2025 Hillsdale Investment Management – CFA Society Toronto Research Award.
The work of Najah Attig, professor of finance and chair of the Department of Finance at Dalhousie University, and Chahine Attig, a data science engineering student at École Nationale de la Statistique et de l’Analyse de l’Information (ENSAI), France, provides one of the first comprehensive academic evaluations of machine learning techniques for forecasting equity risk premiums in Canadian capital markets. Compared with US, European, and Chinese markets, Canada has seen limited application of these techniques despite its distinct structural, liquidity, and informational characteristics.
“This winning paper shows promise in applying machine learning methods to extract higher-dimensional signals from the Canadian stock market,” said Chris Guthrie, CEO of Hillsdale Investment Management.
Canadian markets are dominated by small-cap and value stocks with greater information asymmetry, market frictions, and liquidity constraints, creating unique challenges and opportunities for predicting returns. These characteristics highlight the need for advanced modeling approaches beyond traditional linear methods.
The researchers address two key questions:
- Can machine learning models improve Canadian stock return forecasts compared with classical linear benchmarks?
- Do patterns in anonymous trading—where traders’ identities are concealed to prevent information leakage or market speculation—and variations in brokers’ anonymous trading activity help predict stock returns?
