(L-R) Chris Guthrie, CFA, President and CEO, CIO, Senior Portfolio Manager, Founding Partner, Hillsdale Investment Management; Najah Attig (Winner); Harry Marmer, CFA, EVP and Partner, Hillsdale Investment Management
TORONTO, Sept. 26, 2025 (GLOBE NEWSWIRE) — CFA Society Toronto is pleased to announce that this year’s winners of the 2025 Hillsdale Investment Management – CFA Society Toronto Research Award are Najah Attig, PhD., Professor of Finance and Chair of the Department of Finance at Dalhousie University, Canada and Chahine Attig, Data Science Engineering student at ENSAI, for their exceptional research and groundbreaking paper entitled, “Bay Street Meets Machine Learning: Predicting Stock Risk Premium”. The winners were announced at CFA Society Toronto’s marquee event, the 68th Annual Investment Dinner, where they were awarded a $10,000 cash prize.
“This award recognizes excellence in the field of investment research and fosters a rich exchange of insights that benefits the entire investment community,” said Fred Pinto, CFA, ICD.D, CEO of CFA Society Toronto. “We congratulate the winners for their outstanding achievement and advancing innovation and insights for the finance and investment community.”
Their forward-looking research and innovative methods set them apart as this year’s winners of the prestigious award. The study breaks new ground in Canadian capital markets by providing the first empirical evidence on the effectiveness of machine learning techniques in forecasting stock risk premiums, alongside novel insights into whether anonymous trading imbalances can predict stock returns. By exploring these timely and underexamined questions, the award winners’ research contributes significantly to the field of empirical asset pricing. Their findings not only deepen understanding of return predictability, a central theme in modern finance, but also offer valuable implications for asset managers and researchers navigating today’s increasingly complex and dynamic markets.
“Applications of machine learning methods to portfolio management and investments have grown in popularity in both academia and industry, but very few have focused on Canadian markets. This winning paper shows some promise in applying machine learning methods to extract higher dimensional signals from the Canadian stock market,” – Chris Guthrie, President and CEO, Hillsdale Investment Management Inc.
The Hillsdale Investment Management – CFA Society Toronto Research Award welcomes researchers from around the world to conduct studies on Canadian capital markets. This includes both academic researchers, such as professors and students, as well as practitioners. To ensure high quality, the research papers are evaluated by a panel of CFA® Charterholder investment experts. The award aims to recognize research that aligns with the values and standards upheld by the CFA charter.
Award Recipients’ Reflections:
Najah Attig, PhD., Professor of Finance and Chair of the Department of Finance at Dalhousie University, Canada: “Receiving the 2025 Hillsdale Investment Management – CFA Society Toronto Research Award is a great honor that carries deep professional and personal significance for me. This recognition affirms the value of producing rigorous scholarship that bridges academic inquiry with practitioner relevance, particularly within Canadian capital markets. In this work, we provide the first empirical evidence on the performance of machine learning methods in forecasting Canadian stock risk premiums, demonstrating that select techniques materially enhance return predictability and portfolio construction. Beyond personal recognition, this award strengthens my motivation to continue pursuing research that contributes to academic knowledge while also informing practice and policy in ways that advance the broader investment and financial community.”
Chahine Attig, Data Science Engineering student at ENSAI: “Winning the 2025 Hillsdale Investment Management – CFA Society Toronto Research Award marks a pivotal moment in my journey as a student pursuing quantitative finance. I anticipate that this recognition will broaden my horizons and further inspire me to embrace the challenges and opportunities of this field. Our research paper, “Bay Street Meets Machine Learning: Predicting Stock Risk Premium”, highlights the outperformance of machine learning methods over traditional linear models in predicting stock risk premium, while also shedding light on how anonymous trading can influence market dynamics. The journey was challenging—balancing the research with the start of my internship in a new environment, overcoming technical setbacks, and working late nights—but the experience was deeply rewarding. Collaborating with my uncle, whom I greatly admire, added a unique dimension to the work. Navigating time differences and different work rhythms while leveraging each other’s strengths made this our first official, yet successful, collaboration. Winning this award has been a culmination of hard work, and sacrifice, and I look forward to the new opportunities and learning it will bring.”
The Award judging panel commended the high quality of the award winners’ academic research for being both “actionable” and “Canadian market specific”.
Research Paper Abstract:
We evaluate the performance of machine learning (ML) methods in forecasting Canadian equity risk premiums and show that moderately flexible models-such as gradient-boosted trees (XGBoost) and mid-depth neural networks consistently outperform deeper architectures and traditional linear benchmarks across portfolio strategies and horizons. Monthly long-short portfolios constructed using these models generate economically meaningful excess returns and notable improvements in Sharpe ratios relative to traditional benchmarks. Annual back tests further confirm the robustness of these gains across investment horizons. Our results highlight that return predictability is concentrated in small-cap and value stocks. Furthermore, predictive power is substantially enhanced in stocks characterized by market frictions, including high information asymmetry and low trading volume, where regularized and nonlinear models achieve superior risk-adjusted returns. We also show that elevated net anonymous buying and greater broker-level dispersion of anonymous order flow augment forecast accuracy, yielding 60 to 80 basis points higher monthly returns and Sharpe ratios that approach or exceed 0.9 for flexible learners. Overall, select ML techniques materially enhance return forecasting and portfolio construction in the Canadian equity market.
Click here to read this year’s award-winning research paper.
About Hillsdale Investment Management Inc.
Celebrating over 29 years of investment excellence, Hillsdale Investment Management is an independent, employee-owned and client-aligned investment boutique managing over $9.0 billion on behalf of a select group of institutional and private wealth investors. Hillsdale engages in disciplined active investment management, specializing in equities, alternatives, ESG and customized mandates. Hillsdale is recognized for both investment and service excellence – a reflection of its relentless pursuit of research and development and the dedication of its partners and employees. For more information, please refer to www.hillsdaleinv.com.
About CFA Society Toronto
CFA Society Toronto is the world’s largest Society of CFA® Charterholders, dedicated to supporting the professional and business development of more than 11,500 members. With a focus on education, networking, and thought leadership, CFA Society Toronto empowers finance professionals to excel in a dynamic and competitive global market. Founded in 1936, CFA Society Toronto is a not-for-profit organization affiliated with CFA Institute, the global body that administers the Chartered Financial Analyst (CFA) curriculum and sets voluntary, ethics-based performance-reporting standards for the investment industry. For more information, visit cfatoronto.ca. For more information visit http://www.cfatoronto.ca or follow us on Twitter @cfatoronto and on LinkedIn CFA Society Toronto.
Chartered Financial Analyst® and CFA® are registered trademarks owned by CFA Institute.
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