Thu, Oct 29, 11:45 AM - 01:00 PM (CT)
Artificial intelligence is transforming the investment landscape, but how is it being applied in practice within quantitative investing? This session will provide an accessible introduction to quantitative and systematic investment strategies, exploring how data, signals, portfolio construction, and risk management are combined to support investment decision-making.
Participants will gain an overview of factor investing, including well-known factors such as value, momentum, quality, size, and low volatility, as well as the economic rationale behind factor premia. The session will also examine the challenges associated with the growing number of proposed investment factors and discuss best practices for distinguishing robust signals from data mining.
Building on these foundations, the discussion will focus on the role of artificial intelligence and machine learning in modern quantitative investing. Topics will include security selection, factor combination, the use of alternative data, and the identification of complex non-linear relationships that traditional models may overlook.
Finally, the session will address how quantitative and AI-driven strategies are evaluated and tested, including performance measurement, risk analysis, backtesting, and model validation. Particular attention will be given to the opportunities and limitations of AI, including challenges such as overfitting, model instability, and interpretability. Attendees will leave with a practical understanding of how AI is shaping the future of quantitative investing and the key considerations investors should keep in mind when assessing these strategies.
Regina, SK, Canada