Understanding Markets in the Age of AI: A Citadel Securities Perspective
AI opens new ways to understand markets. Financial markets present distinctive challenges, from the nature of market activity to how we define and measure success. Drawing on Citadel Securities’ decades of experience studying and participating in markets, this talk explores those challenges and makes the case for trading firms as the best place to apply modern AI techniques. We discuss how market knowledge, data, and experience evaluating real decisions can shape what these powerful methods make possible.
When Powerful Models Meet Weak Signals: Generalization in Financial Deep Learning
Transformers and other deep learning models have changed fields, such as financial markets, where scale, data, and capacity translate into better performance. Predictive relationships are weak, observations are highly dependent, and underlying distribution changes over time. In this setting, expressive models can learn relationships that look strong in sample but do not generalize. Simultaneously, large-scale model development creates a statistical challenge: researchers evaluate architectures, features, objectives, and experiments against the same history, turning model selection into a multiple-testing problem.
AI, Information, and Systematic Investing: A GQS Perspective
AI helps turn the enormous flow of information in financial markets into investment insights. For mid-horizon systematic equity investing, the challenge is not simply building larger models, but identifying economically relevant information across diverse data and determining whether it leads to robust, repeatable forecasts. AI provides new tools for extracting and combining information at scale—from market and fundamental data to alternative data. Learn how financial domain knowledge, expanding data coverage, and rigorous out-of-sample evaluation shape what these increasingly powerful methods can—and cannot—learn about future returns.