AI for Financial Research | VRI Conference 2026
In this research-focused session from the Seventh Annual Volatility and Risk Institute Conference, João Sedoc (NYU Stern) chairs a deep dive into "AI for Research." Four distinguished academic and industry experts present their latest cutting-edge papers, demonstrating how artificial intelligence, machine learning, and advanced modeling are transforming financial research and our understanding of market dynamics.
Presentations & Speakers:
Bryan Kelly (Frederick Frank ’54 and Mary C. Tanner Professor of Finance, Yale School of Management; Head of Machine Learning, AQR Capital Management)
Presentation: The Inefficient Pricing of News (co-authored with Mo Pourmohammadi, Antoine Didisheim, Hanqing Tian)
Robert Richmond (NYU Stern)
Presentation: Asset Embeddings (co-authored with Xavier Gabaix, Ralph S.J. Koijen, Motohiro Yogo)
Sebastian Hillenbrand (Harvard Business School)
Presentation: Inflation Uncertainty: Measurement, Causes, and Consequences (co-authored with Viral Acharya, Venky Venkateswaran, and Margaret Underwood)
Sophia Kazinnik (Stanford University)
Presentation: Ex Machina: Financial Stability in the Age of Artificial Intelligence (co-authored with Kartik Anand, Agnese Leonello, and Ettore Panetti)
About the Conference:
The Seventh Annual Volatility and Risk Institute Conference: "Artificial Intelligence and Risk Management" was held on Friday, April 24, 2026, at 44 West 4th Street, New York, NY.
Artificial intelligence (AI) creates vast opportunities for reshaping business and the economy, enhancing productivity, reducing costs, and streamlining operations. Nowhere is this transformation more evident than in finance, where AI tools can analyze vast quantities of information to improve efficiency and reduce human error. However, AI also carries risks and can be prone to error. Developing sound practices to ensure its responsible use is essential.
This conference examined the opportunities and challenges of applying AI to identify and manage risks in finance. Topics included enhancing the monitoring of firms and markets, enabling deeper research into systemic risks, and addressing data gaps exposed by AI analysis.