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Articles by Bradford Levy
Assessing the Benefits of Optimized Agentic AI Systems for Asset Pricing
We thank Andrew Chen (discussant), Ingrid Tierens, Stefan Nagel, and conference and seminar participants at The Future of AI in Asset Management Conference at Chicago Booth, the ABFR Seminar, Baruch College, Columbia University, and the University of Utah for helpful feedback and suggestions. For financial support, Koijen thanks the Center for Research in Security Prices at the University of Chicago and the Fama Research Fund at the University of Chicago Booth School of Business.
Computer Science > Computation and Language
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Computer Science > Computation and Language
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Caution Ahead: Numerical Reasoning and Look-Ahead Bias in AI Models
A cat can remember, can understand the physical world, can do some level of reasoning actually much better than the biggest LLMs. Yann LeCun Chief AI Scientist, Meta Jacob T. Schwartz Professor of Computer Science, Data Science, Neural Science, and Electrical and Computer Engineering, New York University. 1 Introduction A challenge with applying AI models to our research is that these models are often black boxes.
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