This paper from Francesco Grigoli, Damiano Sandri, Yuriy Gorodnichenko, & Olivier Coibion studies how households perceive the transmission of monetary policy and how these perceptions affect their decisions.

As autonomous agents move from single-user tools to networked participants, trust infrastructure becomes critical. This work outlines a practical roadmap for certifiable, interoperable agent evaluation at scale.

If genomic foundation models are to become reliable tools for biology and medicine, the field may need to rethink how these models are built, trained, and evaluated.

As models improve rapidly, the field needs benchmarks that can keep up. HLE provides a rigorous, publicly available measuring stick for the most capable AI systems.

This paper introduces a nonparametric permutation test for event studies designed to remain statistically valid even when the number of firms is extremely small, addressing a key limitation of standard tests for average and cumulative abnormal returns. The method is evaluated through Monte Carlo simulations and illustrated using real-world financial data.

“How to Use the Sharpe Ratio” by Marcos López de Prado, Alexander Lipton, and Vincent Zoonekynd (ADIA Lab) re-examines the world’s most widely used measure of investment efficiency. The authors expose common statistical errors in Sharpe ratio analysis and present a rigorous framework for correcting bias, non-Normality, and multiple testing, ensuring the metric remains reliable for both researchers and practitioners.

This inaugural volume of Transactions of ADIA Lab features peer-reviewed research from ADIA Lab on finance, AI, and data science, highlighting breakthroughs in asset modelling, ethics, and high-performance computing.

This paper by Alexander Lipton & Marcos Lopez de Prado, introduces a new mathematical framework for valuing private equity (PE) investments by developing closed-form, analytical solutions to complex valuation problems, especially under conditions that more accurately reflect the reality of PE: illiquidity, nonlinearity, and market incompleteness.

By Marcos López de Prado & Vincent Zoonekynd, this paper proposes a major shift in how factor investing is approached, arguing that most traditional methods are flawed because they rely on associational rather than causal inference. By Marcos López de Prado & Vincent Zoonekynd