Agentic Empirical Asset Pricing: Methodological Foundations

Abstract:Recent advances in LLM agents enable a new paradigm for asset pricing, which we call Agentic Empirical Asset Pricing (AEAP): systems that autonomously conduct the scientific discovery process itself. We define AEAP and identify its core building blocks. Existing evaluation practices backtest only the outputs (factors or trades), not the autonomous discovery system that produced them. We focus on factor discovery, contributing a reference architecture, a rigorous evaluation standard for discovered factors, and a method for out-of-sample backtesting the discovery system. As a concrete instance of that architecture, we evaluate SEADS against five re-implemented baselines on two US equity panels using this standard: no single metric ranks the systems consistently, motivating evaluation on multiple axes at once. A separate rolling re-execution then asks the complementary question of whether the discovery process itself, not one static output, is reliable. We also report negative findings and limitations that surface further evaluation pitfalls for future AEAP systems.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Statistical Finance (q-fin.ST)

arXiv:2609.00731 [cs.AI]

(or arXiv:2609.00731v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.00731 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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