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New research proposes LLM agents for autonomous asset pricing discovery

By PulseAugur Editorial · [1 sources] ·

A new paper introduces Agentic Empirical Asset Pricing (AEAP), a paradigm where LLM agents autonomously conduct the scientific discovery process for asset pricing. The research defines AEAP, outlines its core components, and proposes a rigorous evaluation standard for these autonomous discovery systems, moving beyond just backtesting outputs. The paper also presents a reference architecture and a method for out-of-sample backtesting of the discovery system itself, highlighting potential evaluation pitfalls through negative findings and limitations. AI

IMPACT Introduces a novel framework for autonomous scientific discovery in finance using LLM agents.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research proposes LLM agents for autonomous asset pricing discovery

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The cluster contains a research paper published on arXiv detailing a new methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yingjian Pan, Xiaowei Ding, Kay Giesecke ·

    Agentic Empirical Asset Pricing: Methodological Foundations

    arXiv:2609.00731v1 Announce Type: new 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 …

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