Problem
Research questions and friction points this paper is trying to address.
Agentic Empirical Asset Pricing
autonomous discovery system
factor discovery
evaluation standard
re-execution
Agentic Empirical Asset Pricing: Methodological Foundations
🤖 AI Summary
本文提出了自主经验资产定价（AEAP）方法，通过构建参考架构和评估标准，对因子发现系统进行全面评价，解决了现有评估仅针对输出而非系统本身的问题。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agentic Empirical Asset Pricing
autonomous discovery system
factor discovery
out-of-sample backtesting
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