---
title: Assessing the Benefits of Optimized Agentic AI Systems for Asset Pricing
url: https://www.ml-quant.com/papers/repec/nbr-nberwo-35431/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:nbr:nberwo:35431
source_url: https://econpapers.repec.org/RePEc:nbr:nberwo:35431
featured: 2026-09-25
citations: unknown
topic: ML & AI Methods
---


# Assessing the Benefits of Optimized Agentic AI Systems for Asset Pricing

Optimized AI systems analyzing earnings call transcripts double explained variation in stock returns versus standard benchmarks while improving interpretability through human-readable decision rules.

- Source: https://econpapers.repec.org/RePEc:nbr:nberwo:35431
- Identifier: RePEc:nbr:nberwo:35431
- Released: 2026-09-17
- First featured: Quant Letter No. 132 (2026-09-25): https://www.ml-quant.com/issues/2026-09-25/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods
- Authors: Ralph S. J. Koijen, Bradford Levy

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