@inproceedings{yang-2026-valid,
title = "When Valid Signals Fail: Regime Boundaries Between {LLM} Features and {RL} Trading Policies",
author = "Yang, Zhengzhe",
editor = "Mysore, Sheshera and
Kumar, Sachin and
Balachandran, Vidhisha and
Hayati, Shirley Anugrah and
Brahman, Faeze and
Moussa, Hanane Nour and
Salemi, Alireza",
booktitle = "Proceedings of the Second Workshop on Customizable {NLP}: Progress and Challenges in Customizing {NLP} for a Domain, Application, Group, or Individual ({C}ustom{NLP}4{U})",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.customnlp4u-1.17/",
doi = "10.18653/v1/2026.customnlp4u-1.17",
pages = "182--190",
ISBN = "979-8-89176-396-8",
abstract = "Can large language models (LLMs) generate continuous numerical features that improve reinforcement learning (RL) trading agents? We build a modular pipeline where a frozen LLM serves as a stateless feature extractor, transforming unstructured daily news and filings into a fixed-dimensional vector consumed by a downstream PPO agent. We introduce an automated prompt-optimization loop that treats the extraction prompt as a discrete hyperparameter and tunes it directly against the Information Coefficient{---}the Spearman rank correlation between predicted and realized returns{---}rather than NLP losses. The optimized prompt discovers genuinely predictive features (IC above $\sim$0.15 on held-out data). However, these valid intermediate representations do not automatically translate into downstream task performance: during a distribution shift caused by a macroeconomic shock, LLM-derived features add noise, and the augmented agent under-performs a price-only baseline. In a calmer test regime the agent recovers, yet macroeconomic state variables remain the most robust driver of policy improvement. Our findings highlight a gap between feature-level validity and policy-level robustness that parallels known challenges in transfer learning under distribution shift."
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%0 Conference Proceedings
%T When Valid Signals Fail: Regime Boundaries Between LLM Features and RL Trading Policies
%A Yang, Zhengzhe
%Y Mysore, Sheshera
%Y Kumar, Sachin
%Y Balachandran, Vidhisha
%Y Hayati, Shirley Anugrah
%Y Brahman, Faeze
%Y Moussa, Hanane Nour
%Y Salemi, Alireza
%S Proceedings of the Second Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-396-8
%F yang-2026-valid
%X Can large language models (LLMs) generate continuous numerical features that improve reinforcement learning (RL) trading agents? We build a modular pipeline where a frozen LLM serves as a stateless feature extractor, transforming unstructured daily news and filings into a fixed-dimensional vector consumed by a downstream PPO agent. We introduce an automated prompt-optimization loop that treats the extraction prompt as a discrete hyperparameter and tunes it directly against the Information Coefficient—the Spearman rank correlation between predicted and realized returns—rather than NLP losses. The optimized prompt discovers genuinely predictive features (IC above \sim0.15 on held-out data). However, these valid intermediate representations do not automatically translate into downstream task performance: during a distribution shift caused by a macroeconomic shock, LLM-derived features add noise, and the augmented agent under-performs a price-only baseline. In a calmer test regime the agent recovers, yet macroeconomic state variables remain the most robust driver of policy improvement. Our findings highlight a gap between feature-level validity and policy-level robustness that parallels known challenges in transfer learning under distribution shift.
%R 10.18653/v1/2026.customnlp4u-1.17
%U https://aclanthology.org/2026.customnlp4u-1.17/
%U https://doi.org/10.18653/v1/2026.customnlp4u-1.17
%P 182-190
Markdown (Informal)
[When Valid Signals Fail: Regime Boundaries Between LLM Features and RL Trading Policies](https://aclanthology.org/2026.customnlp4u-1.17/) (Yang, CustomNLP4U 2026)
ACL