@inproceedings{li-lu-2026-decoding,
title = "Decoding the Market{'}s Pulse: Context-Enriched Agentic Retrieval Augmented Generation for Predicting Post-Earnings Price Shocks",
author = "Li, Chenhui and
Lu, Weihai",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.141/",
pages = "3055--3073",
ISBN = "979-8-89176-380-7",
abstract = "Accurately forecasting large stock price movements after corporate earnings announcements is a longstanding challenge. Existing methods{--}sentiment lexicons, fine-tuned encoders, and standalone LLMs{--}often **lack temporal-causal reasoning** and are prone to **narrative bias**, echoing overly optimistic managerial tone. We introduce **Context-Enriched Agentic RAG (CARAG)**, a retrieval-augmented framework that deploys a team of cooperative LLM agents, each specializing in a distinct analytical task: evaluating historical performance, assessing the credibility of guidance, or benchmarking against peers.Agents retrieve structured evidence from a Causal-Temporal Knowledge Graph (CTKG) built from financial statements and earnings calls, enabling grounded, context-rich reasoning. This design mitigates LLM hallucinations and produces more objective predictions.Without task-specific training, our system achieves state-of-the-art zero-shot performance across NASDAQ, NYSE, and MAEC datasets, outperforming both larger LLMs and fine-tuned models in macro-F1, MCC, and Sharpe, beating market benchmarks (S P 500 and Nasdaq) for the same forecasting horizon. Code, datasets, prompts, and implementation details are included in the supplementary material to ensure full reproducibility."
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<abstract>Accurately forecasting large stock price movements after corporate earnings announcements is a longstanding challenge. Existing methods–sentiment lexicons, fine-tuned encoders, and standalone LLMs–often **lack temporal-causal reasoning** and are prone to **narrative bias**, echoing overly optimistic managerial tone. We introduce **Context-Enriched Agentic RAG (CARAG)**, a retrieval-augmented framework that deploys a team of cooperative LLM agents, each specializing in a distinct analytical task: evaluating historical performance, assessing the credibility of guidance, or benchmarking against peers.Agents retrieve structured evidence from a Causal-Temporal Knowledge Graph (CTKG) built from financial statements and earnings calls, enabling grounded, context-rich reasoning. This design mitigates LLM hallucinations and produces more objective predictions.Without task-specific training, our system achieves state-of-the-art zero-shot performance across NASDAQ, NYSE, and MAEC datasets, outperforming both larger LLMs and fine-tuned models in macro-F1, MCC, and Sharpe, beating market benchmarks (S P 500 and Nasdaq) for the same forecasting horizon. Code, datasets, prompts, and implementation details are included in the supplementary material to ensure full reproducibility.</abstract>
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%0 Conference Proceedings
%T Decoding the Market’s Pulse: Context-Enriched Agentic Retrieval Augmented Generation for Predicting Post-Earnings Price Shocks
%A Li, Chenhui
%A Lu, Weihai
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F li-lu-2026-decoding
%X Accurately forecasting large stock price movements after corporate earnings announcements is a longstanding challenge. Existing methods–sentiment lexicons, fine-tuned encoders, and standalone LLMs–often **lack temporal-causal reasoning** and are prone to **narrative bias**, echoing overly optimistic managerial tone. We introduce **Context-Enriched Agentic RAG (CARAG)**, a retrieval-augmented framework that deploys a team of cooperative LLM agents, each specializing in a distinct analytical task: evaluating historical performance, assessing the credibility of guidance, or benchmarking against peers.Agents retrieve structured evidence from a Causal-Temporal Knowledge Graph (CTKG) built from financial statements and earnings calls, enabling grounded, context-rich reasoning. This design mitigates LLM hallucinations and produces more objective predictions.Without task-specific training, our system achieves state-of-the-art zero-shot performance across NASDAQ, NYSE, and MAEC datasets, outperforming both larger LLMs and fine-tuned models in macro-F1, MCC, and Sharpe, beating market benchmarks (S P 500 and Nasdaq) for the same forecasting horizon. Code, datasets, prompts, and implementation details are included in the supplementary material to ensure full reproducibility.
%U https://aclanthology.org/2026.eacl-long.141/
%P 3055-3073
Markdown (Informal)
[Decoding the Market’s Pulse: Context-Enriched Agentic Retrieval Augmented Generation for Predicting Post-Earnings Price Shocks](https://aclanthology.org/2026.eacl-long.141/) (Li & Lu, EACL 2026)
ACL