@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://preview.aclanthology.org/ingest-eacl/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."
}