Decoding the Market’s Pulse: Context-Enriched Agentic Retrieval Augmented Generation for Predicting Post-Earnings Price Shocks

Accurately forecasting large stock price movements after corporate earnings announcements is a longstanding challenge.Existing methodssentiment 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, contextrich 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.The complete code, datasets and prompts are available at https://github.com/luweihai/ CARAG.