MENTOR: a multi-agent framework for event and narrative trend prediction with optimized reasoning

Narrative economics suggests that financial markets are strongly influenced by evolving narratives, creating opportunities for forecasting emerging events and their economic impacts. However, existing large language model (LLM)-based approaches are inadequate in terms of systematic task decomposition and alignment with financial applications. We propose MENTOR, a multi-agent framework for event and narrative trend prediction that integrates teacher-student iterative reasoning with progressive subtasks: detecting and ranking trending events, forecasting future events from current narratives, and predicting industry index performance influenced by these events. Experiments on our self-constructed Chinese key opinion leader (KOL) articles dataset and English financial news dataset show that MENTOR consistently outperforms recent baselines such as the stakeholder-enhanced future event prediction (StkFEP) and summarize–explain–predict (SEP) frameworks in both event prediction and industry ranking tasks. In addition, the backtest results at the portfolio level show that improved event and industry forecasts can bring about a practical improvement in investment performance. These results demonstrate that incorporating structured reasoning and multi-agent feedback enables more reliable event forecasting and strengthens the connection between narrative dynamics and financial market outcomes.