Can LLMs Hit Moving Targets? Tracking Evolving Signals in Corporate Disclosures
This paper addresses the “moving target” problem arising from managerial dynamic adjustments of key performance indicators (KPIs) in corporate disclosures, which undermines text-driven financial forecasting. Traditional named entity recognition (NER)-based KPI extraction suffers from high noise and contextual information loss, limiting prediction accuracy. To overcome these limitations, we propose a large language model (LLM)-driven KPI extraction framework that integrates context-aware semantic understanding with a novel indicator-level semantic similarity metric, enabling fine-grained, low-noise identification of KPI evolution. Experiments demonstrate substantial improvements in KPI tracking stability and forecasting accuracy. Across multiple financial outcome prediction tasks—including earnings surprise, revenue growth, and profitability—our method consistently outperforms NER-based baselines with robust performance gains. By explicitly modeling semantic continuity and temporal shifts in KPI definitions, the framework establishes a new paradigm for text-based dynamic signal modeling in financial analytics.