Moving targets -- managers' strategic shifting of key performance metrics when the original targets become difficult to achieve -- have been shown to predict subsequent stock underperformance. However, our work reveals that the method employed in that study exhibits two key limitations that hinder the accuracy -- noise in the extracted targets and loss of contextual information -- both of which stem primarily from the use of a named entity recognition (NER). To address these two limitations, we propose an LLM-based target extraction method with a newly defined metric that better captures semantic context. This approach preserves semantic context beyond simple entity recognition and yields consistently higher predictive power than the original approach. Overall, our approach enhances the granularity and accuracy of financial text-based performance prediction.

翻译：移动目标——即当原有业绩目标难以达成时，管理层对关键绩效指标的策略性调整——已被证明能够预测后续股价表现不佳。然而，我们的研究发现，该研究所采用的方法存在两个关键局限性，影响了预测准确性：提取目标时的噪声干扰以及上下文信息的丢失。这两个问题主要源于对命名实体识别（NER）技术的依赖。为应对这些局限，我们提出了一种基于大型语言模型的目标提取方法，并引入一种新定义的度量指标，以更有效地捕捉语义上下文。该方法不仅保留了超越简单实体识别的语义语境，而且相比原始方法展现出持续更强的预测能力。总体而言，我们的研究提升了基于金融文本的绩效预测的精细度与准确性。