LLM-Driven Sentiment Analysis in MD&A: A Multi-Agent Framework for Corporate Misconduct Prediction

The textual analysis of Management Discussion and Analysis (MD&A) reveals valuable insights into corporate operational performance and future risks. However, techniques for accurately extracting sentiment from unstructured Chinese MD&A texts still lack comprehensiveness. Existing studies related to sentiment analysis often use lexicon-based methods, which rely on predefined, context-agnostic word lists and accurate Chinese word segmentation and struggle with domain-specific terminology, leading to limited accuracy and interpretability. Although research has attempted to develop context-aware lexicons and language models, these methods still face limitations when applied to long and complex financial texts. To address the limitations, we propose MDARisk, a novel framework for corporate misconduct prediction. The core of MDARisk is a MultiSenti module, which leverages a multi-agent LLM approach to extract comprehensive and contextual sentiment from MD&A. Unlike lexicon methods, our LLM-based module interprets words based on their surrounding semantic context, allowing it to decipher nuanced expressions and specialized financial language. We first conduct an econometric validation using fixed-effects logit models to test whether the MultiSenti-derived MD&A sentiment is significantly associated with subsequent corporate misconduct. We then evaluate out-of-sample predictive utility by adding this sentiment feature to multiple classifiers and assessing its incremental gains over the baseline model. Empirical results demonstrate that our approach provides a more reliable sentiment-based indicator for misconduct risk, achieves higher predictive accuracy, and outperforms the traditional financial sentiment analysis approach. Our MDARisk framework provides a cost-efficient approach for automated disclosure screening, benefiting auditors, regulators, and investors in assessing potential misconduct risks.