Converting Expert Deliberation into Financial Signals Through A Context-Aware NLP Pipeline

2026-08-19

AI Summary

研究通过CDSP将投资委员会会议记录转化为预测特征，以预测全球股票与债券表现，最佳模型结合了句子嵌入和工程特征，准确率达73%。

Abstract

We introduce the CDSP (context-conditional deliberation signal pipeline), converting an investment committee's meeting transcripts into structured predictive features. CDSP segments the meeting transcripts into topical chunks, assigns asset-class context labels using a large language model (LLM), maps financial keywords to a pre-determined taxonomy of labels, and constructs complementary features: sentiment polarity and mention frequency. This feature engineering framework is applied to a dataset spanning 48 monthly committee meetings to predict if global equities will perform better or worse than global bonds in the following month. In experiments with engineered features, raw transcript text, sentence embeddings, and combined representations, the prediction accuracy ranges from 62% to 73%, compared to always choosing stocks, which outperforms bonds 60.4% of the time. The best (73% accurate) model combines sentence embeddings with engineered CDSP features, achieving a 0.73 F1 score (although this is not statistically significant compared to always choosing stocks). Sentiment carries a stronger signal than mention frequency for several taxonomy categories. These findings suggest that experts' deliberations may contain forward-looking information that context-aware NLP can extract.

Problem

• investment committee
• meeting transcripts
• predictive features
• global equities
• global bonds

Innovation

• context-conditional deliberation signal pipeline
• large language model
• sentiment polarity
• mention frequency
• sentence embeddings