MulFSA: Multi-level Financial Sentiment Analysis Framework for Bond Market
It addresses the need for nuanced sentiment analysis in financial risk assessment for bond market participants, but is incremental as it combines existing PLMs/LLMs with a multi-level design.
The paper proposes MulFSA, a multi-level sentiment analysis framework integrating micro, meso, and duration-aware smoothing for bond markets, achieving 10.25% MAE and 11.94% MAPE reduction in credit spread forecasting on a Chinese bond market corpus.
Existing financial sentiment analysis methods often fail to capture the multi-faceted nature of risk in bond markets due to their single-level approach and neglect of temporal dynamics. We propose Multi-level Financial Sentiment Analysis (MulFSA) based on pre-trained language models (PLMs) and large language models (LLMs), a novel framework that systematically integrates firm-specific micro-level sentiment, industry-specific meso-level sentiment, and duration-aware smoothing to model the latency and persistence of textual impact. Applying MulFSA to the comprehensive Chinese bond market corpus constructed by us (2013-2023, 1.35M texts), we extracted a daily composite sentiment index. Empirical results show statistically measurable improvements in credit spread forecasting when incorporating sentiment (10.25% MAE and 11.94% MAPE reduction), with sentiment shifts closely correlating with major social risk events and firm-specific crises. Project Page: https://mulfsa.github.io/.