Industry Risk Assessment via Hierarchical Financial Data Using Stock Market Sentiment Indicators

TL;DR

This paper proposes a real-time, holistic industry risk assessment model that combines hierarchical financial data analysis with AI-driven sentiment interpretation to improve accuracy and timeliness.

Contribution

It introduces a dual explicit-implicit analysis framework using hierarchical data and pre-trained language models to enhance industry risk prediction accuracy.

Findings

Effective industry trend analysis demonstrated in experiments

Hierarchical data approach reduces noise impact

Pre-trained language models improve trend interpretation

Abstract

Risk assessment across industries is paramount for ensuring a robust and sustainable economy. While previous studies have relied heavily on official statistics for their accuracy, they often lag behind real-time developments. Addressing this gap, our research endeavors to integrate market microstructure theory with AI technologies to refine industry risk predictions. This paper presents an approach to analyzing industry trends leveraging real-time stock market data and generative small language models (SLMs). By enhancing the timeliness of risk assessments and delving into the influence of non-traditional factors such as market sentiment and investor behavior, we strive to develop a more holistic and dynamic risk assessment model. One of the key challenges lies in the inherent noise in raw data, which can compromise the precision of statistical analyses. Moreover, textual data about industry analysis necessitates a deeper understanding facilitated by pre-trained language models. To tackle these issues, we propose a dual-pronged approach to industry trend analysis: explicit and implicit analysis. For explicit analysis, we employ a hierarchical data analysis methodology that spans the industry and individual listed company levels. This strategic breakdown helps mitigate the impact of data noise, ensuring a more accurate portrayal of industry dynamics. In parallel, we introduce implicit analysis, where we pre-train an SML to interpret industry trends within the context of current news events. This approach leverages the extensive knowledge embedded in the pre-training corpus, enabling a nuanced understanding of industry trends and their underlying drivers. Experimental results based on our proposed methodology demonstrate its effectiveness in delivering robust industry trend analyses, underscoring its potential to revolutionize risk assessment practices across industries.