  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.
