Analyzing trends across industries is critical to maintaining a healthy and
stable economy. Previous research has mainly analyzed official statistics,
which are more accurate but not necessarily real-time. In this paper, we
propose a method for analyzing industry trends using stock market data. The
difficulty of this task is that the raw data is relatively noisy, which affects
the accuracy of statistical analysis. In addition, textual data for industry
analysis needs to be better understood through language models. For this
reason, we introduce the method of industry trend analysis from two
perspectives of explicit analysis and implicit analysis. For the explicit
analysis, we introduce a hierarchical data (industry and listed company)
analysis method to reduce the impact of noise. For implicit analysis, we
further pre-train GPT-2 to analyze industry trends with current affairs
background as input, making full use of the knowledge learned in the
pre-training corpus. We conduct experiments based on the proposed method and
achieve good industry trend analysis results.

本文提出了一种利用股票市场数据进行产业趋势分析的方法，并从显式分析和隐式分析两个方面引入了产业趋势分析方法，即层次数据分析和基于 GPT-2 预训练模型的分析，最终取得了良好的产业趋势分析结果。