This article applies natural language processing (NLP) to extract and quantify textual information to predict stock performance. Using an extensive dataset of Chinese analyst reports and employing a customized BERT deep learning model for Chinese text, this study categorizes the sentiment of the reports as positive, neutral, or negative. The findings underscore the predictive capacity of this sentiment indicator for stock volatility, excess returns, and trading volume. Specifically, analyst reports with strong positive sentiment will increase excess return and intraday volatility, and vice versa, reports with strong negative sentiment also increase volatility and trading volume, but decrease future excess return. The magnitude of this effect is greater for positive sentiment reports than for negative sentiment reports. This article contributes to the empirical literature on sentiment analysis and the response of the stock market to news in the Chinese stock market.

翻译：本文应用自然语言处理技术提取并量化文本信息以预测股票表现。通过使用大规模的中国分析师报告数据集，并采用针对中文文本定制的BERT深度学习模型，本研究将报告情感分类为积极、中性或消极。研究结果突显了该情感指标对股票波动率、超额收益和交易量的预测能力。具体而言，具有强烈积极情感的分析师报告会增加超额收益和日内波动率；反之，具有强烈消极情感的报告同样会增加波动率和交易量，但会降低未来的超额收益。积极情感报告的影响幅度大于消极情感报告。本文为情感分析及中国股市对新闻反应的实证研究文献作出了贡献。