To answer this question, we fine-tune transformer-based language models,
including BERT, on different sources of company-related text data for a
classification task to predict the one-year stock price performance. We use
three different types of text data: News articles, blogs, and annual reports.
This allows us to analyze to what extent the performance of language models is
dependent on the type of the underlying document. StonkBERT, our
transformer-based stock performance classifier, shows substantial improvement
in predictive accuracy compared to traditional language models. The highest
performance was achieved with news articles as text source. Performance
simulations indicate that these improvements in classification accuracy also
translate into above-average stock market returns.

本文使用 BERT 等基于变换器的语言模型对不同公司相关文本数据进行分类，以预测一年的股票表现，并使用新闻文章、博客和年报三种不同类型的文本数据进行分析。研究发现，使用新闻文章源的 StonkBERT 在股票表现分类方面比传统语言模型表现出更好的预测精度，该研究还表明这种精度提升也会转化为股票市场上超出平均的回报率。