论文标题
Stonkbert：语言模型可以预测中型股票价格变动吗？
StonkBERT: Can Language Models Predict Medium-Run Stock Price Movements?
论文作者
论文摘要
为了回答这个问题，我们在与公司相关的文本数据的不同来源上微调了基于变压器的语言模型，以预测一年股票价格绩效。我们使用三种不同类型的文本数据：新闻文章，博客和年度报告。这使我们能够分析语言模型的性能在多大程度上取决于基础文档的类型。与传统语言模型相比，我们的基于变压器的股票性能分类器Stonkbert在预测准确性方面显示出很大的提高。新闻文章作为文本来源实现了最高的性能。性能模拟表明，分类准确性的这些改进也转化为高于平均水平的股票市场回报。
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.
