基于文本分类的新闻异常收益预测
Predicting Abnormal Returns From News Using Text Classification
摘要：
我们展示了如何使用支持向量机从新闻文章的文本可以用来预测金融资产的日内价格变动。多核学习用于将股票收益与文本作为预测特征相结合以提高分类性能，并且我们开发了一种分析中心切割平面方法来有效地解决核学习问题。我们观察到，虽然回报的方向是不可预测的，无论是使用文本或回报，他们的大小，与文本功能产生显着更好的性能比历史回报。
英文摘要：
We show how text from news articles can be used to predict intraday price movements of financial assets using support vector machines. Multiple kernel learning is used to combine equity returns with text as predictive features to increase classification performance and we develop an analytic center cutting plane method to solve the kernel learning problem efficiently. We observe that while the direction of returns is not predictable using either text or returns, their size is, with text features producing significantly better performance than historical returns alone.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
