Predicting stock price and spread movements from news
Predicting stock price and spread movements from news
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DOI：
10.24251/hicss.2021.192
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Pontus Wistbacka;Samuel Rönnqvist;Katia Vozian;Satchit Sagade
中科院分区：
文献类型：
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作者：
Pontus Wistbacka;Samuel Rönnqvist;Katia Vozian;Satchit Sagade
我们探索了几种使用新闻文章和金融数据来训练神经网络机器学习模型的方法，以预测高频市场数据中的冲击事件和汇总的冲击事件。我们研究了在这种情况下价格变动的使用，并且也以每日间隔单独研究。我们详细描述了如何从我们的数据源创建训练集，以及如何训练我们的机器学习模型。我们发现，将公司相关的新闻文本与金融时间序列中的事件或运动配对证明不如文献所表明的那样直接。我们讨论了负面结果的可能原因，特别是与分钟级新闻和毫秒级市场数据相结合。
—We explore several ways of using news articles and financial data to train neural network machine learning models to predict shock events in high-frequency market data, and aggregated shock episodes. We investigate the use of price movements in this context, and separately at a daily interval as well. We describe in detail how training sets are created from our data sources and how our machine learning models are trained. We find that pairing company-related news text with events or movements in financial time series proves less straight-forward than the literature would indicate. We discuss possible reasons for negative results, especially relating to the combination of minute-level news and millisecond-level market data.