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Hot Events Detection of Stock Market Based on Time Series Data of Stock and Text Data of Network Public Opinion

机译：基于股票时间序列数据和网络舆情文本数据的股市热点事件检测

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摘要

With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and enables investors to quickly identify relevant financial events that may lead to stock market volatility. However, in the research of event detection in the financial field, many studies are focused on micro-blog, news and other network text information. Few scholars have studied the characteristics of financial time series data. Considering that in the financial field, the occurrence of an event often affects both the online public opinion space and the real transaction space, so this paper proposes a multi-source heterogeneous information detection method based on stock transaction time series data and online public opinion text data to detect hot events in the stock market. This method uses outlier detection algorithm to extract the time of hot events in stock market based on multi-member fusion. And according to the weight calculation formula of the feature item proposed in this paper, this method calculates the keyword weight of network public opinion information to obtain the core content of hot events in the stock market. Finally, accurate detection of stock market hot events is achieved.
机译：随着Internet世界与现实世界的高度集成，Internet信息不仅为金融投资者提供实时有效的数据，而且还帮助他们了解市场动态，并使投资者能够快速识别可能导致股票下跌的相关金融事件。市场动荡。但是，在金融领域的事件检测研究中，许多研究集中在微博客，新闻和其他网络文本信息上。很少有学者研究金融时间序列数据的特征。考虑到在金融领域，事件的发生往往会同时影响在线舆论空间和真实交易空间，因此本文提出了一种基于股票交易时间序列数据和在线舆论文本的多源异构信息检测方法。数据以检测股市中的热点事件。该方法采用离群检测算法，基于多成员融合提取股市热点事件的时间。并根据本文提出的特征项权重计算公式，计算网络舆情信息的关键词权重，得出股市热点事件的核心内容。最终，实现了对股票市场热点事件的准确检测。

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