Journal of Data Analysis and Information Processing

Volume 7, Issue 4 (November 2019)

ISSN Print: 2327-7211   ISSN Online: 2327-7203

Google-based Impact Factor: 1.33  Citations  

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

HTML  XML Download Download as PDF (Size: 5281KB)  PP. 174-189  
DOI: 10.4236/jdaip.2019.74011    382 Downloads   739 Views  
Author(s)

ABSTRACT

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.

Cite this paper

Cao, B. (2019) Hot Events Detection of Stock Market Based on Time Series Data of Stock and Text Data of Network Public Opinion. Journal of Data Analysis and Information Processing, 7, 174-189. doi: 10.4236/jdaip.2019.74011.

Cited by

No relevant information.

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.