By a News Reporter-Staff News Editor at Journal of Robotics & Machine Learning -- Current study results on Engineering - Knowledge Engineering have been published. According to news reporting originating from Indianapolis, Indiana, by VerticalNews correspondents, research stated, “Online social networks are now attracting a lot of attention not only from their users but also from researchers in various fields. Many researchers believe that the public mood or sentiment expressed in social media is related to financial markets.”
Funders for this research include National Science Foundation, National Institute of Food and Agriculture.
Our news editors obtained a quote from the research from Indiana University, “We propose to use trust among users as a filtering and amplifying mechanism for the social media to increase its correlation with financial data in the stock market. Therefore, we used the real stock market data as ground truth for our trust management system. We collected stock-related data (tweets) from Twitter, which is a very popular Micro-blogging forum, to see the correlation between the Twitter sentiment valence and abnormal stock returns for eight firms in the S&P 500. We developed a trust management framework to build a user-to-user trust network for Twitter users. Compared with existing works, in addition to analyzing and accumulating tweets’ sentiment, we take into account the source of tweets - their authors. Authors are differentiated by their power or reputation in the whole community, where power is determined by the user-to-user trust network. To validate our trust management system, we did the Pearson correlation test for an eight months period (the trading days from 01/01/2015 through 08/31/2015). Compared with treating all the authors equally important, or weighting them by their number of followers, our trust network based reputation mechanism can amplify the correlation between a specific firm’s Twitter sentiment valence and the firm’s stock abnormal returns. To further consider the possible auto-correlation property of abnormal stock returns, we constructed a linear regression model, which includes historical stock abnormal returns, to test the relation between the Twitter sentiment valence and abnormal stock returns.”
According to the news editors, the research concluded: “Again, our results showed that by using our trust network power based method to weight tweets, Twitter sentiment valence reflect abnormal stock returns better than treating all the authors equally important or weighting them by their number of followers.”
For more information on this research see: Using Twitter trust network for stock market analysis. Knowledge-Based Systems , 2018;145():207-218. Knowledge-Based Systems can be contacted at: Elsevier Science Bv, PO Box 211, 1000 Ae Amsterdam, Netherlands. (Elsevier - www.elsevier.com; Knowledge-Based Systems - http://www.journals.elsevier.com/knowledge-based-systems/)
The news editors report that additional information may be obtained by contacting Y.F. Ruan, Indiana University, Purdue University, Dept. of Comp & Informat Sci, Indianapolis, IN 46202, United States. Additional authors for this research include A. Durresi and L. Alfantoukh.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.knosys.2018.01.016. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-04-16), Study Findings from Indiana University Provide New Insights into Knowledge Engineering (Using Twitter trust network for stock market analysis), Journal of Robotics & Machine Learning, 238, ISSN: 1944-186X, BUTTER® ID: 015496453
From the newsletter Journal of Robotics & Machine Learning.
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