Open AccessStock Market Prediction Using Heat of Related Keywords on Micro Blog
Author(s) -
Shengchen Zhou,
Xunzhi Shi,
Yunchen Sun,
Wenting Qu,
Yingzi Shi
Publication year - 2013
Publication title -
journal of software engineering and applications
Language(s) - English
Resource type - Journals
eISSN - 1945-3124
pISSN - 1945-3116
DOI - 10.4236/jsea.2013.63b009
Subject(s) - composite index , stock market , granger causality , research object , support vector machine , stock market prediction , microblogging , stock (firearms) , econometrics , social media , causality (physics) , computer science , data mining , data science , artificial intelligence , business , machine learning , economics , engineering , world wide web , composite indicator , geography , mechanical engineering , business administration , context (archaeology) , physics , archaeology , quantum mechanics
Whether the stock market investors’ emotion can influence the stock market itself is one of the hot topic in financial research. In this paper, a method based on the heat of related keywords on Micro Blog is proposed, as Micro Blog is an ideal source for capturing public opinions towards certain topic. We choose Shanghai Composite index as the research object, through correlation analysis, Granger causality analysis, and support vector machine classification, the results have shown that the keywords heat on micro blog can make a short-time prediction of stock market, and the keyword which expresses negative emotion have more powerful prediction ability
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