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Predicting Stock Trading Volume through Social Media Data

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dc.contributor.author Chen, Yeqing
dc.date.accessioned 2016-05-26T17:48:02Z
dc.date.available 2016-05-26T17:48:02Z
dc.date.issued 2016-04-01
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1649
dc.description.abstract Social media plays a big role and can profoundly affect individual behavior and decision-making. This project is to predict the stock trading volume trend from multiple online sources. I chose Facebook to demonstrate the relationship between the stock trading volume and social media data. I used Twitter API to obtain the number of tweets, the number of “retweet” and “favorite” of twitter users. Support vector machine Model(SVM) is used in this project. The daily stock volume data is from Yahoo! Finance, Standard & Poor’s 500 indexes (S&P 500) during the period of July 2015 to December 2015. Through social media data, I analyze the tweet amount and the response form public. This way, I generated the percentage of impact rate on trading volume through social media data. It would be helpful to investment companies to predict the trend of stock market. en_US
dc.language.iso en_US en_US
dc.subject Data analysis en_US
dc.subject Data mining en_US
dc.subject Social media en_US
dc.subject Stock trading en_US
dc.title Predicting Stock Trading Volume through Social Media Data en_US
dc.type Presentation en_US
dc.institute.department School of Engineering en_US
dc.institute.name University of Bridgeport en_US
dc.event.location Bridgeport, CT en_US
dc.event.name Faculty Research Day en_US

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