Can Twitter Proxy the Investors' Sentiment? The Case for the Technology Sector

Corea, Francesco

doi:10.1016/j.bdr.2016.05.001

Cited by 30 publications

(17 citation statements)

References 17 publications

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“…The general opinion and investors' sentiment, what they think about an event or a company, influence companies' share prices. The investors' sentiment can be understood through the content of Tweets (Corea, 2016). For example, a large number of negative comments and sentiments in Tweets that targets a specific company might predict a future decline of its revenue (Alles & Gray, 2016) and imply a negative effect of share prices.…”

Section: Literature Reviewmentioning

confidence: 99%

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Using social media analytics: The effect of President Trump’s tweets on companies’ performance

Juma’h¹,

Alnsour²

2018

JAMIS

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Social media is becoming one of the main sources of unstructured data. Many financial analysts use Twitter to gather data and obtain meaningful insights. Recently, high-profile politicians like President Trump have been using Twitter to communicate with the public. The President's Tweets are used as considered to be informative signals that may influence consumers and affect investors' decisions in the stock market. The effect of these signals can be measured by coinciding changes in the stock market or changes in the share prices of certain companies targeted in the President's Tweets. The Tweets used in this research include keywords that are related to finance and economics, public policy, political considerations, and targeted companies. Event study methodology is used to determine the relationship between Trump's Tweets' content and daily changes in major indexes and targeted companies' excess returns. Although many of the recent discussions have indicated that Trump's microblogging can affect the financial markets, the results, on average, show no significant effect of Trump's Tweets on either market indexes nor on most of the targeted companies share prices.

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Section: Literature Reviewmentioning

confidence: 99%

“…Business analysis depends mainly on structured data sources. However, text mining permits management accountants to excerpt decision-related information from unstructured data from different sources such as social media (Appelbaum et al, 2017;Corea, 2016;Alexander & Gentry, 2014).…”

Section: Using Social Media Analytics: the Effect Of President Trump'mentioning

confidence: 99%

Using social media analytics: The effect of President Trump’s tweets on companies’ performance

Juma’h¹,

Alnsour²

2018

JAMIS

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“…The market influenced by many things since there are many indicators related to the corporation stock, such as the economy microeconomics, the regulation of the market, the competitor activities, and also many related activities could influence the future projection of the stock (Corea, 2016).…”

Section: Theoretical Reviewmentioning

confidence: 99%

Stock Exchange Frequent Topics @NYSE

2019

RJFA

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The utilization of Twitter content to predict the market is necessary since the fundamental perception related to the activities of the market influenced by the opinion in the market space. Therefore this research is utilizing the tweet of @NYSE and compare the research finding @IDX_BEI the stock exchange office in Indonesia. There is 2069 tweet extracted from the period January until June 2018. Further analysis using the Exploratory Factor Analysis conducted, and show that @NYSE delivering more varieties of information related to the stock market. It is not only information related to the IPO, but also an opinion from the recognized economist and analyst. The findings show that Twitter could be improved for further utilization and reduce asymmetric information related to the market

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“…Such a direction includes Twitter analysis [6] and Facebook analysis [7]. The second one analysed millions of Facebook pages in order to predict stock behaviour.…”

Section: Literature Reviewmentioning

confidence: 99%

Extracting Patent-Related Information from Online Social Networks: Case of Facebook

Ivanov¹,

Tekić²

2017

DAAAM Proceedings

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In this study, we investigate how to identify and extract patent-related information from Facebook, the largest online social network. In the first step, we identified a list of trustworthy sources we started search from. Then, we developed algorithms for extracting and filtering information, and based on them software tool that is able to identify and deliver a patent, the Facebook post where it is mentioned and news / blog article which discuss it. We did a pilot test and collected more than 50 examples of articles (and Facebook posts) that add value to the patent they refer to. We classified collected articles and discussed how they can be used. Finally, we outlined where developed tool can be applied.

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Can Twitter Proxy the Investors' Sentiment? The Case for the Technology Sector

Cited by 30 publications

References 17 publications

Using social media analytics: The effect of President Trump’s tweets on companies’ performance

Using social media analytics: The effect of President Trump’s tweets on companies’ performance

Stock Exchange Frequent Topics @NYSE

Extracting Patent-Related Information from Online Social Networks: Case of Facebook

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