Twitter Equity Firm Value

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dc.contributor.authorSmith, Jacoben
dc.contributor.authorWiskur, Christianen
dc.contributor.authorGuinn, Nathanielen
dc.contributor.authorAgren, Eriken
dc.contributor.authorRane, Rohanen
dc.date.accessioned2018-05-09T15:42:22Zen
dc.date.available2018-05-09T15:42:22Zen
dc.date.issued2018-05-09en
dc.description.abstractWe analyzed how a company's response on social media (Twitter) can affect their stock market value following a data breach. Given a list of all data breaches since 2006 we collected their stock value for 150 days before the data breach and 120 after. Using a Fama French Model we came up with an abnormality value that demonstrated how the stock would have performed if no data breach occurred. While doing this we simultaneously collected tweets from the companies and customers about the data breach. We wanted to compare the stock performance to things such as the number of replies from a company, customer tweet sentiment, and links tweeted by the company. The way we did all of this work was by building Python scripts for all of the functionalities. When scraping the tweets the user would just need to supply a CSV file with the company's Twitter handle and company name. The other Python scripts used, do things like compute the abnormality difference from the client's Fama French Model, scrub the stock data to only have the date range needed, compute tweet sentiment, and grab client profiles. Our conclusion was that companies need to make few but comprehensive announcement tweets to decrease reply tweets. This could keep the sentiment of client tweets positive. Lastly, companies need to focus on replying to customer tweets to also keep sentiment positive.en
dc.description.notesTwitterEquityFirmValueReport.pdf - PDF version of our final report. TwitterEquityFirmValueReport.docx - DOCX version of our final report. TwitterEquityFirmValuePresentation.pdf - PDF version of our final presentation. TwitterEquityFirmValuePresentation.pptx - PPTX version of our final presentation. abnormal.py - Python script used to analyze stock abnormality around data breach events. abnormalDif.csv - CSV containing values representing differences in stock performance before and after data breach events. announcement_reply_firm.py - Python script used to analyze firm Twitter replies to users. countURLs.py - Python script used to count URLs in relevant tweets. keyword_scrape.py - Python script used to scrape tweets within certain date ranges containing words from a list of keywords. keywords.txt - Text file containing the keywords used for scraping relevant tweets. profile_scrape.py - Python script used to scrape profiles for relevant information. scrape_company_tweets.py - Python script used to scrape company Twitter timelines for tweets related to data breaches. stockManipulation.py - Python script used to clean and manipulate stock price data. stockReturn.csv - CSV containing 10 years of stock information for all companies affected by data breaches in our data set. user_sentiment.py - Python script used to calculate and output user sentiment in tweets related to data breach events. -3to3.csv - CSV containing 7 days of Fama French output values for each data breach, representing how abnormal each company's stock performance was around target dates. 160_searsholdings.csv - Example of stockManipulation.py output file, containing 150 rows of stock data related to a Sears Holding data breach. 160_searsholdings_keywords.csv - Keyword_scrape.py output containing rows of tweets and their associated metadata related to a Sears Holding data breach. 2_Target Corp..csv - Example of stockManipulation.py output file, containing 150 rows of stock data related to a Target Corp. data breach. 647_Dyn_company.csv - Example of a CSV file containing company tweets with associated analysis and metadata surrounding a Dyn data breach. 647_Dyn_user.csv - Example of a CSV file containing user tweets with associated analysis and metadata surrounding a Dyn data breach. AllDataBreaches.xlsx - XLSX file containing all data breach events and their associated information.en
dc.identifier.urihttp://hdl.handle.net/10919/83199en
dc.language.isoen_USen
dc.publisherVirginia Techen
dc.rightsCreative Commons CC0 1.0 Universal Public Domain Dedicationen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/en
dc.subjectSecurityen
dc.subjectBreachen
dc.subjectTwitteren
dc.subjectEquityen
dc.subjectFirmen
dc.subjectAnalysisen
dc.subjectPythonen
dc.subjectSentimenten
dc.subjectStocken
dc.subjectDataen
dc.titleTwitter Equity Firm Valueen
dc.typeDataseten
dc.typePresentationen
dc.typeReporten
dc.typeSoftwareen

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