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dc.contributor.advisorAhmad, Khurshid
dc.contributor.authorFLYNN, DANIEL ROGER
dc.date.accessioned2019-08-28T15:32:41Z
dc.date.available2019-08-28T15:32:41Z
dc.date.issued2019en
dc.date.submitted2019
dc.identifier.citationFLYNN, DANIEL ROGER, Brexit has us all on Edge, An Investigation into the Predictive Efficacy of Investor Sentiment Proxies on Irish Stock Market Returns during Brexit., Trinity College Dublin.School of Computer Science & Statistics, 2019en
dc.identifier.otherYen
dc.descriptionAPPROVEDen
dc.description.abstractIn this piece of research, we examine the evolution of sentiment proxies, commonly used to account for investor behaviour in the financial economics literature. Advancements in computing techniques, such as sentiment analysis and natural language processing (NLP), have allowed the creation of investor sentiment proxies directly from textual news data. Recent literature identifies that such proxies may be used to predict movements in financial assets, particularly during heightened periods of investor sensitivity - recessions, bad news cycles, etc. Following the techniques employed in other markets, we construct a sentiment indicator for the Irish Stock Market, and evaluate it against returns for the ISEQ 20 Index. With Brexit introducing uncertainty into the Irish market as a whole, we look to expand the construction of sentiment proxies from one news source, as per the existing literature, to many. Following this we evaluate the predictive power of the indicator created.en
dc.language.isoenen
dc.publisherTrinity College Dublin. School of Computer Science & Statistics. Discipline of Computer Scienceen
dc.rightsYen
dc.subjectBrexiten
dc.subjectInvestor Sentimenten
dc.subjectMarket Sentimenten
dc.subjectSentiment Indicatorsen
dc.subjectComputational Financeen
dc.subjectAlternative Dataen
dc.subjectNatural Language Processing for Financeen
dc.titleBrexit has us all on Edgeen
dc.title.alternativeAn Investigation into the Predictive Efficacy of Investor Sentiment Proxies on Irish Stock Market Returns during Brexit.en
dc.typeThesisen
dc.type.supercollectionthesis_dissertationsen
dc.type.supercollectionrefereed_publicationsen
dc.type.qualificationlevelMasters (Research)en
dc.identifier.peoplefinderurlhttps://tcdlocalportal.tcd.ie/pls/EnterApex/f?p=800:71:0::::P71_USERNAME:DFLYNN5en
dc.identifier.rssinternalid206598en
dc.rights.ecaccessrightsopenAccess
dc.identifier.urihttp://hdl.handle.net/2262/89373


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