Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/11234
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dc.contributor.authorAl Nasseri, A-
dc.contributor.authorTucker, A-
dc.contributor.authorde Cesare, S-
dc.date.accessioned2015-08-17T11:22:24Z-
dc.date.available2015-08-17T11:22:24Z-
dc.date.issued2015-
dc.identifier.citationExpert Systems with Applications, 42: pp. 9192–9210, (2015)en_US
dc.identifier.issn0957-4174-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0957417415005473-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/11234-
dc.descriptionThis article is available under the terms of the Creative Commons Attribution License (CC BY).en_US
dc.description.abstractGrowing evidence is suggesting that postings on online stock forums affect stock prices, and alter investment decisions in capital markets, either because the postings contain new information or they might have predictive power to manipulate stock prices. In this paper, we propose a new intelligent trading support system based on sentiment prediction by combining text-mining techniques, feature selection and decision tree algorithms in an effort to analyse and extract semantic terms expressing a particular sentiment (sell, buy or hold) from stock-related micro-blogging messages called “StockTwits”. An attempt has been made to investigate whether the power of the collective sentiments of StockTwits might be predicted and how the changes in these predicted sentiments inform decisions on whether to sell, buy or hold the Dow Jones Industrial Average (DJIA) Index. In this paper, a filter approach of feature selection is first employed to identify the most relevant terms in tweet postings. The decision tree (DT) model is then built to determine the trading decisions of those terms or, more importantly, combinations of terms based on how they interact. Then a trading strategy based on a predetermined investment hypothesis is constructed to evaluate the profitability of the term trading decisions extracted from the DT model. The experiment results based on 122-tweet term trading (TTT) strategies achieve a promising performance and the (TTT) strategies dramatically outperform random investment strategies. Our findings also confirm that StockTwits postings contain valuable information and lead trading activities in capital markets.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectDecision treeen_US
dc.subjectFilter approachen_US
dc.subjectText miningen_US
dc.subjectTrading strategyen_US
dc.titleQuantifying stocktwits semantic terms' trading behavior in financial markets: An effective application of decision tree algorithmsen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.eswa.2015.08.008-
dc.relation.isPartOfExpert Systems with Applications-
pubs.publication-statusAccepted-
pubs.publication-statusAccepted-
Appears in Collections:Brunel Business School Research Papers

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