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dc.contributor.authorCremonesi P
dc.contributor.authorPagano R
dc.contributor.authorFrancalanci C
dc.contributor.authorMazzoni L
dc.contributor.authorPoli A
dc.contributor.authorMaggioni A
dc.contributor.authorElahi M
dc.date.accessioned2019-02-11T15:07:22Z
dc.date.available2019-02-11T15:07:22Z
dc.date.issued2018
dc.identifier.urihttps://arxiv.org/abs/1801.05295
dc.identifier.urihttp://hdl.handle.net/10863/8117
dc.description.abstractThis paper proposes a novel adaptive algorithm for the automated short-term trading of financial instrument. The algorithm adopts a semantic sentiment analysis technique to inspect the Twitter posts and to use them to predict the behaviour of the stock market. Indeed, the algorithm is specifically developed to take advantage of both the sentiment and the past values of a certain financial instrument in order to choose the best investment decision. This allows the algorithm to ensure the maximization of the obtainable profits by trading on the stock market. We have conducted an investment simulation and compared the performance of our proposed with a well-known benchmark (DJTATO index) and the optimal results, in which an investor knows in advance the future price of a product. The result shows that our approach outperforms the benchmark and achieves the performance score close to the optimal result. en_US
dc.languageEnglish
dc.language.isoenen_US
dc.rights
dc.titleSocial Network based Short-Term Stock Trading Systemen_US
dc.typeWorking Paperen_US
dc.date.updated2018-12-12T08:48:47Z
dc.language.isiEN-GB
dc.description.fulltextopenen_US


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