Show simple item record

dc.contributor.authorBogle, Sherrene
dc.date.accessioned2016-03-24T04:30:22Z
dc.date.available2016-03-24T04:30:22Z
dc.date.issued2015-12
dc.identifier.otherbogle_sherrene_201512_phd
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/bogle_sherrene_201512_phd
dc.identifier.urihttp://hdl.handle.net/10724/34876
dc.description.abstractThe Jamaica Stock Exchange (JSE) has been defined by Standard and Poor's as a frontier market. It has undergone periods where trading gains exceeded that of major markets such as the London Stock Exchange. The randomness of the JSE was investigated over the period 2001 - 2014, using statistical tests and the Hurst exponent to reveal periods when the JSE did not follow a random walk. This dissertation focuses on machine learning algorithms including decision trees, neural networks and support vector machines used to predict the JSE. Selected algorithms were applied to trading data over a 22 month period for price and trend forecasting and a 12-year period for volume forecasts. Experimental results show 90% accuracy in the movement prediction with mean absolute error of 0.4 and 0.95 correlation coefficient for price prediction. Volume predictions were enhanced by a discretization method and support vector machine to yield over 70% accuracy. Being aware of the rapid impact social media comments have in the past had on stock markets, we decided to develop a model that incorporated social media input. This dissertation investigates the sentiments expressed on the social media platform Twitter and their predictive impact on the Jamaica Stock Exchange. A hybrid predictive model of sentiment analysis and machine learning algorithms including decision trees, neural networks and support vector machines are used to predict the Jamaica Stock Exchange. The architecture created, SentAMaL, investigated the impact of sentiments on medical marijuana legalization on relevant stock indices. Due to the unstructured nature of tweets, a customized pre-processing routine was developed prior to determining sentiment and to perform the prediction. Experimental results show 87% accuracy in the movement prediction and a 0.99 correlation coefficient and reduced mean absolute error of 0.2 for price prediction.
dc.languageeng
dc.publisheruga
dc.rightsOn Campus Only Until 2017-12-01
dc.subjectmachine learning
dc.subjectstock prediction
dc.subjectpre-processing
dc.subjectsentiment analysis
dc.subjectJamaica
dc.titleDevelopment of SentAMaL
dc.title.alternativea sentiment analysis machine learning hybrid for predicting stock markets
dc.typeDissertation
dc.description.degreePhD
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorWalter Potter
dc.description.committeeWalter Potter
dc.description.committeeKhaled Rasheed
dc.description.committeeTianming Liu
dc.description.committeeBudak Arpinar


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record