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dc.contributor.authorXu, Yichuan
dc.date.accessioned2020-03-31T17:21:47Z
dc.date.available2020-03-31T17:21:47Z
dc.identifier.urihttp://hdl.handle.net/10222/78230
dc.description.abstractStock prediction has been a popular research topic. Due to its stochastic nature, predicting the future stock market remains a difficult problem. This thesis studies the application of Deep Neural Networks (DNNS) in investment from following perspectives: sentiment, stock technical indicators and candlestick charting. In our first experiment, we use DNN to process collective sentiment on the news dataset from Kaggle, and then compare the performance between DNN and traditional machine learning approach. In our second experiment, we build our own dataset that covers 80 stocks from the US stock market. Our attention-based LSTM model shows overall accuracy of 54.6% and MCC of 0.0478 on the aggregate dataset and the best individual stock achieve 64.7% of accuracy. Our third experiment studies the Japanese candlestick charting. In this experiment, harami patterns shows predictive power in our dataset and CNN model on candlestick charting shows great potential in stock market prediction.en_US
dc.language.isoenen_US
dc.subjectDeep Learningen_US
dc.subjectNLPen_US
dc.subjectCandlestick Chartingen_US
dc.subjectStock Predictionen_US
dc.titleStock Movement Prediction with Deep Learning, Finance Tweets Sentiment, Technical Indicators, and Candlestick Chartingen_US
dc.date.defence2020-02-11
dc.contributor.departmentFaculty of Computer Scienceen_US
dc.contributor.degreeMaster of Computer Scienceen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorMichael McAllisteren_US
dc.contributor.thesis-readerMalcolm Heywooden_US
dc.contributor.thesis-readerVladimir Lucicen_US
dc.contributor.thesis-supervisorVlado Keseljen_US
dc.contributor.thesis-supervisorSageev Ooreen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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