Adaptive Machine Learning for Stock Market Monitoring

dc.contributor.advisorAzencott, Robert
dc.contributor.committeeMemberGunaratne, Gemunu H.
dc.contributor.committeeMemberTimofeyev, Ilya
dc.contributor.committeeMemberTörök, Andrew
dc.creatorLin, Weiqiang
dc.date.accessioned2021-09-03T20:34:59Z
dc.date.createdMay 2021
dc.date.issued2021-05
dc.date.submittedMay 2021
dc.date.updated2021-09-03T20:35:00Z
dc.description.abstractWith the thriving of research on machine learning and the demand for innovative methods of approaching stock markets, recent years have seen a surge of applying machine learning techniques to stock markets. This dissertation explored the effectiveness of some basic machine learning models applied to the stocks from the Information Technology Sector of the S&P 500 Index over a period of five years (from October 1st, 2013 to September 30th, 2018). And these application were under a time adaptive model training framework that was proposed to simulate the trading scenarios in real world. After comparing the prediction performances of different models based on the ``prediction matrices'', it seemed that random forest (RF) models were more accurate than simple MLP and LSTM models when predicting price trends of the target stock by the other stocks in the IT Sector, although RF models may suffer from low betting frequency during the trading simulation.
dc.description.departmentMathematics, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/8161
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectMachine learning
dc.subjectStock market
dc.subjectRandom forest
dc.subjectMultilayer perceptron
dc.subjectLong short-term memory
dc.titleAdaptive Machine Learning for Stock Market Monitoring
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2023-05-01
local.embargo.terms2023-05-01
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentMathematics, Department of
thesis.degree.disciplineMathematics
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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