Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149917
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTan, Nigel Jun Wenen_US
dc.date.accessioned2021-06-10T14:42:03Z-
dc.date.available2021-06-10T14:42:03Z-
dc.date.issued2021-
dc.identifier.citationTan, N. J. W. (2021). Stock trading and prediction using weakly supervised learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149917en_US
dc.identifier.urihttps://hdl.handle.net/10356/149917-
dc.description.abstractThis project attempts to use both technical analysis and sentimental analysis to predict stock market prices. The method in this published paper, Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network, will be replicated. After which, modifications will be made to try to improve on the results achieved in the published paper. Historical price of the S&P500 will be used along with news article from Bloomberg and Reuters.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleStock trading and prediction using weakly supervised learningen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorWang Lipoen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.supervisoremailELPWang@ntu.edu.sgen_US
item.fulltextWith Fulltext-
item.grantfulltextrestricted-
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
FYP_Final_report_Nigel_U1722170B.pdf
  Restricted Access
1.18 MBAdobe PDFView/Open

Page view(s)

15
Updated on Jun 13, 2021

Download(s)

1
Updated on Jun 13, 2021

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.