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dc.contributor.authorYong, Bangen
dc.contributor.authorABDULLAH, AHMAD SHAHIDANen
dc.contributor.authorABDUL RAHIM, MOHD ROZAINIen
dc.date.accessioned2019-11-22T00:31:02Z
dc.date.available2019-11-22T00:31:02Z
dc.identifier.issn1865-0929
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/299144
dc.description.abstractThe stock market prediction is a lucrativefield of interest withpromising profit and covered with landmines for the unprecedented. The mar-kets are complex, non-linear and chaotic in nature which poses huge difficultiesto predict the prices accurately. In this paper, a stock trading system utilizingfeed-forward deep neural network (DNN) to forecast index price of Singaporestock market using the FTSE Straits Time Index (STI) in t days ahead is pro-posed and tested through market simulations on historical daily prices. There are40 input nodes of DNN which are the past 10 days’opening, closing, minimumand maximum prices and consist of 3 hidden layers with 10 neurons per layer.The training algorithm used is stochastic gradient descent with back-propagationand is accelerated with multi-core processing. A trading system is proposedwhich utilizes the DNN forecasting results with defined entry and exit rules toenter a trade. DNN performance is evaluated using RMSE and MAPE. Theoverall trading system shows promising results with a profit factor of 18.67,70.83% profitable trades and Sharpe ratio of 5.34 based on market simulation ontest data.
dc.rightsAll rights reserved
dc.titleA Stock Market Trading System Using Deep Neural Networken
dc.typeConference Object
prism.endingPage364
prism.startingPage356
prism.volume751en
dc.identifier.doi10.17863/CAM.46208
dcterms.dateAccepted2017-06-07en
rioxxterms.versionofrecord10.1007/978-981-10-6463-0_31en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2017-06-07en
dc.contributor.orcidYong, Bang [0000-0002-6214-2253]
dc.identifier.eissn1865-0937
rioxxterms.typeConference Paper/Proceeding/Abstracten
cam.issuedOnline2017-08-26en
dc.identifier.urlhttps://link.springer.com/chapter/10.1007/978-981-10-6463-0_31#citeasen
pubs.conference-name17TH ASIA SIMULATION CONFERENCEen
pubs.conference-start-date2019-08-27en
cam.orpheus.successThu Nov 05 11:55:07 GMT 2020 - Embargo updated*
rioxxterms.freetoread.startdate2018-08-26


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