Stock prediction

Leung, Yiu Fai

Please use this identifier to cite or link to this item: http://dspace.cityu.edu.hk/handle/2031/3663

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DC Field	Value	Language
dc.contributor.author	Leung, Yiu Fai
dc.date.accessioned	2006-10-19T06:48:47Z
dc.date.accessioned	2017-09-19T08:51:16Z
dc.date.accessioned	2019-02-12T06:53:28Z	-
dc.date.available	2006-10-19T06:48:47Z
dc.date.available	2017-09-19T08:51:16Z
dc.date.available	2019-02-12T06:53:28Z	-
dc.date.issued	2006
dc.identifier.other	2006cslyf705
dc.identifier.uri	http://144.214.8.231/handle/2031/3663	-
dc.description.abstract	Stock prediction is always an absorbing topic. Enormous efforts have been contributed by scholars but until now, there is still hardly a conclusion. This research aims to predict stock trend by using traditional technical indicators and soft computing techniques. Existing prediction techniques are studied and evaluated. Five popular technical indicators are used as input of two classifier, naïve bayes and k nearest neighbors for decision making. A trading strategy system is implemented to simulate trading environment. Historic data of the Heng Seng Index, Dow Jones Index, HSBC (005), MeiKin (335) and Wing Lung (0096) are used to evaluate prediction methods.	en
dc.format.extent	164 bytes
dc.format.mimetype	text/html
dc.rights	This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.
dc.rights	Access is restricted to CityU users.
dc.title	Stock prediction	en
dc.contributor.department	Department of Computer Science	en
dc.description.supervisor	Dr. Rynson Lau	en
Appears in Collections:	Computer Science - Undergraduate Final Year Projects

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