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dc.contributor.authorGhorbani, Mahsa
dc.contributor.authorChong, Edwin
dc.date.accessioned2017-11-13T22:50:16Z
dc.date.available2017-11-13T22:50:16Z
dc.date.issued2017
dc.description.abstractStock price prediction is one of the most challenging problems in finance and is receiving considerable attention from researchers. The literature provides strong evidence that prices can be predicted from past price data as well as other fundamental and macroeconomic variables. We propose a filtering operation using covariance information in order to predict future stock prices. We use daily historical price data for Generals Electric Company to illustrate our method, which shows promising results in terms of the estimation performance and volatility.
dc.format.mediumborn digital
dc.format.mediumStudent works
dc.format.mediumposters
dc.identifier.urihttps://hdl.handle.net/10217/184932
dc.languageEnglish
dc.publisherColorado State University. Libraries
dc.relation.ispartof2017 Projects - Graduate Student Showcase
dc.rightsCopyright of the original work is retained by the author.
dc.titleNew stock price prediction method using covariance information, A
dc.title.alternative114 - Mahsa Ghorbani
dc.typeText
dc.typeImage
dcterms.rights.dplaThe copyright and related rights status of this Item has not been evaluated (https://rightsstatements.org/vocab/CNE/1.0/). Please refer to the organization that has made the Item available for more information.


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