Stock price prediction using news factor correlation model
Refereed conference paper presented and published in conference proceedings

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AbstractA generalized "news" factor correlation model is devised from n-factor model, "news" model and continuous varied correlation coefficients. It is developed to improve the accuracy of stock price prediction by considering correlation coefficient for n-factor and "news" models. This model was tested using the GEM stocks and NASDAQ index with linear regression model and Adaline neural network model. 0-factor model, "news" factor model and "news" factor correlation model were examined with in-sample training and out-of-sample testing. Experimental results showed that "news" factor correlation model outperformed the other models in in-sample training.
All Author(s) ListNg HS, Lam KP
Name of Conference7th Joint Conference on Information Sciences (JCIS)
Start Date of Conference26/09/2003
End Date of Conference30/09/2003
Place of ConferenceRES TRIANGLE PK
Country/Region of ConferenceUnited States of America
Pages1071 - 1074
LanguagesEnglish-United Kingdom
Keywordsn-factor model; neural networks; news model
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods

Last updated on 2020-04-08 at 05:17