Predictive mining for stock market based on live news TF-IDF features
by Vaishali Ingle; Sachin Deshmukh
International Journal of Autonomic Computing (IJAC), Vol. 2, No. 4, 2017

Abstract: The various machine learning algorithms are used for prediction of stock market movement. The data collected for stock market is in the form of breaking news from various finance websites. The TF-IDF features extracted from online news data are used for creation of HMM model along with log likelihood values. The next day's stock price is predicted as either higher or lower than current day's stock price. Results obtained from proposed model is compared with results from other machine learning predictive techniques such as random forest, KNN, multiple regression, bagging and boosting. The proposed model produces approximately 70% of accurate prediction. The captured features are graphically represented with word cloud. The results can be further improved with the use of deep learning ensemble methods.

Online publication date: Wed, 07-Feb-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Autonomic Computing (IJAC):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com