Real-time predictive big data analytics system: forecasting stock trend using technical indicators
by Myat Cho Mon Oo
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 21, No. 1, 2022

Abstract: The emergence of financial big data stocks has caused dramatic changes, and predictive analytics systems require a scalable architecture to intelligently process these data. In this paper, a real-time predictive big data analytics (RPBA) system is proposed using the technical indicators to predict stock market trend. Scalable random forest (SRF) is enhanced as a financial instrument by contributing the hyperparameters optimisation. This paper explores the novel alternative by the combination of features engineering and enhanced SRF to maximise the desired measure of stock prediction models based on the data from four stocks periods: inactive, sub-active, active, and strong-active periods. The empirical findings indicate that the proposed RPBA system can provide high predictability 85% for short-term and 99% for long-term predictions over real-time financial eight stock markets.

Online publication date: Mon, 04-Jul-2022

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 Business Intelligence and Data Mining (IJBIDM):
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