| Abstrakt: |
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. |
