Stock market price prediction with Cascading and Ensemble classifier methods
Main Article Content
Abstract
The stock market is the backbone of the financial status of any country. The
unpredictable behaviors of the stock market change the mental group of investors and
buyers. If the stocks traders predict right trend in stock price, they can realize profits.
Therefore, prediction of stock price is very important factor for buyers and seller in
stock market. The accuracy and behavior is accurate in limited data of stocks, if the
range of data are increases the impact of prediction is decrease. The recent research in
the area of stock price prediction with machine learning methodologies makes use
mainly of SVM architectures and takes into account several predictors. The aim of this
proposed work is to increase the capacity of sample size of classifier and increase the
accuracy of prediction with the derivation of cascading and ensemble classifier.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
