Stock market price prediction with Cascading and Ensemble classifier methods
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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.
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