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Share Market Prediction using Regression Analysis -A Parallel Programming Mode

Publication Date : 02/05/2016

Author(s) :

Shital deshmukh , Priyanka sonawane .

Conference Name :
International Conference on Recent Trends in Engineering and Technology

Abstract :

Predicting daily behavior of stock market is a serious challenge for investors and corporate stockholders and it can help them to invest with more confident by taking risks and fluctuations into consideration. In this paper, by applying linear regression for predicting behavior share value, Stock market prediction is of great interest to stock traders and applied researchers. Main issues in developing a fully automated stock market prediction system are feature extraction from the stock market data, feature selection for highest prediction accuracy, the dimensionality reduction of the selected feature set and the accuracy and robustness of the prediction system. In this project, regression analysis stock market trend prediction system is proposed. The proposed system uses technical analysis traditionally used by stock traders for feature extraction. Selected features are then subjected to dimensionality regression analysis for the next-day stock market trend prediction. we prove that our proposed method has a similar and good performance in comparison to real volumes and the stockholders can invest confidentially based on that.

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