Title :
Stock market prediction
Author_Institution :
Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, Romania
Abstract :
In a financially volatile market, as the stock market, it is important to have a very precise prediction of a future trend. Because of the financial crisis and scoring profits, it is mandatory to have a secure prediction of the values of the stocks. Predicting a non-linear signal requires advanced algorithms of machine learning. The literature contains studies with different machine learning algorithms such as ANN (artificial neural networks) with different feature selection. The results of this study will show that the algorithm of classification SVM (Support Vector Machines) with the help of feature selection PCA (Principal component analysis) will have the success of making a profit.
Keywords :
"Support vector machines","Stock markets","Principal component analysis","Algorithm design and analysis","Covariance matrices","Prediction algorithms","Market research"
Conference_Titel :
System Theory, Control and Computing (ICSTCC), 2015 19th International Conference on
DOI :
10.1109/ICSTCC.2015.7321293