@article{(Open Science Index):https://publications.waset.org/pdf/8952,
	  title     = {A Hybrid Machine Learning System for Stock Market Forecasting},
	  author    = {Rohit Choudhry and  Kumkum Garg},
	  country	= {},
	  institution	= {},
	  abstract     = {In this paper, we propose a hybrid machine learning
system based on Genetic Algorithm (GA) and Support Vector
Machines (SVM) for stock market prediction. A variety of indicators
from the technical analysis field of study are used as input features.
We also make use of the correlation between stock prices of different
companies to forecast the price of a stock, making use of technical
indicators of highly correlated stocks, not only the stock to be
predicted. The genetic algorithm is used to select the set of most
informative input features from among all the technical indicators.
The results show that the hybrid GA-SVM system outperforms the
stand alone SVM system.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {2},
	  number    = {3},
	  year      = {2008},
	  pages     = {689 - 692},
	  ee        = {https://publications.waset.org/pdf/8952},
	  url   	= {https://publications.waset.org/vol/15},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 15, 2008},
	}