Parameters for Stock Market Prediction

In recent years researchers have developed a lot of interest in stock market prediction because of its dynamic & unpredictable nature. Although there were lots of methods of prediction none of them is prove to produce satisfactory results. Machine learning techniques proved to be better than other methods because of its ability of nonlinear mapping. In this paper we survey different input parameters that can be used for stock market prediction with ANN. In this paper we will try to find out most important input parameters that have major impact on accuracy. From the survey we see that most of machine learning techniques make use of Technical variables over fundamental variables for a particular stock price prediction, while Microeconomic variables are mostly used to predict stock market index. But hybridized parameters give better result that applying only single type of input variables.