Description

In our world a number of things and events are unpredictable, among them stock market price is an entity that is highly impulsive. The stock market prices are fluctuating in an irregular manner. A number of researchers are doing hard work to predict the stock market prices. Expertise to forecast direction and exact value of upcoming stock market values is the essential issue in financial market to make money and profits. So forecasting the exact price and performance of stock market has become the area of interest. Though, because of high volatile nature of basic laws behind the financial time series, it is not any straightforward task to assemble such a forecasting system. To predict the upcoming stock market trends the data mining and machine intelligence based techniques are engaged to predict the prices precisely. The proposed work precisely approximate the stock prices by using back propagation neural network based practice. The comparative performance study is performed using the accuracy, error rate, memory and time consumption. According to the achieved results the performance of the anticipated technique is found improved and adoptable.