• CH. Sravanthi
• Krishna Rupendra Singh

This project is a study on the various applications of neural networks aimed at the complexity of interior and variety of exterior composition of stock pricing system for solving business problems like stock market predictions. It can be concluded from analysis that ANNs are most implemented with Back propagation algorithm as the methodology in forecasting of stock prices, returns and stock modeling. The aim of this project is implementation of neural networks with back propagation algorithm for stock market. It is most often used as training algorithm in current neural network applications, we apply data mining technology to stock market in order to research the trend of price; it aims to predict the future trend of the stock market and the fluctuation of price. This project points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier structure of the neural network, namely input layer, hidden layer and output layer. Finally, we get a better predictive model to improve forecast accuracy.

Stock Market Prediction, AI,BP Neural Network, Mutual Funds

"A framework for predictive analysis of Stock market forecasting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.564-569, November-2018, Available :http://www.jetir.org/papers/JETIR1811073.pdf

"A framework for predictive analysis of Stock market forecasting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp564-569, November-2018, Available at : http://www.jetir.org/papers/JETIR1811073.pdf