Design and Implementation of MCNN for Better Prediction of Stock Price Movement

Abstract

The stock market prediction is problematic subsequently the stock price is active in environment. To decrease the inappropriate predictions of the stock market and evolution the ability to predict the market actions. To escape the risk and the challenging in predicting stock price. Predicting stock market prices is a difficult task that conventionally contains extensive neural network. Owed to the linked environment of stock prices, conventional batch processing technique cannot be developed competently for stock market analysis. We propose an efficient Learning algorithm that develops a kind of Modified Computational Neural Networks (MCNN) based on BPNN (Back Propagation neural network) filter in training to increase the stock price prediction. Where the weights are adjusted for separate data points using stochastic gradient descent. This will distribute extra precise outcomes when linked to existing stock price prediction algorithms. The network is trained and evaluated for accurateness complete numerous sizes of data, and the results are organized.