Stock Market Close Price Prediction Using Neural Network and Regression Analysis - Purey, Prateek; Patidar, Anil (International Journal of Computer Sciences and Engineering, 2018)
The financial market is very dynamic in nature and changing continuously. In addition of that due to it’s dynamicity prediction of stock price values are not much accurate. In order to predict the stock’s close values accurately machine learning technique is used in this proposed work. The proposed technique usages supervised learning technique because supervised learning techniques can predict values more accurately. In order to train and test the proposed machine learning prediction technique the YQL data in offline mode is used. The proposed stock market price prediction method is a hybrid data model. In this context two different algorithms are combined for obtaining the goodness of both the techniques. Here both the algorithms are analyze data according to their methodology and perform prediction. After that both algorithms’ approximated values are combined by computing the mean values as final prediction. Therefore the proposed technique optimizes the performance of the traditional back propagation based stock market price prediction. The implementation of the proposed technique is performed using the JAVA technology and the performance of the system is measured in term of accuracy, error rate, time complexity and memory usages. The performance of the system demonstrate the proposed technique enhance the prediction of the close values. In addition of that comparative performance study of proposed technique is performed with the traditional back propagation model using experimental outcomes. Results demonstrate proposed technique out perform with respect to the traditional approach of stock market price prediction.