Stock Market Price Analysis Prediction Result Using a Hybrid Model Based on Long Short-Term Memory Algorithms
Abstract
The purpose of this research is to develop a hybrid Deep Learning model capable of forecasting new stock price values based on stock market data. Textual analysis of public opinion from online news sources and blogs is being examined in addition to numerical analysis of stock movement. Using hybrid architecture for these algorithms, we take RMS value and median (along with 95% Confidence Interval) of the predictions from the algorithms.