Open AccessShare Market Prediction using Deep Neural Network
Author(s) -
Gayatri Purushottam Panchwagh*,
Prof. Dr. Deepak Gupta*
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c6447.098319
Subject(s) - stock market , reinforcement learning , stock market prediction , recurrent neural network , artificial intelligence , computer science , artificial neural network , deep learning , stock (firearms) , multilayer perceptron , machine learning , engineering , mechanical engineering , paleontology , horse , biology
People, due to their complexity and volatile actions, are constantly faced with challenges in understanding the situation in the market share and the forecast for the future. For any financial investment, the stock market is a very important aspect. It is necessary to study while understanding the price fluctuations of the stock market. In this paper, the stock market prediction model using the Recurrent Digital natural Network (RDNN) is described. The model is designed using two important machine learning concepts: the recurrent neural network (RNN), multilayer perceptron (MLP) and reinforcement learning (RL). Deep learning is used to automatically extract important functions of the stock market; reinforcement learning of these functions will be useful for future prediction of the stock market, the system uses historical stock market data to understand the dynamic market behavior when you make decisions in an unknown environment. In this paper, the understanding of the dynamic stock market and the deep learning technology for predicting the price of the future stock market are described.