Share Market Prediction using Deep Neural Network
Gayatri Purushottam Panchwagh1, Deepak Gupta2

1Gayatri Purushottam Panchwagh, Department of Computer Engineering, Siddhant College of Engineering Sadumbre, Pune, India.
2Prof. Dr. Deepak Gupta, Department of Computer Engineering, Siddhant College of Engineering Sadumbre, Pune, India. 

Manuscript received on 15 August 2019. | Revised Manuscript received on 20 August 2019. | Manuscript published on 30 September 2019. | PP: 8619-8622 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6447098319/2019©BEIESP | DOI: 10.35940/ijrte.C6447.098319

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Abstract: 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.
Keywords: Deep Neural Network (DNN), Multi-layered Perceptron (MLP), Multilayer Perceptron Model. Deep Learning (DL), Reinforcement Learning (RL)

Scope of the Article: Deep Learning