Deep Learning based forecast using RNN for stock price prediction

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R.Muruganandham, M.S.Karthikeyan, R.Jagajeevan, R.Chitra, Joshua Selvakumar.J, S.Akshaya


Predicting the profit of a firm, predicting the expenditures and cost of an organization and predicting the demand of any product and service have always been playing a significant role in current global business scenario. For forecasting conventional techniques are being adopted. At the same time, accuracy is very important for forecasting. Thanks to the progress of technology, scientific techniques are replacing the conventional forecasting methods. Due to the phase of Industry 4.0, automation, artificial intelligence and machine learning have become areas of thrust and focus in the world. In this article, deep learning based recurring neural network technique that employs and recognizes sequential data is applied for prediction the stock price. Thanks to technological revolution, stock market analysis has become very easy and that too predicting stock price has become an important agenda for investors. Most of the stock and securities platforms have started using technological and scientific means and methods of price prediction and price analysis. Off late, machine learning algorithms have been evolved and customized for stock market analysis. Thus, this article has also applied deep learning technique for stock price prediction. The paper presents a well-organized approach that aids investors and organizations to gain profit.

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