Prediction of Stock Market Using Recurrent Neural Network

Abstract

In the financial realm, stock price forecasting is becoming increasingly popular. Shares price prediction is important for increasing the interest of speculators in putting money in a company's stock in order to grow the number of shareholders in the stock. Successfully predicting the price of a stock in the future could yield significant profit. When it involves forecasting, various methodologies are used. This paper uses a recently introduced model for predicting stock price. This proposed model is a well-liked model named is the Recurrent Neural Network (RNN) model. One of the variant of RNN is Long Short Term Memory (LSTM) model. It are often shown from the simulation results that utilizing these RNN models such as LSTM, and constructing with proper hyper-parameter tuning, these expected models can estimate the future stock market with the maximum percentage of accuracy. The RMSE for a LSTM model was calculated by changing the amount of epochs, the variation between predicted stock price and actual stock price. The model is trained and classified for accuracy with different sizes of knowledge. The computations are conducted by exploiting a widely accessible datasets for stock markets containing date, volume, opening price, highest price, lowest price, and closing prices. The major goal of this article is to determine to what degree a Machine Learning algorithm can anticipate the stock market price with greater accuracy.

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