Stock Market Price Forecasting Using Recurrent Neural Network - Pragya Bhardwaj; Jayant Kwatra (School of Computing, Telkom University, 2022)
A stock refers to the ownership of the organisation and its investors. A market where these stocks are sold or purchased is known as stock market. The prices of the stock is listed over National Stock Exchange or Bombay Stock Exchange for all Indian Companies. In this work, a machine learning approach is used to predict and forecast the prices of a company listed in NSE and BSE for 30 days using recurrent neural network known as stacked long-short term memory model. The results show that the model worked highly effective in performing the task. The model in the evaluation phase gave a root mean square error of 3.00 on the training data, 0.03 on testing data. R2 score for training data was 0.99 and 0.97 for the testing data. The prices when compared by the client organisation showed that they matched the predicted values to upto 90%. Thus, stacked LSTM models are one of the best models to make predictions of stock related data.