Deep Learning-Based Stock Price Prediction Using LSTM and Bi-Directional LSTM Model

Abstract

In the financial world, the forecasting of stock price gains significant attraction. For the growth of shareholders in a company's stock, stock price prediction has a great consideration to increase the interest of speculators for investing money to the company. The successful prediction of a stock's future cost could return noteworthy benefit. Different types of approaches are taken in forecasting stock trend in the previous years. In this research, a new stock price prediction framework is proposed utilizing two popular models; Recurrent Neural Network (RNN) model i.e. Long Short Term Memory (LSTM) model, and Bi-Directional Long Short Term Memory (BI-LSTM) model. From the simulation results, it can be noted that using these RNN models i.e. LSTM, and BI-LSTM with proper hyper-parameter tuning, our proposed scheme can forecast future stock trend with high accuracy. The RMSE for both LSTM and BI-LSTM model was measured by varying the number of epochs, hidden layers, dense layers, and different units used in hidden layers to find a better model that can be used to forecast future stock prices precisely. The assessments are conducted by utilizing a freely accessible dataset for stock markets having open, high, low, and closing prices.