Prediction of future stock price using recurrent neural network

The stock market can affect businesses in a variety of ways. The rise and fall of a company’s share price values affects its market capitalization and thus its market value. Forecasting stock market returns is difficult because financial stock markets are unpredictable and non-linear. The market trend, supply and demand ratio, global economy, public opinion, and a variety of other factors may all influence the price of a particular stock. With the advent of artificial intelligence and increased processing power, programmable prediction techniques have proven to be more effective in predicting stock values. This study proposed a Recurrent Neural Network (RNN) model that uses a deep learning machine to forecast Malaysian Pacific Industries' stock price in the future (MPI). The five stages were data analysis, dataset preparation, network design, network training, and network testing. The accuracy of the model examined is determined by the mean square error (MSE) and root mean square error (RMSE), which are 1.24 and 1.12, respectively. The predicted closing price is compared to the actual closing price. Finally, it is proposed that this approach be used to forecast other volatile time-series data.