Prediction of Future Stock Price Using Recurrent Neural Network

Abstract

The stock market can affect businesses in various ways, as the rise and fall of a company's share price values impact its market capitalization and overall market value. However, forecasting stock market returns is challenging because financial stock markets are unpredictable and non-linear, with factors such as market trends, supply and demand ratios, global economies, and public opinion affecting stock prices. With the advent of artificial intelligence and increased processing power, intelligent prediction techniques have become more effective in forecasting stock values. This study proposes a Recurrent Neural Network (RNN) model that uses a deep learning machine to predict stock prices. The process includes five stages: data analysis, dataset preparation, network design, network training, and network testing. The accuracy of the model 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 then compared to the actual closing price to assess the accuracy of the model. Finally, it is suggested that this approach can also be used to forecast other volatile time-series data.

Keywords

• stock price
• prediction
• recurrent neural network
• rapid miner