Implementation of Artificial Neural Network to Predict S&P 500 Stock Closing Price

Artificial Neural Network (ANN) is a learning method that can be used for prediction and classification. In this study, ANN back-propagation is implemented to predict the closing price of the S&P 500 stock exchange using historical data. Historical data consisting of five variables, namely open, high, low, close, and volume. The historical data are taken from finance.yahoo.com which stores historical data of daily stock prices up to 65 years earlier. The data is designed based on daily prediction scenarios of the closing stock price of the S&P 500 stock exchange. Using the scenario an application prototype using R and Java software has been successfully built. The prototype is dynamic during select the data set (10, 50, 100, 500 past data) to get the best stock closing prediction for the next day. The selection of the best stock closing prediction uses the MAPE (Mean Average Percentage Error) criterion which is in contrast to the prediction accuracy level. The smaller the MAPE value the better the predicted result. Based on the implementation results, the average MAPE for daily forecast for 1 month in April 2017 was 0.2307 indicating that the average daily prediction accuracy rate for 1 month was 99.77%. Our prototype application can make prediction automatically every day, every time when connected to the internet.