Selection of Trading Indicators Using Machine Learning and Stock Close Price Prediction with the Long Short Term Memory Method

Abstract

Abstract. Humans have a limit to their physical ability to work, so investment is needed to meet their needs and other goals according to their wants and needs. Investment has many types and risks according to the portion of the return value, such as mutual funds, bonds and stocks. Stocks are a form of investment that has a high risk because of the rapid fluctuations in stock values. Prediction of stock movements is usually assisted by indicators, but predictions using indicators require complex analysis because of the diverse periods and different movements in each stock data case. Purpose: To predict the closing price of BBCA and BBRI shares in the next 10 days by considering the count of technical indicators in the form of Moving average (MA), Exponential moving average (EMA), Rate Of Change (ROC), Price Momentum, Relative Strength Index (RSI), Stochastic Oscillator in periods 21, 63 and 252. Methods/Study design/approach: This research was conducted by comparing the accuracy of Random Forest, Decision Tree, KNN, SVM using K-fold Cross Validation then the method with the best accuracy was used to find out how much velue from the trading indicators used and predict the closing price of shares per day at BBRI and BBCA companies for the next 10 day period using the LSTM algorithm. Result/Findings: The best accuracy in the k-fold cross validation process is random forest. random forest is used to train indicator data in determining 5 indicators along with the period that has the highest value, in this test it produces values on BBCA data in order, namely ROC63, RSI63, MOM63, MA252, EMA21 while on BBRI data in order, namely ROC63, MOM63, RSI63, MA252, MA21. This indicator is used in the price forecasting process with the LSTM method to determine the closing price in the next 10 days. The LSTM method in this study resulted in 96.8% accuracy for BBCA and 96.4% accuracy for BBRI. Novelty/Originality/Value: The forecasting accuracy on BBCA is 96.8% and the forecasting accuracy on BBRI is 96.4%. This shows that the accuracy results are classified as good because the prediction results are close to the actual results. The data training process is expected to help traders in making stock buying and selling decisions that are adjusted to the fundamental aspects of the company.

Keywords

• Long Short Term Memory
• Stock
• Prediction
• Historical Data
• trading
• machine learning