Google Trends and Technical Indicator based Machine Learning for Stock Market Prediction

Mamluatul Hani'ah (Politeknik Negeri Malang, Malang, Indonesia)
Moch Zawaruddin Abdullah (Politeknik Negeri Malang, Malang, Indonesia)
Wilda Imama Sabilla (Politeknik Negeri Malang, Malang, Indonesia)
Syafaat Akbar (Politeknik Negeri Malang, Malang, Indonesia)
Dikky Rahmad Shafara (Politeknik Negeri Malang, Malang, Indonesia)

MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Vol 22 No 2 (2023)

The stock market often attracts investors to invest, but it is not uncommon for investors to experience losses when buying and selling shares. This causes investors to hesitate to determine when to sell or buy shares in the stock market. The accurate stock price prediction will help investors to decide when to buy or sell their shares. In this study, we propose a new approach to predicting stocks using machine learning with a combination of features from stock price features, technical indicators, and Google trends data. Three well-known machine learning algorithms such as Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Multiple Linear regression are used to predict future stock prices. The test results show that the SVR outperformed the MLP and Multiple Linear Regression to predict stock prices for Indonesian stocks with an average MAPE is 0.50%. The SVR can predict the stock price close to the actual price.