Stock price prediction through an artificial intelligence model using basic, technical, and macroeconomic indicators

Abstract

This study aims to analyze and predict future stock values more accurately. We proposed a stock price prediction model based on an artificial intelligence model using basic stock price data and technological and macroeconomic indicators. As a result of the experiment, the model using the features of adding technical indicators to the actual stock index (basic indicators) has better performance than the model using basic indicators and forecast performance by adding basic, technical, and macroeconomic indicators. Comparing artificial intelligence algorithms, the LightGBM model performed better than the deep neural network model and random forest model.