Risk Adapting Stock Trading System Associated Technical with Macroeconomic Indicators

Machine learning has emerged as a powerful tool in studying the behavior of stock movement. However, it has yet to be highly accurate due to market randomness. This article aims to improve stock movement classification accuracy by addressing macroeconomic factors, which have been neglected in previous machine learning stock prediction studies. Hence, we propose a Risk Adapting Stock Trading System (RAST) using both technical and macroeconomic indicators. The simulated trading result of the system presented here proves that a combination of these two types of indicators is more effective than only using technical indicators when associated with machine learning.