Machine Learning-Based Decision-Making for Stock Trading: Case Study for Automated Trading in Saudi Stock Exchange

Stock markets are becoming the center of attention for many investors and hedge funds, providing them with a wide range tools investment opportunities to grow their wealth participate in economy. However, investing stock market is not trivial. traders financial advisors required frequently monitor actions, search profitable companies, analyze price movements generate various trading ideas (e.g., selecting symbol making decision when enter or exit trade), potentially leading returns. Therefore, this study aims address challenge through exploring adaptation machine learning methods combined risk management techniques develop framework automating task trading. We evaluated our by creating diverse portfolio containing several companies listed on Saudi Exchange (Tadawul) using simulated actions (executed framework) estimate portfolio’s returns 3.7 years. The findings show that terms returns, proposed very promising; it has generated over 86% outperformed almost all funds top banks Arabia.