Artificial intelligence techniques in investment trading: a systematic review - Amirul Ammar Anuar; Mohammad Taqiuddin Mohamad; Ahmad Azam Sulaiman @ Mohamad (Penerbit Universiti Kebangsaan Malaysia, 2025)
The application of Artificial Intelligence (AI) in investment trading has grown rapidly, yet the literature on the subject remains scattered and lacks cohesive structure. This study conducts a systematic review to identify the AI techniques employed in trading activities and to examine how these techniques function within the investment process. The findings are organized into three analytical categories: traditional machine learning, neural networks and deep learning, and optimization-based methods. This classification encompasses a range of techniques such as decision trees, support vector machines, recurrent neural networks, and particle swarm optimization, among others. Drawing from the synthesis of existing literature, the study further provides a deductive analysis indicating that these AI techniques are primarily applied by institutional investors and remain largely inaccessible to smaller retail investors. By consolidating fragmented insights, the study offers an original and structured framework for understanding AI’s role in trading, contributing to both academic discourse and financial innovation.