Decision Support Systems in Stock Investment Problems

Abstract

This study compiles decision support systems that aim to optimize financial decision processes by examining the literature studies targeting stock investments. The review encompasses a range of methodologies and applications, from traditional approaches such as Markowitz’s Modern Portfolio Theory, Black-Litterman, and Single Index models to artificial intelligence-based techniques. In detail, the contributions of Decision Support Systems to stock portfolio construction and portfolio optimization processes along with comparative analyses between these systems are scrutinized. The review also aims to enable researchers and practitioners to be engaged in portfolio optimization with a framework for future investigations in areas such as historical data analysis, future price movement prediction, assessment of risk factors, and determination of optimal portfolio distribution. Furthermore, it seeks to enhance the understanding of decision support systems employed in portfolio optimization, facilitating a more comprehensive grasp of their utility within stock investments.