Abstract
The task of investing in financial markets to make profits and grow one's wealth is not a straightforward task. Typically, financial domain experts, such as investment advisers and financial analysts, conduct extensive research on a target financial market to decide which stock symbols are worthy of investment. The research process used by those experts generally involves collecting a large volume of data (e.g., financial reports, announcements, news, etc.), performing several analytics tasks, and making inferences to reach investment decisions. The rapid increase in the volume of data generated for stock market companies makes performing thorough analytics tasks impractical given the limited time available. Fortunately, recent advancements in computational intelligence methods have been adopted in various sectors, providing opportunities to exploit such methods to address investment tasks efficiently and effectively. This paper aims to explore rank-based approaches, mainly machine-learning based, to address the task of selecting stock symbols to construct long-term investment portfolios. Relying on these approaches, we propose a feature set that contains various statistics indicating the performance of stock market companies that can be used to train several ranking models. For evaluation purposes, we selected four years of Saudi Stock Exchange data and applied our proposed framework to them in a simulated investment setting. Our results show that rank-based approaches have the potential to be adopted to construct investment portfolios, generating substantial returns and outperforming the gains produced by the Saudi Stock Market index for the tested period.